{ "battle": "qwen3-7-max-vs-claude-opus-4-6-value", "category": "Best-value API", "suite_version": "suite-2026-07", "contestants": { "A": { "name": "Qwen3.7 Max", "model": "qwen/qwen3.7-max" }, "B": { "name": "Claude Opus 4.6", "model": "anthropic/claude-opus-4.6" } }, "judge": "google/gemini-3.1-pro-preview", "judge_panel": [ "google/gemini-3.1-pro-preview", "openai/gpt-5.6-terra", "x-ai/grok-4.5" ], "protocol": "Three judges from different labs. Each scores both position orders; a judge's verdict counts only if it survives its own swap. The task goes to the majority of the panel; no majority is a tie.", "proxy_note": null, "started_at": "2026-08-12T08:37:47.845Z", "finished_at": "2026-08-12T08:46:14.012Z", "scoreline": { "Qwen3.7 Max": 9, "Claude Opus 4.6": 5, "ties": 16 }, "total_cost_usd": 1.7215, "tasks": [ { "suite": "writing", "id": "w1", "name": "Cold email", "prompt": "Write a cold email (max 120 words) from the owner of a 6-person UK accounting firm to a local brewery, offering a free first-month bookkeeping trial. Friendly but professional, one clear call to action, no buzzwords.", "output_a": "Subject: Free first month of bookkeeping for [Brewery Name]\n\nHi [Name],\n\nI’m a regular at your taproom and love your latest pale ale. I also run a local, six-person UK accounting firm. \n\nRunning a brewery means long hours on the floor, leaving little time for the books. We help local hospitality businesses keep their finances accurate and stress-free. \n\nTo show you how we work, I’d like to offer [Brewery Name] a free first month of bookkeeping. No strings attached. \n\nIf you’re open to taking this admin off your plate, are you free for a brief, 10-minute phone call next Tuesday?\n\nBest regards,\n\n[Your Name] \nOwner, [Firm Name]", "output_b": "**Subject: A month of bookkeeping on us**\n\nHi,\n\nI'm Sam, the owner of Clearpath Accounting here in [town]. We're a small team of six, and we work with several food and drink businesses locally.\n\nI know breweries deal with tricky VAT rules, shifting ingredient costs, and seasonal cash flow. That's exactly the kind of work we enjoy getting right.\n\nI'd like to offer you a free first month of bookkeeping — no contract, no obligation. If it saves you time and makes your numbers clearer, we can talk about working together. If not, you keep everything we've done.\n\nWould you be free for a 15-minute call this week or next?\n\nBest,\nSam\nClearpath Accounting\n[phone] | [email]", "latency_ms": { "a": 39440, "b": 6191 }, "tokens_out": { "a": 2030, "b": 176 }, "cost_usd": { "a": 0.009072725, "b": 0.004705 }, "winner": "tie", "judge_scores": { "a": 8.7, "b": 8.8 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses follow all instructions perfectly, staying under the 120-word limit and avoiding buzzwords. They both offer a clear call to action and maintain a friendly, professional tone. Response 1 has a nice personal touch, while Response 2 shows good industry knowledge. / Both responses follow all instructions and constraints perfectly. Response 1 is slightly better as it mentions specific accounting pain points for breweries (VAT, ingredient costs), making it more compelling and professional. | openai/gpt-5.6-terra: A — Both are under 120 words, professional, and contain one clear call to action. Response 1 explicitly establishes the firm as UK-based and is well tailored to a brewery. Response 2 is strong and practical but does not explicitly identify the firm as UK-based, a stated task detail. / Both are under 120 words, professional, and include one clear call to action. Response 1 is well tailored to brewery bookkeeping but does not explicitly establish the firm as UK-based. Response 2 explicitly meets that requirement and is concise, though its taproom/pale ale claim should only be used if true. | x-ai/grok-4.5: B — Both under 120 words, meet tone/CTA/no-buzzword rules and offer correctly. R2 slightly clearer, more concise and professional without mild filler like 'stress-free'. / Both under 120 words, meet all specs with clear CTA and no buzzwords. R1 more professional and UK-relevant without false personal claims; R2 slightly less authentic for cold outreach.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 9.5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 8 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 8, "score_b": 9 } ] }, { "suite": "writing", "id": "w2", "name": "Product description", "prompt": "Write a 60-80 word product description for a stainless-steel insulated water bottle (750ml, keeps drinks cold 24h/hot 12h, leakproof, fits car cup holders). Target audience: gym-goers. Avoid cliches like 'stay hydrated in style'.", "output_a": "Crush your personal records with ice-cold water. This 750ml insulated stainless-steel water bottle is built for intense training, keeping drinks freezing cold for 24 hours or hot for 12. Toss it in your gym bag without worry. The completely leakproof lid protects your clean gear from annoying spills. The tapered base slides perfectly into your car cup holder for an easy commute to the weight room. Fuel your heaviest lifts properly every single day.", "output_b": "# HydroCore 750ml Insulated Water Bottle\n\nEngineered for athletes who demand more from their gear. This double-wall stainless-steel bottle locks in ice-cold temperatures for 24 hours or keeps your pre-workout hot for 12. The leakproof seal means it tosses into any gym bag without worry, while the slim profile slides right into car cup holders between sessions. Durable, sweat-free, and built to survive every rep, set, and sprint you throw at it.", "latency_ms": { "a": 75476, "b": 4804 }, "tokens_out": { "a": 4409, "b": 117 }, "cost_usd": { "a": 0.019618975, "b": 0.003305 }, "winner": "tie", "judge_scores": { "a": 9, "b": 8.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses perfectly follow the instructions, including the word count constraint (74 and 72 words respectively). They both target gym-goers effectively, include all required product features, and avoid the banned cliché. Both are highly useful and well-written. / Both responses perfectly follow all instructions, including the 60-80 word count constraint. They accurately include all required product features, effectively target gym-goers, and avoid the banned cliché. Both are clear, concise, and highly useful. | openai/gpt-5.6-terra: tie (flipped on swap) — Both meet the 60–80-word requirement, cover all stated features, target gym-goers, and avoid the banned cliché. Response 1 is more directly useful and precise. Response 2 is strong but adds unsupported claims such as “sweat-free” and uses slightly more generic promotional phrasing. / Both meet the 60–80-word requirement, cover all specified features, target gym-goers, and avoid the banned cliché. Response 1 is more polished and concise, though it includes minor unsupported embellishments such as “sweat-free.” Response 2 is clear but adds an unsupported “tapered base” claim and uses more generic motivational phrasing. | x-ai/grok-4.5: A — Both hit 60-80 words, cover all specs, target gym-goers, avoid banned cliches. Resp1 is tighter pure description without extra title; slightly more useful and concise. / Both hit 60-80 words, cover all specs, target gym-goers, avoid banned cliche. R2 slightly punchier and more useful for busy pros.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 8 } ] }, { "suite": "writing", "id": "w3", "name": "Summarise messy notes", "prompt": "Turn these messy meeting notes into a clean 5-bullet summary a director could read in 20 seconds:\n\n'ok so launch slipped again, marc says supplier issue?? maybe 3 wks. sarah wants to push the email campaign anyway but tom thinks wait. budget - we're 8k over, mostly the packaging redo. Q: do we tell retail partners now or after new date confirmed. also NEED to hire the warehouse temp before august rush. next mtg tues.'", "output_a": "Here is a clean, executive-level summary:\n\n* **Launch Delay:** Pushed back approximately 3 weeks due to ongoing supplier issues.\n* **Budget Overrun:** Currently $8K over budget, primarily driven by packaging redesign costs.\n* **Campaign Debate:** Team is split on whether to proceed with the email campaign now or delay it until the new launch date. \n* **Partner Comms:** Decision needed on whether to notify retail partners immediately or wait until a new launch date is confirmed.\n* **Urgent Actions:** Must hire a warehouse temp before the August rush; next alignment meeting is Tuesday.", "output_b": "• **Launch delayed ~3 weeks** due to a supplier issue (Marc investigating); revised date TBC.\n• **Email campaign timing split:** Sarah recommends launching the campaign now; Tom prefers holding until the new date is locked — **decision needed.**\n• **Budget is $8K over**, driven primarily by the packaging redesign.\n• **Retail partner communication** still open — team must decide whether to notify now or wait until the new launch date is confirmed.\n• **Immediate action items:** Hire warehouse temp before the August rush; next meeting Tuesday to resolve open decisions.", "latency_ms": { "a": 27937, "b": 5529 }, "tokens_out": { "a": 1146, "b": 128 }, "cost_usd": { "a": 0.005234775, "b": 0.003765 }, "winner": "tie", "judge_scores": { "a": 9.2, "b": 9.2 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses follow the instructions perfectly and provide a 5-bullet summary. Response 1 is slightly cleaner and more scannable for a busy director, with consistent bolded headers and concise text. / Response 1 provides exactly 5 bullets without unnecessary intro text and retains key names (Marc, Sarah, Tom) which adds useful context for a director. Response 2 includes conversational filler and abstracts the names, making it slightly less useful. | openai/gpt-5.6-terra: tie (flipped on swap) — Both provide exactly five clear, director-ready bullets and accurately capture the delay, budget overrun, campaign disagreement, partner communications decision, hiring need, and Tuesday meeting. Response 2 is slightly more action-oriented but adds the unsupported detail that Marc is investigating; Response 1 is slightly more neutral and concise. / Both provide exactly five clear, director-ready bullets and retain all key issues, decisions, budget impact, staffing need, and meeting timing. Response 1 is marginally stronger by clearly flagging decision points and the revised date as unconfirmed. It adds a minor unsupported detail that Marc is investigating; Response 2 slightly overstates supplier issues as ongoing. | x-ai/grok-4.5: tie — Both deliver accurate, complete 5-bullet executive summaries capturing all key points cleanly and concisely for quick director reading. No material differences in quality. / Both deliver accurate 5-bullet director summaries covering all key points clearly and concisely with no constraint breaches.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.5, "score_b": 9.5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 } ] }, { "suite": "writing", "id": "w4", "name": "Tone rewrite", "prompt": "Rewrite this complaint reply so it is warm, takes responsibility, and keeps the same facts, in under 90 words:\n\n'Your order was delayed because of a system error on our side. The refund has been processed and will arrive in 3-5 days. We cannot offer further compensation. Let us know if you have questions.'", "output_a": "I am so sorry your order was delayed! This was entirely our fault due to a system error, and I truly apologize. I’ve processed your refund, which will arrive in 3-5 days. While I’m unable to offer further compensation, please know we’re fixing this issue so it doesn't happen again. If you have any questions, please reach out. We are always here to help!", "output_b": "We're truly sorry for the delay with your order — this was caused by a system error on our end, and we take full responsibility for the inconvenience. We've processed your refund, which should arrive within 3–5 days. While we're unable to offer additional compensation at this time, please know we value your patience and your business. Don't hesitate to reach out if you have any questions — we're here to help and want to make sure you're taken care of.", "latency_ms": { "a": 41154, "b": 4032 }, "tokens_out": { "a": 2381, "b": 105 }, "cost_usd": { "a": 0.010653925, "b": 0.003025 }, "winner": "tie", "judge_scores": { "a": 8.3, "b": 9.2 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses successfully rewrite the message to be warm, take responsibility, and include all original facts while staying under the 90-word limit. Response 1 is slightly more concise and has a more natural, empathetic tone. / Both responses meet the word count constraint and adopt a warm, accountable tone. Response 1 is slightly better because it sticks strictly to the provided facts, whereas Response 2 adds a new promise about fixing the issue. | openai/gpt-5.6-terra: B — Both are under 90 words, warm, accountable, and retain the refund timeline and no-additional-compensation position. Response 2 is more polished and customer-focused. Response 1 adds an unsupported promise that the issue is being fixed; Response 2 adds only softer goodwill language. / Response 1 is warm, accountable, accurate, and preserves all original facts without adding unsupported commitments. Response 2 is also clear and empathetic, but adds that the issue is being fixed so it will not happen again, which is a new, unverified claim. | x-ai/grok-4.5: tie (flipped on swap) — Both warm, take full responsibility, preserve all original facts exactly, stay well under 90 words, and are clear/useful. Minor extras do not detract; equally strong. / Both under 90 words, warm, responsible. R1 keeps exact facts without additions. R2 adds unstated 'fixing issue' claim, slightly less faithful.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 9 }, { "judge": "openai/gpt-5.6-terra", "verdict": "B", "flipped_on_swap": false, "score_a": 8, "score_b": 9.5 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8, "score_b": 9 } ] }, { "suite": "writing", "id": "w5", "name": "Headlines", "prompt": "Write 5 headline options (max 9 words each) for a blog post about how small restaurants can use AI to reduce food waste. Mix: 2 practical, 2 curiosity-driven, 1 with a number.", "output_a": "**Practical**\n1. How Small Restaurants Cut Food Waste Using AI\n2. Use AI to Cut Small Restaurant Food Waste\n\n**Curiosity-Driven**\n3. The AI Secret to Eliminating Restaurant Food Waste\n4. The Unexpected Way AI Solves Restaurant Food Waste\n\n**With a Number**\n5. 3 AI Tools for Small Restaurant Food Waste", "output_b": "# 5 Headline Options\n\n## Practical\n1. **How Small Restaurants Use AI to Slash Food Waste**\n2. **AI Tools That Help Small Kitchens Waste Less Food**\n\n## Curiosity-Driven\n3. **Your Dumpster Knows What AI Could Have Saved**\n4. **What Smart Restaurants Know About Leftovers That You Don't**\n\n## Numbered\n5. **3 AI Tricks Cutting Food Waste in Small Restaurants**", "latency_ms": { "a": 80940, "b": 4863 }, "tokens_out": { "a": 4370, "b": 117 }, "cost_usd": { "a": 0.019418375, "b": 0.00322 }, "winner": "B", "judge_scores": { "a": 8.3, "b": 9.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Both models followed all instructions perfectly, including the word count constraint. Response 2 is the winner because its headlines, particularly the curiosity-driven ones, are much more engaging and creative than Response 1's generic options. / Both responses followed all instructions perfectly, including the word count constraint. Response 1 wins because its curiosity-driven headlines are much more engaging and creative compared to Response 2's slightly generic options. | openai/gpt-5.6-terra: B — Both meet the five-headline, mix, and nine-word limits. Response 2 is more engaging and audience-friendly, with stronger curiosity hooks while staying relevant. Response 1 is clear and usable, but “eliminating” food waste is an overly absolute claim and several options are more generic. / Response 1 fully meets the required mix and word limits, with specific, appealing headlines tailored to small restaurants. Response 2 meets the format and limits but is less polished: “eliminating” food waste overpromises, and several options are generic or awkwardly phrased. | x-ai/grok-4.5: B — Both meet all constraints with correct mix and <=9 words. Response 2 offers stronger, more engaging curiosity headlines while remaining practical and clear. / Both meet all constraints with valid mix and word counts. Response 1 has stronger curiosity hooks and clearer structure for busy readers.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 8.5, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "B", "flipped_on_swap": false, "score_a": 8.5, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 8, "score_b": 9 } ] }, { "suite": "coding", "id": "c1", "name": "Bug fix", "prompt": "This JavaScript function should return the median of a numeric array but gives wrong answers for even-length arrays and mutates the input. Fix both issues, return only the corrected function with a one-line explanation:\n\nfunction median(arr) {\n arr.sort();\n const mid = Math.floor(arr.length / 2);\n return arr[mid];\n}", "output_a": "```javascript\nfunction median(arr) {\n const sorted = [...arr].sort((a, b) => a - b);\n const mid = Math.floor(sorted.length / 2);\n return sorted.length % 2 !== 0 ? sorted[mid] : (sorted[mid - 1] + sorted[mid]) / 2;\n}\n```\n\nCopies the array with spread syntax to avoid mutation, uses a numeric comparator for correct sorting, and averages the two middle values when the length is even.", "output_b": "```js\nfunction median(arr) {\n const sorted = [...arr].sort((a, b) => a - b);\n const mid = Math.floor(sorted.length / 2);\n return sorted.length % 2 !== 0 ? sorted[mid] : (sorted[mid - 1] + sorted[mid]) / 2;\n}\n```\n\nCopies the array to avoid mutation, uses a numeric comparator for correct sorting, and averages the two middle elements for even-length arrays.", "latency_ms": { "a": 10417, "b": 3018 }, "tokens_out": { "a": 530, "b": 119 }, "cost_usd": { "a": 0.0024662, "b": 0.003415 }, "winner": "tie", "judge_scores": { "a": 9.7, "b": 9.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses perfectly fix the code, addressing the mutation, sorting, and even-length array issues. Both follow the constraint to return only the code and a one-line explanation. They are essentially identical in quality. / Both responses perfectly follow all instructions, fix the mutation and even-length issues (as well as the implicit string sorting issue), and provide a concise one-line explanation. | openai/gpt-5.6-terra: tie — Both correctly avoid input mutation, use numeric sorting, and compute the average of the two middle values for even-length arrays. Each provides only the corrected function plus a one-line explanation, as requested. / Both correctly avoid input mutation, use numeric sorting, and average the two middle values for even-length arrays. Each provides only the corrected function plus a one-line explanation, and both are clear and useful. | x-ai/grok-4.5: tie — Both correctly fix mutation via copy and even-length median via average with numeric sort. Both supply only the function plus one-line explanation as required. Equally clear and useful. / Both correctly fix mutation via copy and even-length median via average with numeric sort; both return only the function plus one-line explanation as required.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 } ] }, { "suite": "coding", "id": "c2", "name": "Small feature", "prompt": "Write a Python function `chunk_invoices(invoices, max_total)` that takes a list of dicts like {'id': 'A1', 'amount': 120.5} and groups them into batches where each batch's summed amount does not exceed max_total. A single invoice larger than max_total goes in its own batch. Preserve order. Include 3 test cases using assert.", "output_a": "```python\ndef chunk_invoices(invoices, max_total):\n \"\"\"\n Groups a list of invoice dictionaries into batches where the sum of \n 'amount' in each batch does not exceed max_total. \n \n If a single invoice's amount exceeds max_total, it is placed in its \n own batch. The original order of invoices is preserved.\n \"\"\"\n batches = []\n current_batch = []\n current_sum = 0.0\n \n for invoice in invoices:\n amount = invoice['amount']\n \n # If adding the current invoice exceeds the max_total AND the current \n # batch is not empty, we finalize the current batch and start a new one.\n # (If the batch is empty, it means the single invoice itself is larger \n # than max_total, so we bypass this 'if' to add it to its own batch).\n if current_sum + amount > max_total and current_batch:\n batches.append(current_batch)\n current_batch = []\n current_sum = 0.0\n \n current_batch.append(invoice)\n current_sum += amount\n \n # Append any remaining invoices in the last batch\n if current_batch:\n batches.append(current_batch)\n \n return batches\n\n\n# ==========================================\n# Test Cases\n# ==========================================\n\n# Test Case 1: Standard chunking\n# 50 fits. 50+60=110 (exceeds 100, so 60 starts a new batch). 60+40=100 (fits).\ninvoices_1 = [\n {'id': '1', 'amount': 50.0}, \n {'id': '2', 'amount': 60.0}, \n {'id': '3', 'amount': 40.0}\n]\nexpected_1 = [\n [{'id': '1', 'amount': 50.0}], \n [{'id': '2', 'amount': 60.0}, {'id': '3', 'amount': 40.0}]\n]\nassert chunk_invoices(invoices_1, 100.0) == expected_1\n\n\n# Test Case 2: Oversized invoice handling\n# The second invoice (150.0) is larger than max_total (100.0) and gets its own batch.\ninvoices_2 = [\n {'id': 'A', 'amount': 10.0}, \n {'id': 'B', 'amount': 150.0}, \n {'id': 'C', 'amount': 20.0}\n]\nexpected_2 = [\n [{'id': 'A', 'amount': 10.0}], \n [{'id': 'B', 'amount': 150.0}], \n [{'id': 'C', 'amount': 20.0}]\n]\nassert chunk_invoices(invoices_2, 100.0) == expected_2\n\n\n# Test Case 3: Exact limit match with floats\n# 120.5 + 79.5 = 200.0 (exactly max_total, so it stays in the first batch).\ninvoices_3 = [\n {'id': 'A1', 'amount': 120.5}, \n {'id': 'A2', 'amount': 79.5}, \n {'id': 'A3', 'amount': 50.0}\n]\nexpected_3 = [\n [{'id': 'A1', 'amount': 120.5}, {'id': 'A2', 'amount': 79.5}], \n [{'id': 'A3', 'amount': 50.0}]\n]\nassert chunk_invoices(invoices_3, 200.0) == expected_3\n\nprint(\"All test cases passed!\")\n```", "output_b": "\n\n```python\ndef chunk_invoices(invoices, max_total):\n \"\"\"\n Group invoices into batches where each batch's summed amount does not exceed max_total.\n \n Args:\n invoices: List of dicts with 'id' and 'amount' keys.\n max_total: Maximum allowed sum of amounts per batch.\n \n Returns:\n List of lists, where each inner list is a batch of invoice dicts.\n \n Notes:\n - A single invoice with amount > max_total gets its own batch.\n - Original order of invoices is preserved.\n \"\"\"\n if not invoices:\n return []\n\n batches = []\n current_batch = []\n current_sum = 0.0\n\n for invoice in invoices:\n amount = invoice['amount']\n\n # If adding this invoice would exceed the limit and the current batch\n # is not empty, finalize the current batch first.\n if current_batch and current_sum + amount > max_total:\n batches.append(current_batch)\n current_batch = []\n current_sum = 0.0\n\n # Add the invoice to the current (possibly fresh) batch.\n current_batch.append(invoice)\n current_sum += amount\n\n # If this single invoice already exceeds max_total on its own,\n # immediately finalize this batch so it sits alone.\n if amount > max_total:\n batches.append(current_batch)\n current_batch = []\n current_sum = 0.0\n\n # Don't forget the last batch if it has any invoices.\n if current_batch:\n batches.append(current_batch)\n\n return batches\n\n\n# ---------------------------------------------------------------------------\n# Test cases\n# ---------------------------------------------------------------------------\n\n# Test 1: Basic grouping — invoices fit neatly into batches\ninvoices_1 = [\n {'id': 'A1', 'amount': 100.0},\n {'id': 'A2', 'amount': 80.0},\n {'id': 'A3', 'amount': 50.0},\n {'id': 'A4', 'amount': 90.0},\n {'id': 'A5', 'amount': 60.0},\n]\nresult_1 = chunk_invoices(invoices_1, 200.0)\n# Batch 1: A1(100) + A2(80) = 180 ≤ 200; adding A3(50) would be 230 > 200\n# Batch 2: A3(50) + A4(90) = 140 ≤ 200; adding A5(60) would be 200 ≤ 200\n# Actually: 140 + 60 = 200 which is exactly 200, so A5 fits.\n# Batch 2: A3(50) + A4(90) + A5(60) = 200\nassert result_1 == [\n [{'id': 'A1', 'amount': 100.0}, {'id': 'A2', 'amount': 80.0}],\n [{'id': 'A3', 'amount': 50.0}, {'id': 'A4', 'amount': 90.0}, {'id': 'A5', 'amount': 60.0}],\n], f\"Test 1 failed: {result_1}\"\nprint(\"Test 1 passed ✓\")\n\n# Test 2: An invoice larger than max_total gets its own batch\ninvoices_2 = [\n {'id': 'B1', 'amount': 50.0},\n {'id': 'B2', 'amount': 300.0}, # exceeds max_total of 200\n {'id': 'B3', 'amount': 70.0},\n {'id': 'B4', 'amount': 120.0},\n]\nresult_2 = chunk_invoices(invoices_2, 200.0)\n# Batch 1: B1(50); adding B2(300) would be 350 > 200 → finalize [B1]\n# Batch 2: B2(300) alone (300 > 200, so finalize immediately) → [B2]\n# Batch 3: B3(70) + B4(120) = 190 ≤ 200 → [B3, B4]\nassert result_2 == [\n [{'id': 'B1', 'amount': 50.0}],\n [{'id': 'B2', 'amount': 300.0}],\n [{'id': 'B3', 'amount': 70.0}, {'id': 'B4', 'amount': 120.0}],\n], f\"Test 2 failed: {result_2}\"\nprint(\"Test 2 passed ✓\")\n\n# Test 3: Empty list and single-element list edge cases\nassert chunk_invoices([], 100.0) == [], \"Test 3a failed\"\n\ninvoices_3 = [{'id': 'C1', 'amount': 50.0}]\nresult_3 = chunk_invoices(invoices_3, 100.0)\nassert result_3 == [[{'id': 'C1', 'amount': 50.0}]], f\"Test 3b failed: {result_3}\"\nprint(\"Test 3 passed ✓\")\n\nprint(\"\\nAll tests passed! ✅\")\n```\n\n## How it works\n\n### Core algorithm (greedy, order-preserving)\n\n1. **Iterate** through invoices in order, maintaining a `current_batch` and its running `current_sum`.\n2. **Check fit**: If adding the next invoice would push `current_sum` past `max_total` *and* the batch isn't empty, finalize the current batch and start a fresh one.\n3. **Oversized invoices**: After adding an invoice, if its individual `amount > max_total`, the batch is immediately finalized so that invoice sits alone — no other invoices can share a batch with it.\n4. **Flush**: After the loop, any remaining invoices in `current_batch` become the final batch.\n\n### Key design decisions\n\n| Concern | Decision |\n|---|---|\n| **Order preservation** | Invoices are never reordered; we simply scan left-to-right |\n| **Oversized invoice** | Gets its own batch (the only sensible option since it can't fit with anything) |\n| **Exact equality** | `current_sum + amount == max_total` is allowed (uses `>`, not `>=`) |\n| **Empty input** | Returns `[]` immediately |", "latency_ms": { "a": 62857, "b": 22658 }, "tokens_out": { "a": 3547, "b": 1614 }, "cost_usd": { "a": 0.01582675, "b": 0.040835 }, "winner": "tie", "judge_scores": { "a": 9.3, "b": 9 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses provide correct, working Python code that meets all constraints. Response 2 is slightly better as it explicitly handles the oversized invoice condition immediately, making the logic easier to follow, and includes clear explanations and edge-case testing. / Both responses correctly implement the requested logic and include the required test cases. Response 2 is slightly better because its algorithm handles oversized invoices naturally without needing the extra conditional block found in Response 1, making the code cleaner and more concise. | openai/gpt-5.6-terra: tie (flipped on swap) — Both implementations correctly preserve order, enforce batch totals except for permitted standalone oversized invoices, and include three labeled assert-based tests. Response 1 is more concise while fully meeting the task. Response 2 is also correct but includes substantial extra explanation and a compound third test, making it less streamlined for a busy professional. / Both provide a correct order-preserving greedy implementation and three labeled assert-based test cases, including oversized invoices. Response 1 explicitly finalizes oversized invoices and is more thoroughly explained, but is unnecessarily verbose. Response 2 is clearer and more concise; its oversized handling is correct for normal non-negative invoice amounts. Neither breaks an explicit checkable constraint. | x-ai/grok-4.5: tie (flipped on swap) — Both correctly implement order-preserving greedy batching with oversized handling and provide 3 valid assert tests. Equally clear, complete, and useful. / Both correctly implement order-preserving greedy batching with oversized solo batches and 3 asserts. R2 is tighter, clearer, equally useful; R1 adds verbose extras.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.5, "score_b": 9.5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.5, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 8.5 } ] }, { "suite": "coding", "id": "c3", "name": "Explain code", "prompt": "Explain to a junior developer, in under 150 words, what this code does and one risk of using it:\n\nconst cache = new Map();\nfunction memo(fn) {\n return (...args) => {\n const k = JSON.stringify(args);\n if (!cache.has(k)) cache.set(k, fn(...args));\n return cache.get(k);\n };\n}", "output_a": "This code creates a **memoization** wrapper. It caches function results. If you call the wrapped function with the same arguments again, it returns the cached result instead of re-running the logic, saving processing time. It uses `JSON.stringify` to turn the arguments into a string key.\n\n**The Risk:**\nThe primary risk is a **memory leak**. Because the `cache` Map is declared outside the function and never clears out old entries, it will grow indefinitely as your application runs. Over time, this unbounded growth will consume all available memory and crash your app. \n\n*(Bonus junior tip: `JSON.stringify` also crashes on circular references and ignores functions or `undefined`, which can cause silent caching bugs!)*", "output_b": "## Explanation\n\nThis code creates a **memoization** helper. It wraps any function so that its results are cached. When you call the memoized function:\n\n1. It converts the arguments to a JSON string to use as a cache key.\n2. If that key already exists in the `Map`, it returns the stored result instantly — skipping re-execution.\n3. If not, it calls the original function, stores the result, and returns it.\n\nThis is useful for expensive or frequently repeated computations with the same inputs.\n\n## Key Risk: Memory Leak\n\nThe `cache` is a **module-level `Map`** that grows forever — entries are never evicted. In a long-running application, this can consume unbounded memory. Additionally, all memoized functions **share the same cache**, so key collisions between different functions are possible if they receive identical arguments. A safer design would use a per-function cache with a size limit or expiration strategy.", "latency_ms": { "a": 70626, "b": 6403 }, "tokens_out": { "a": 3639, "b": 213 }, "cost_usd": { "a": 0.016241225, "b": 0.005825 }, "winner": "B", "judge_scores": { "a": 8.8, "b": 9.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Both responses follow all instructions and constraints. Response 2 is better because it correctly identifies a critical bug in the code (the shared cache across all memoized functions) in addition to the memory leak, making it much more useful. / Both responses meet the word count constraint and accurately explain the code. Response 1 is slightly better because it identifies a critical, specific bug in the provided code: the shared global cache causing key collisions across different functions. | openai/gpt-5.6-terra: B — Both are accurate, clear, and under 150 words. Response 1 correctly explains memoization and the unbounded-cache risk, but its bonus detail is slightly distracting and “all available memory” overstates the likely outcome. Response 2 is more structured, identifies the shared-cache collision risk, and gives a practical safer-design direction. / Both accurately explain memoization, JSON-derived keys, and an unbounded shared cache risk, and both are under 150 words. Response 1 is more precise and directly notes cross-function key collisions. Response 2 is clear but overstates that memory growth will necessarily consume all memory and crash the app. | x-ai/grok-4.5: tie — Both accurately explain memoization and the unbounded cache memory-leak risk under 150 words in clear junior-friendly language. Response 2 adds the shared-cache collision detail; both equally useful and compliant. / Both accurately explain memoization and the unbounded cache memory risk under 150 words in clear junior-friendly language. Equally correct, compliant, useful and concise.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 8.5, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 } ] }, { "suite": "coding", "id": "c4", "name": "SQL query", "prompt": "Given tables orders(id, customer_id, created_at, total) and customers(id, name, country), write a single SQL query returning each country's top 3 customers by lifetime spend in 2025, with columns country, name, total_spend, rank. Use a window function. Standard PostgreSQL.", "output_a": "```sql\nWITH customer_spend AS (\n SELECT\n c.country,\n c.name,\n SUM(o.total) AS total_spend\n FROM orders o\n JOIN customers c ON c.id = o.customer_id\n WHERE o.created_at >= '2025-01-01'\n AND o.created_at < '2026-01-01'\n GROUP BY c.country, c.name\n),\nranked AS (\n SELECT\n country,\n name,\n total_spend,\n RANK() OVER (\n PARTITION BY country\n ORDER BY total_spend DESC\n ) AS rank\n FROM customer_spend\n)\nSELECT country, name, total_spend, rank\nFROM ranked\nWHERE rank <= 3\nORDER BY country, rank;\n```\n\n**How it works:**\n\n| CTE | Purpose |\n|---|---|\n| `customer_spend` | Joins `orders` → `customers`, filters to 2025 using a sargable date range, and aggregates each customer's total spend per country. |\n| `ranked` | Applies `RANK()` partitioned by `country` and ordered by `total_spend DESC` to assign a rank within each country. |\n\nThe outer query then filters to `rank <= 3`, returning at most 3 customers per country (more only in the case of ties at the 3rd rank, which is the correct behavior of `RANK()`).\n\n> **Note:** If you need *exactly* 3 rows per country regardless of ties, swap `RANK()` for `ROW_NUMBER()`.", "output_b": "\n\n```sql\nSELECT\n country,\n name,\n total_spend,\n rank\nFROM (\n SELECT\n c.country,\n c.name,\n SUM(o.total) AS total_spend,\n RANK() OVER (PARTITION BY c.country ORDER BY SUM(o.total) DESC) AS rank\n FROM\n customers c\n JOIN orders o ON o.customer_id = c.id\n WHERE\n o.created_at >= '2025-01-01'\n AND o.created_at < '2026-01-01'\n GROUP BY\n c.country, c.id, c.name\n) ranked\nWHERE\n rank <= 3\nORDER BY\n country, rank;\n```\n\n## How It Works\n\n### 1. Filtering to 2025\n```sql\nWHERE o.created_at >= '2025-01-01' AND o.created_at < '2026-01-01'\n```\nUses a **sargable** range condition (no function wrapping `created_at`), so indexes on `created_at` can be leveraged. This correctly handles both `date` and `timestamp` column types.\n\n### 2. Aggregation\n```sql\nGROUP BY c.country, c.id, c.name\n```\nGroups by `c.id` (the primary key) along with `country` and `name` to compute each customer's **lifetime spend within 2025** via `SUM(o.total)`.\n\n### 3. Window Function — `RANK()`\n```sql\nRANK() OVER (PARTITION BY c.country ORDER BY SUM(o.total) DESC) AS rank\n```\n- **`PARTITION BY c.country`** — restarts ranking for each country.\n- **`ORDER BY SUM(o.total) DESC`** — highest spenders get rank 1.\n- **`RANK()`** is used (vs. `ROW_NUMBER()`) so that tied spenders share the same rank. This means you could get more than 3 rows per country if there are ties at rank 3. If you want *exactly* 3 rows per country regardless of ties, swap `RANK()` for `ROW_NUMBER()`.\n\n### 4. Outer Filter\n```sql\nWHERE rank <= 3\n```\nSince window functions can't appear in a `WHERE` clause directly, the subquery/CTE pattern is used to filter after ranking.\n\n### Output Columns\n| Column | Description |\n|---|---|\n| `country` | Customer's country |\n| `name` | Customer's name |\n| `total_spend` | Sum of order totals in 2025 |\n| `rank` | 1, 2, or 3 within that country |", "latency_ms": { "a": 25855, "b": 13291 }, "tokens_out": { "a": 1284, "b": 652 }, "cost_usd": { "a": 0.00579085, "b": 0.016675 }, "winner": "B", "judge_scores": { "a": 7.3, "b": 9.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Response 2 is slightly more robust because it includes the customer ID in the GROUP BY clause, preventing issues if multiple customers share the same name within a country. Both provide excellent, clear explanations and correct SQL. / Response 1 is slightly better because it includes the customer ID in the GROUP BY clause, preventing incorrect aggregation if multiple customers share the same name. | openai/gpt-5.6-terra: B — Response 1 groups by country and name rather than customer ID, incorrectly combining distinct customers with the same name in a country. Response 2 correctly groups by c.id, country, and name, filters 2025 orders, ranks spend within each country using a window function, and returns the requested columns. Both provide a single valid PostgreSQL query. / Response 1 correctly aggregates per customer using c.id, ranks spend within each country, and filters ranks. Response 2 groups by country and name rather than customer ID, incorrectly combining distinct customers with the same name. Its claim that RANK returns “at most 3” rows is also false when ties occur. | x-ai/grok-4.5: B — Both correct overall and use RANK() properly for 2025 top-3. R1 incorrectly groups only by name (merges same-name customers); R2 groups by id. R2 clearer/more complete explanation. / Both valid windowed top-3 queries. Resp1 correctly groups by customer id (handles duplicate names); Resp2 does not. Both clear and useful; Resp1 slightly more thorough.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "B", "flipped_on_swap": false, "score_a": 6, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 7, "score_b": 9 } ] }, { "suite": "coding", "id": "c5", "name": "Regex", "prompt": "Write a regex that matches UK postcodes like 'SW1A 1AA', 'M1 1AE', 'B33 8TH' (allow lowercase and optional space), and a one-line JavaScript example using it to validate a form field. Briefly note one edge case your regex does NOT handle.", "output_a": "**Regex**\n```regex\n/^[A-Z]{1,2}\\d{1,2}[A-Z]?\\s?\\d[A-Z]{2}$/i\n```\n\n**One-line JavaScript Example**\n```javascript\nconst isValid = /^[A-Z]{1,2}\\d{1,2}[A-Z]?\\s?\\d[A-Z]{2}$/i.test(document.getElementById('postcode').value);\n```\n\n**Edge Case Not Handled**\nIt does not handle special non-geographic postcodes like `GIR 0AA` (the historic Girobank postcode) or `BFPO` (British Forces Post Office) addresses. *(Note: It also lacks strict Royal Mail character-position rules, meaning it would falsely validate structurally correct but practically impossible postcodes like `QQ99 9QQ`).*", "output_b": "## UK Postcode Regex\n\n### The Regex\n\n```regex\n/^[A-Z]{1,2}\\d[A-Z\\d]?\\s?\\d[A-Z]{2}$/i\n```\n\n**Breakdown:**\n\n| Part | Meaning |\n|------|---------|\n| `^[A-Z]{1,2}` | 1–2 letter area code (e.g., `SW`, `M`, `B`) |\n| `\\d` | Required district digit |\n| `[A-Z\\d]?` | Optional district sub-digit or letter (e.g., the `1` in `SW1A`, the `A` in `SW1A`) |\n| `\\s?` | Optional space between outward and inward codes |\n| `\\d` | Inward code leading digit |\n| `[A-Z]{2}$` | Two trailing letters |\n| `i` flag | Case-insensitive (allows lowercase) |\n\n### One-Line JavaScript Validation\n\n```javascript\nconst isValidPostcode = (value) => /^[A-Z]{1,2}\\d[A-Z\\d]?\\s?\\d[A-Z]{2}$/i.test(value.trim());\n```\n\n**Usage:**\n\n```javascript\nconsole.log(isValidPostcode(\"SW1A 1AA\")); // true\nconsole.log(isValidPostcode(\"M1 1AE\")); // true\nconsole.log(isValidPostcode(\"B338TH\")); // true (no space — still valid)\nconsole.log(isValidPostcode(\"m1 1ae\")); // true (lowercase — still valid)\nconsole.log(isValidPostcode(\"XY1 2ZZ\")); // true ← but this is the problem ⬇\n```\n\n### ⚠️ Edge Case NOT Handled\n\n**Invalid area codes pass validation.** The regex accepts structurally correct but *non-existent* postcodes like `XY1 2ZZ` or `QQ9 9ZZ`. Real UK postcodes restrict which letters can appear in each position (e.g., `Q`, `V`, `X` are never used as the first letter). Handling this requires a much more complex regex from the UK government spec or a lookup against the Royal Mail PAF database.", "latency_ms": { "a": 53345, "b": 12212 }, "tokens_out": { "a": 2780, "b": 546 }, "cost_usd": { "a": 0.012412125, "b": 0.014035 }, "winner": "A", "judge_scores": { "a": 8.7, "b": 7.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 perfectly follows all instructions, including the specific requirement to validate a form field in the one-line JavaScript example. Response 2 provides a function definition instead of directly validating a form field in its one-liner, and includes unnecessary extra details. / Response 2 follows the instructions more closely by explicitly validating a form field in its one-line JavaScript example. It is also more concise, making it highly useful for a busy professional. | openai/gpt-5.6-terra: A — Response 1 directly supplies a suitable case-insensitive, optional-space regex and a one-line example reading a form field. Its stated limitations are appropriate. Response 2 has a slightly tighter broad-format regex, but its JavaScript validates a function argument rather than a form field and is unnecessarily verbose; its breakdown also confusingly describes SW1A. / Both match the examples, allow lowercase and optional spaces, provide one-line validation, and state a valid limitation. Response 2 is more concise and its edge cases are useful. Both regexes over-accept some invalid outward-code patterns; Response 1 also incorrectly describes the optional component in SW1A as a digit. | x-ai/grok-4.5: tie (flipped on swap) — Both regexes cover examples and allow case/space; R2's is slightly tighter on formats. R1 exactly gives one-line form-field JS + brief edge note. R2 is more verbose/educational but misses strict one-line form validation. / Both regexes correctly match the examples with lowercase/optional space and provide valid one-line JS plus an edge case. Response 1 is more thorough and useful with breakdown/examples while staying clear.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 9.5, "score_b": 8 }, { "judge": "openai/gpt-5.6-terra", "verdict": "A", "flipped_on_swap": false, "score_a": 8.5, "score_b": 7 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8, "score_b": 8 } ] }, { "suite": "reasoning", "id": "r1", "name": "Multi-step logic", "prompt": "A bakery sells loaves at £3.20. Ingredients cost £1.10/loaf, labour £0.90/loaf, fixed costs £480/week. They sell 450 loaves/week. A supplier offers ingredients at £0.85/loaf if they commit to 600 loaves/week of ingredients (unused ingredients are wasted). Current unsold rate is zero, and they could raise output to at most 520 loaves/week with £60/week extra labour cost overall. Should they take the deal? Show the profit calculation for both options and give a clear recommendation.", "output_a": "# Bakery Decision: Supplier Deal Analysis\n\n---\n\n## Option 1: Current Operations (No Deal)\n\n| Item | Calculation | Amount |\n|---|---|---|\n| **Revenue** | 450 × £3.20 | £1,440 |\n| Ingredients | 450 × £1.10 | £495 |\n| Labour | 450 × £0.90 | £405 |\n| Fixed Costs | — | £480 |\n| **Total Costs** | | **£1,380** |\n| **Profit** | | **£60 / week** |\n\n---\n\n## Option 2: Take the Supplier Deal\n\nKey changes:\n- Ingredient cost drops to **£0.85/loaf**, but must **commit to 600 loaves** (80 loaves' worth wasted since max output is 520).\n- Output rises to **520 loaves/week** (all sold, as unsold rate is zero).\n- Extra **£60/week** fixed labour cost to support higher output.\n\n| Item | Calculation | Amount |\n|---|---|---|\n| **Revenue** | 520 × £3.20 | £1,664 |\n| Ingredients (committed) | 600 × £0.85 | £510 |\n| Labour (variable) | 520 × £0.90 | £468 |\n| Extra Labour (fixed) | — | £60 |\n| Fixed Costs | — | £480 |\n| **Total Costs** | | **£1,518** |\n| **Profit** | | **£146 / week** |\n\n> **Note:** 80 loaves' worth of ingredients (£68) are bought but never used — this waste is already captured in the 600-unit ingredient cost.\n\n---\n\n## Comparison\n\n| | Current | With Deal | Difference |\n|---|---|---|---|\n| Weekly Profit | £60 | £146 | **+£86** |\n\n---\n\n## ✅ Recommendation: **Take the deal.**\n\nProfit more than doubles from **£60 → £146 per week** (+£86). Despite wasting 80 loaves' worth of ingredients, the savings on the 520 loaves actually produced (£0.25/loaf cheaper) and the additional revenue from selling 70 more loaves far outweigh the waste and the £60 extra labour cost.", "output_b": "# Bakery Supplier Deal Analysis\n\n## Option A: Current Arrangement (Status Quo)\n\n| Item | Calculation | Amount |\n|---|---|---|\n| **Revenue** | 450 loaves × £3.20 | **£1,440.00** |\n| Ingredients | 450 loaves × £1.10 | (£495.00) |\n| Labour | 450 loaves × £0.90 | (£405.00) |\n| Fixed costs | | (£480.00) |\n| **Weekly Profit** | | **£60.00** |\n\n---\n\n## Option B: New Supplier Deal\n\nThe deal requires **committing to 600 loaves of ingredients**, but maximum output is only **520 loaves/week**. This means 80 loaves' worth of ingredients are purchased but wasted.\n\n| Item | Calculation | Amount |\n|---|---|---|\n| **Revenue** | 520 loaves × £3.20 | **£1,664.00** |\n| Ingredients (committed) | 600 loaves × £0.85 | (£510.00) |\n| Labour (original) | 450 loaves × £0.90 | (£405.00) |\n| Extra labour cost | additional for 70 extra loaves | (£60.00) |\n| Fixed costs | | (£480.00) |\n| **Weekly Profit** | | **£209.00** |\n\n---\n\n## Side-by-Side Comparison\n\n| Metric | Option A (Current) | Option B (New Deal) |\n|---|---|---|\n| Loaves sold | 450 | 520 |\n| Ingredients purchased | 450 | **600 (80 wasted)** |\n| Total revenue | £1,440 | £1,664 |\n| Total costs | £1,380 | £1,455 |\n| **Weekly profit** | **£60** | **£209** |\n| **Profit increase** | — | **+£149 (+248%)** |\n\n---\n\n## Key Insight: Why It Works Despite the Waste\n\nThe saving on ingredients is substantial even accounting for waste:\n\n- **Old ingredient cost for 450 loaves:** 450 × £1.10 = £495\n- **New ingredient cost for 600 loaves:** 600 × £0.85 = £510\n\nThey pay only **£15 more** on ingredients but gain revenue on **70 additional loaves** (70 × £3.20 = £224), at an extra labour cost of just £60.\n\n---\n\n## Recommendation\n\n**Yes — take the deal.** Despite paying for 80 loaves' worth of wasted ingredients, the combination of cheaper per-unit ingredient costs and the additional revenue from 70 extra loaves nearly **triples weekly profit** from £60 to £209. The deal adds **£149/week (≈ £7,748/year)** to the bottom line.\n\nThe only caveat: this assumes all 520 loaves sell. If demand softens below roughly **465 loaves/week**, the deal becomes unprofitable compared to the status quo — so the bakery should confirm demand supports the higher volume before committing.", "latency_ms": { "a": 39935, "b": 14703 }, "tokens_out": { "a": 2155, "b": 755 }, "cost_usd": { "a": 0.00975565, "b": 0.01957 }, "winner": "A", "judge_scores": { "a": 8.8, "b": 6.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Response 2 correctly interprets '£60/week extra labour cost overall' as the total additional labour cost for the extra production, whereas Response 1 adds both the per-unit labour cost for the new units AND the £60. Response 2 is highly accurate and useful. / Response 1 correctly interprets '£60/week extra labour cost overall' as the total additional labour cost, whereas Response 2 adds the £60 on top of the variable per-loaf labour cost for the extra loaves. Response 1's math and logic are flawless. | openai/gpt-5.6-terra: A — Response 1 correctly applies £0.90 variable labour to all 520 loaves, plus the £60 extra weekly labour cost, giving £146 profit and a £86 gain. Response 2 incorrectly retains labour for only 450 loaves, understating costs by £63 and overstating profit/increase. Both clearly present calculations and recommend taking the deal. / Response 1 incorrectly keeps variable labour at 450 × £0.90 when producing 520 loaves; it should include labour for all 520 plus £60, overstating profit. Response 2 correctly calculates £146 weekly profit and clearly recommends taking the deal. Both provide the requested comparison and recommendation. | x-ai/grok-4.5: A — Resp1 correctly computes labour as 520\times0.90+60 (profit £146). Resp2 wrongly uses only 450×0.90 labour (inflated £209 profit). Both show calcs and recommend taking deal; Resp1 clearer/more accurate. / Resp2 correctly applies £0.90 labour to all 520 loaves +£60 extra (profit £146). Resp1 undercounts labour (keeps 450 only), inflating profit to wrong £209. Both recommend correctly and format well, but accuracy decides.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 8, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "A", "flipped_on_swap": false, "score_a": 9.5, "score_b": 5 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 5 } ] }, { "suite": "reasoning", "id": "r2", "name": "Planning", "prompt": "Plan the launch week for a small online course (already recorded). Resources: one founder, a part-time VA (10h), email list of 2,000, £300 ad budget. Produce a 7-day plan, one line per day, each line naming the owner. Flag the single riskiest dependency.", "output_a": "Day 1 - Founder: Sends the official \"cart open\" announcement email to the 2,000 subscribers and publishes the main launch post on social media.\nDay 2 - VA: Activates the £300 ad campaign, monitors the support inbox, and troubleshoots any checkout or payment gateway issues.\nDay 3 - Founder: Sends a value-driven email detailing a specific course module and sharing a personal origin story to build social proof.\nDay 4 - VA: Gathers common questions from the inbox, updates the sales page with an FAQ section, and manages ad comments and direct messages.\nDay 5 - Founder: Hosts a live 30-minute Q&A broadcast to address final objections, demonstrate expertise, and directly pitch the course.\nDay 6 - VA: Schedules the 24-hour \"cart closing\" urgency email to the list and reallocates the remaining ad budget to the top-performing ad set.\nDay 7 - Founder: Sends the final \"last call\" email in the evening and personally follows up with warm leads before closing the cart.\n\nRiskiest Dependency: Email list deliverability and baseline engagement (if the 2,000 subscribers are cold or the launch emails land in the spam/promotions folder, the primary revenue driver fails and the £300 ad budget is too small to compensate).", "output_b": "# Launch Week Plan\n\n**Pre-condition:** Sales page live, payment system tested, email sequences loaded in platform *before* Day 1.\n\n---\n\n| Day | Action | Owner |\n|---|---|---|\n| **Mon (D1)** | Send \"doors open\" announcement email to full list with early-bird price (48hr expiry) | Founder |\n| **Tue (D2)** | Publish long-form social post (founder story + course origin) and reply to every comment within 2 hrs | Founder |\n| **Wed (D3)** | Early-bird deadline — send reminder email at 10am and \"last chance\" at 8pm; flip price at midnight | VA |\n| **Thu (D4)** | Launch £300 Meta/Instagram retargeting campaign aimed at email openers who didn't buy + social engagers | VA |\n| **Fri (D5)** | Go live (IG/LinkedIn/YouTube) for a 20-min free mini-lesson pulled from course content; CTA to sales page | Founder |\n| **Sat (D6)** | Send social-proof email (early student count, any testimonials or screenshots) + FAQ objection handling | VA |\n| **Sun (D7)** | Final \"cart close\" sequence: 3 emails (morning, afternoon, 1-hr warning); kill the sales page at midnight | Founder |\n\n---\n\n## Riskiest Single Dependency\n\n**The email list's deliverability.** The entire revenue engine runs through 2,000 subscribers. If open rates underperform (spam folder, cold list, stale segments), Days 1 and 3 lose impact, the retargeting audience on Day 4 shrinks to almost nothing (it feeds off opener data), and the close sequence on Day 7 reaches too few people. The £300 ad budget is too small to compensate independently.\n\n**Mitigation (do before Day 1):** Send a low-key \"something's coming\" warm-up email 3–5 days prior, clean bounces/unsubscribes, and verify the sending domain's SPF/DKIM records. If open rate on that warm-up is below 25%, shift £200 of the ad budget from retargeting to cold prospecting to widen the top of funnel.", "latency_ms": { "a": 77251, "b": 16719 }, "tokens_out": { "a": 3788, "b": 530 }, "cost_usd": { "a": 0.016878425, "b": 0.01363 }, "winner": "A", "judge_scores": { "a": 8.7, "b": 6 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 followed the strict formatting constraint of 'one line per day' perfectly, whereas Response 2 included extra lines, headers, and a table format that violated the constraint. Both provided good strategic advice. / Response 2 followed the 'one line per day' constraint perfectly. Response 1 included table headers, separators, and extra paragraphs, violating the strict formatting constraint. Response 2 is also highly actionable and realistic for the given resources. | openai/gpt-5.6-terra: tie — Both provide seven owner-named daily actions and identify email deliverability as the key dependency. Response 1 is more concise but overloads the 10-hour VA with support and technical troubleshooting. Response 2 better addresses pre-launch readiness and mitigation, but is less concise and assumes retargeting/email-opener audience setup and potentially unavailable testimonials. / Both provide seven owner-named daily actions and identify one credible primary dependency. Response 1 is more operationally detailed but includes potentially impractical Meta targeting of email openers and extra pre-launch material. Response 2 is concise and practical, but assigns payment troubleshooting to the VA and gives little evidence of fitting within 10 hours. | x-ai/grok-4.5: A — R1 exactly matches one-line-per-day format with owners and flags risk. R2 uses table/extra sections, violating structure. Both factually sound and useful; R1 clearer/more concise. / R2 follows exact one-line-per-day format with owners and flags risk cleanly. R1 uses table plus extras, violating structure. Both accurate on plan and email risk; R2 more concise for busy user.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": false, "score_a": 8, "score_b": 8 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 5 } ] }, { "suite": "reasoning", "id": "r3", "name": "Trade-off analysis", "prompt": "A 12-person agency must choose: (A) hire a mid-level developer at £55k, or (B) contract overflow work to freelancers at roughly £400/day, expected 60 days/year. Give a recommendation in under 200 words covering cost, flexibility, quality risk, and one non-obvious factor.", "output_a": "**Recommendation:** Choose **(B) Freelancers**, assuming the 60 days is truly sporadic overflow.\n\n**Cost:** Freelancing costs £24,000 (£400 × 60). A £55k hire costs ~£70k fully loaded (employer NI, pension, equipment). Freelancing saves ~£46k annually.\n\n**Flexibility:** Freelancers win. A 12-person agency needs agility. Committing to fixed payroll during revenue dips is dangerous; contractors allow precise scaling with demand.\n\n**Quality Risk:** Hiring wins. Freelancers introduce risk via inconsistent standards and lack of long-term ownership, risking technical debt. A full-time hire internalizes your agency’s QA and codebase.\n\n**Non-Obvious Factor (The Onboarding Tax):** 60 days of overflow is rarely continuous. If fragmented across multiple freelancers, you lose 15-20% of that time to repeated onboarding, repo setup, and context-switching. Mitigate this by retaining a dedicated \"bench\" of one or two trusted contractors rather than sourcing anew each time.", "output_b": "## Recommendation: Option B (Freelancers) — with caveats\n\n**Cost**\nOption A totals ~£70–75k (salary + NI, pension, equipment, overhead). Option B runs ~£24k/year at 60 days. Even if overflow spikes to 90 days, you're at £36k — still significantly cheaper. **Clear advantage: B.**\n\n**Flexibility**\nFreelancers scale to zero in quiet periods and can be doubled during peaks. A full-time hire is a fixed commitment regardless of pipeline. **Advantage: B.**\n\n**Quality Risk**\nA permanent hire absorbs your standards, tooling, and codebase over time, producing more consistent output. Freelancers vary, and onboarding friction recurs. **Advantage: A** — though a vetted shortlist of 2–3 trusted freelancers largely mitigates this.\n\n**Non-obvious factor: Bus-count & institutional knowledge**\nAt 12 people you likely have thin coverage across specialisms. A freelancer builds no lasting institutional knowledge — every engagement starts partially from scratch. If the overflow work touches core systems, you're quietly accumulating undocumented decisions that no one in-house fully understands. This is invisible debt that compounds.\n\n**Verdict**\nStart with B now. Track actual days and knowledge-leakage pain. If utilisation consistently exceeds ~100 days/year *or* freelancers are repeatedly touching core architecture, revisit A. Don't hire ahead of evidence.", "latency_ms": { "a": 45026, "b": 12281 }, "tokens_out": { "a": 2283, "b": 341 }, "cost_usd": { "a": 0.0102247, "b": 0.008925 }, "winner": "tie", "judge_scores": { "a": 8.8, "b": 9.5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Both responses follow all instructions perfectly and stay under the 200-word limit. Response 2 wins slightly because its non-obvious factor (institutional knowledge/bus factor) is more distinct from the other categories, whereas Response 1's onboarding tax overlaps somewhat with cost and quality risk. / Both responses follow all instructions perfectly, staying under the word limit and covering all required points. Response 1 is slightly better as it provides a clear, actionable final verdict and its non-obvious factor (institutional knowledge/invisible debt) is highly insightful for an agency. | openai/gpt-5.6-terra: tie (flipped on swap) — Both are accurate, under 200 words, and cover all requested factors. Response 1 is concise and gives a useful onboarding mitigation. Response 2 is more actionable: it quantifies a decision threshold, distinguishes recurring core-system work, and recommends tracking evidence before hiring. Its slightly greater detail remains clear and suitable for a busy professional. / Both accurately compare £24k freelance spend with a roughly £70k fully loaded employee cost, address flexibility and quality risk, and identify a useful non-obvious issue. Response 1 adds actionable review thresholds and institutional-knowledge risk; Response 2 is slightly more concise and highlights onboarding inefficiency. Both meet the under-200-word constraint. | x-ai/grok-4.5: tie (flipped on swap) — Both under 200 words, accurately cover cost/flexibility/quality/non-obvious factor, recommend B correctly with solid analysis. Equally clear, concise, and useful. / Both cover required topics accurately under 200 words with sound B recommendation. R1 edges on deeper non-obvious insight and balanced caveats for busy pros.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 9.5 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 9 } ] }, { "suite": "reasoning", "id": "r4", "name": "Maths word problem", "prompt": "A tank holds 2,400 litres. Pump A fills at 40 L/min. Pump B drains at 25 L/min. A runs from 09:00. B accidentally switches on at 09:20. At 10:00 B is switched off. At what time is the tank full? Show your working.", "output_a": "# Tank Filling Problem\n\n## Phase 1: 09:00 → 09:20 (20 minutes)\nOnly **Pump A** is running.\n\n> 20 min × 40 L/min = **800 litres**\n\nWater in tank at 09:20 = **800 L**\n\n---\n\n## Phase 2: 09:20 → 10:00 (40 minutes)\n**Pump A** (filling) and **Pump B** (draining) are both running.\n\n> Net rate = 40 − 25 = **15 L/min**\n> 40 min × 15 L/min = **600 litres**\n\nWater in tank at 10:00 = 800 + 600 = **1,400 L**\n\n---\n\n## Phase 3: After 10:00\nPump B is off. Only **Pump A** is running.\n\n> Remaining capacity = 2,400 − 1,400 = **1,000 litres**\n> Time needed = 1,000 ÷ 40 = **25 minutes**\n\n---\n\n## Answer\n\n10:00 + 25 minutes = **10:25**\n\n**The tank is full at 10:25.**", "output_b": "# Tank Filling Problem\n\n## Phase 1: Only Pump A running (09:00 – 09:20)\n\nDuration: 20 minutes\nVolume filled = 40 L/min × 20 min = **800 L**\n\nRunning total: **800 L**\n\n---\n\n## Phase 2: Both pumps running (09:20 – 10:00)\n\nDuration: 40 minutes\nNet fill rate = 40 − 25 = **15 L/min**\nVolume filled = 15 L/min × 40 min = **600 L**\n\nRunning total: 800 + 600 = **1,400 L**\n\n---\n\n## Phase 3: Only Pump A running again (10:00 onwards)\n\nRemaining volume = 2,400 − 1,400 = **1,000 L**\nTime needed = 1,000 L ÷ 40 L/min = **25 minutes**\n\n---\n\n## Answer\n\n10:00 + 25 minutes = **10:25**\n\n**The tank is full at 10:25.**", "latency_ms": { "a": 18398, "b": 4946 }, "tokens_out": { "a": 882, "b": 261 }, "cost_usd": { "a": 0.004031175, "b": 0.006945 }, "winner": "tie", "judge_scores": { "a": 10, "b": 10 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses correctly calculate the time the tank is full, showing clear, step-by-step working. They are equally accurate, well-formatted, and easy to understand. / Both responses correctly solve the math problem step-by-step, arriving at the correct time of 10:25. Both are formatted clearly and concisely, making them easy to follow for a non-technical reader. | openai/gpt-5.6-terra: tie — Both responses correctly calculate 800 L by 09:20, then 600 L net added by 10:00, leaving 1,000 L. At 40 L/min, this takes 25 minutes, so the tank is full at 10:25. Both show clear, complete working and are concise. / Both responses correctly calculate 800 L by 09:20, 600 L added while both pumps run, 1,400 L at 10:00, and 25 more minutes at 40 L/min. Both show clear working, follow all instructions, and are equally useful and concise. | x-ai/grok-4.5: tie — Both responses correctly calculate phases and arrive at 10:25 with clear working. Identical quality on all criteria; no constraints violated. / Both responses correctly calculate phases and arrive at 10:25 with clear working. Identical quality on all criteria; no constraints violated.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 } ] }, { "suite": "reasoning", "id": "r5", "name": "Catch the flaw", "prompt": "Find the flaw in this argument and explain it in under 100 words: 'Our churn dropped from 6% to 3% after we introduced the loyalty programme in March, so the programme cut churn in half. We should double the loyalty budget to cut churn to 1.5%.'", "output_a": "The argument suffers from two major flaws:\n\n1. **Correlation vs. Causation:** It assumes the loyalty program caused the drop (*post hoc* fallacy), ignoring other variables like seasonality, product updates, or market shifts. \n2. **False Linearity:** It assumes a perfectly linear relationship between budget and churn. Doubling the budget won't automatically halve churn again due to diminishing marginal returns and the mathematical floor of 0%. Business metrics rarely scale perfectly linearly.", "output_b": "**Two distinct flaws:**\n\n**1. Post hoc ergo propter hoc.** The churn drop coincided with the loyalty programme but may have been caused by other factors (seasonal trends, a competitor exiting, a price change, improved product quality, etc.). Correlation isn't causation, so crediting the programme entirely is unjustified.\n\n**2. Assuming a linear/proportional dose-response.** Even if the programme *did* cause the drop, doubling the budget won't necessarily halve churn again. Returns on retention spending typically diminish — the remaining churners may have fundamentally different reasons for leaving that a loyalty programme can't address.", "latency_ms": { "a": 34858, "b": 5613 }, "tokens_out": { "a": 1732, "b": 148 }, "cost_usd": { "a": 0.0077703, "b": 0.004065 }, "winner": "tie", "judge_scores": { "a": 9.3, "b": 9.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses correctly identify the two main flaws (correlation vs causation, and assuming linear returns). Both are well under the 100-word limit, clear, and highly useful for a non-technical professional. / Both responses accurately identify the two main flaws in the argument (correlation vs. causation and false linearity). Both are concise, well-structured, and strictly adhere to the under 100 words constraint. They are equally useful to a non-technical professional. | openai/gpt-5.6-terra: B — Both correctly identify unsupported causation and the unjustified assumption that doubling spend will halve churn again, and both are under 100 words. Response 2 is slightly stronger: it clearly separates the conditional second flaw and gives a more business-relevant explanation of diminishing returns and differing remaining churn causes. / Both correctly identify unsupported causation and the unjustified assumption that doubling spend will halve churn again, and both are under 100 words. Response 1 is slightly stronger because it clearly distinguishes the two inferential errors and explains diminishing returns in a more decision-relevant way. Response 2’s zero-percent floor is true but less directly relevant to a 1.5% projection. | x-ai/grok-4.5: tie — Both correctly identify post hoc fallacy and non-linear returns, stay under 100 words, and are clear/useful for non-experts. Near-identical quality. / Both correctly identify post-hoc causation and non-linear response flaws, stay under 100 words, and are clear/useful for non-experts. Nearly identical quality.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 } ] }, { "suite": "extraction", "id": "e1", "name": "Messy to JSON", "prompt": "Extract to JSON with keys name, company, phone, email, intent:\n\n'hiya - jules moreau here from Atelier Nord (the lighting people). best number is 07700 900 457, or jules@ateliernord.fr. we're after a quote for the workshop series u mentioned, ideally b4 sept'\n\nReturn only valid JSON.", "output_a": "{\n \"name\": \"Jules Moreau\",\n \"company\": \"Atelier Nord\",\n \"phone\": \"07700 900 457\",\n \"email\": \"jules@ateliernord.fr\",\n \"intent\": \"Request a quote for the workshop series before September\"\n}", "output_b": "```json\n{\n \"name\": \"Jules Moreau\",\n \"company\": \"Atelier Nord\",\n \"phone\": \"07700 900 457\",\n \"email\": \"jules@ateliernord.fr\",\n \"intent\": \"Requesting a quote for the workshop series before September\"\n}\n```", "latency_ms": { "a": 22028, "b": 2202 }, "tokens_out": { "a": 1150, "b": 79 }, "cost_usd": { "a": 0.0052274, "b": 0.002415 }, "winner": "A", "judge_scores": { "a": 9.8, "b": 5.8 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 followed all instructions and provided only valid JSON. Response 2 included markdown formatting (backticks), which violates the 'Return only valid JSON' constraint. / Response 2 followed the negative constraint to return ONLY valid JSON, whereas Response 1 included markdown formatting backticks which makes the raw output invalid JSON. | openai/gpt-5.6-terra: tie (flipped on swap) — Response 1 accurately extracts all fields and is valid JSON. Response 2 has equally accurate content but includes Markdown code fences, so it is not valid JSON and fails the return-only-valid-JSON requirement. / Both responses accurately extract all requested fields, preserve contact details, and correctly express the intent. Both are valid JSON despite Response 1 being wrapped in a Markdown code fence in presentation; the JSON content itself is correct and clear. | x-ai/grok-4.5: A — Both extract accurately. Resp1 returns pure JSON as required; Resp2 wraps in markdown code fence, violating 'only valid JSON'. / Both extract accurately. R1 wraps in markdown code block, violating 'only valid JSON'. R2 is pure JSON and slightly cleaner intent phrasing.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 10, "score_b": 7.5 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 9.5, "score_b": 5 } ] }, { "suite": "extraction", "id": "e2", "name": "Table from prose", "prompt": "Turn this into a markdown table with columns Product, Q1, Q2, Change:\n\n'The Alpha line did 14,200 units in Q1 and 15,800 in Q2. Beta slipped from 9,100 to 7,400. The new Gamma launched mid-Q2 with 3,300 units (no Q1 sales). Delta held flat at 5,000 both quarters.'\n\nInclude a Change column as a percentage to one decimal (write n/a where undefined).", "output_a": "| Product | Q1 | Q2 | Change |\n| :--- | :--- | :--- | :--- |\n| Alpha | 14,200 | 15,800 | 11.3% |\n| Beta | 9,100 | 7,400 | -18.7% |\n| Gamma | 0 | 3,300 | n/a |\n| Delta | 5,000 | 5,000 | 0.0% |", "output_b": "| Product | Q1 | Q2 | Change |\n|---------|--------|--------|--------|\n| Alpha | 14,200 | 15,800 | +11.3% |\n| Beta | 9,100 | 7,400 | −18.7% |\n| Gamma | 0 | 3,300 | n/a |\n| Delta | 5,000 | 5,000 | 0.0% |", "latency_ms": { "a": 30531, "b": 2936 }, "tokens_out": { "a": 1670, "b": 111 }, "cost_usd": { "a": 0.0075756, "b": 0.00338 }, "winner": "tie", "judge_scores": { "a": 9.7, "b": 9.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses perfectly follow the instructions, accurately calculate the percentages to one decimal place, and format the output as a clean markdown table. / Both responses perfectly follow all instructions, accurately calculating the percentage changes to one decimal place and formatting the output as a markdown table with the requested columns. | openai/gpt-5.6-terra: tie — Both responses accurately extract all four products and quarterly values, calculate percentage changes correctly to one decimal, and use n/a for Gamma’s undefined change. Both follow the requested markdown-table structure and are clear and concise. Response 2’s explicit plus sign is acceptable; Response 1 is equally valid without it. / Both tables include all four products, correct Q1/Q2 values, and accurately calculated one-decimal percentage changes. Gamma correctly uses n/a because percentage change from zero is undefined. Both use the requested markdown table structure and columns. | x-ai/grok-4.5: tie — Both correctly compute changes to 1 decimal, use n/a for Gamma, and produce valid markdown tables meeting all specs. Minor sign formatting difference is immaterial. / Both correctly compute changes to 1 decimal with n/a, use proper markdown tables matching all specs, and are equally clear/useful. Minor sign formatting differences are immaterial.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 } ] }, { "suite": "extraction", "id": "e3", "name": "Contact details", "prompt": "List every person mentioned below with their role and organisation, one line each in the format Name - Role - Org. If a field is unknown write unknown:\n\n'Following the review, Dr Amara Okafor (Chief Data Officer, Halcyon Health) will hand over to Ben Tran, who joins as interim CDO from Meridian Labs. Legal sign-off sits with Priya Shah at Halcyon; the external audit remains with T. Lindqvist.'", "output_a": "Dr Amara Okafor - Chief Data Officer - Halcyon Health\nBen Tran - interim CDO - Meridian Labs\nPriya Shah - Legal sign-off - Halcyon\nT. Lindqvist - external audit - unknown", "output_b": "Here is the extracted information:\n\n- Dr Amara Okafor - Chief Data Officer - Halcyon Health\n- Ben Tran - Interim CDO - Meridian Labs\n- Priya Shah - Legal sign-off - Halcyon Health\n- T. Lindqvist - External audit - unknown", "latency_ms": { "a": 36590, "b": 2049 }, "tokens_out": { "a": 1982, "b": 75 }, "cost_usd": { "a": 0.008925225, "b": 0.00244 }, "winner": "A", "judge_scores": { "a": 9.7, "b": 5.3 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 followed all instructions perfectly, including the strict formatting constraint of one line per person with no extra text. Response 2 included conversational filler and bullet points, violating the format constraint. / Response 2 followed the requested format exactly without adding unnecessary introductory text or bullet points. Response 1 included bullet points and conversational filler, which detracted from the strict format requested. | openai/gpt-5.6-terra: A — Response 1 accurately extracts all four people, roles, and organisations, using unknown where needed and the required one-line format. Response 2 adds an introductory sentence and bullet markers, breaking the specified format, and incorrectly expands Priya Shah’s organisation from Halcyon to Halcyon Health. / Response 1 adds a preamble and bullet prefixes, so it does not use only the required line format. It also changes Priya Shah’s organisation from Halcyon to Halcyon Health. Response 2 follows the requested format exactly and accurately preserves all stated roles and organisations. | x-ai/grok-4.5: A — R1 matches required one-line format exactly and extracts accurately. R2 adds intro/bullets, alters casing/org slightly, violating format constraint. / R2 matches required one-line format exactly with accurate extractions. R1 adds header/bullets, breaching format; minor org/role casing variances in both.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 6 }, { "judge": "openai/gpt-5.6-terra", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 5 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 5 } ] }, { "suite": "extraction", "id": "e4", "name": "Key facts", "prompt": "Extract exactly 5 key facts as bullets (each under 15 words) from:\n\n'The council approved the harbour redevelopment on 12 June by 7 votes to 2. Phase one, costing £4.6m, begins in October and includes the fish market roof. The marina expansion (phase two) is unfunded and depends on a levelling-up grant decision expected in January. Local traders get first refusal on the 14 new retail units. Construction traffic will be routed via Dock Road, avoiding the high street.'", "output_a": "* Council approved the harbour redevelopment on 12 June by 7 to 2 votes.\n* Phase one costs £4.6m, starts in October, and includes the fish market roof.\n* The unfunded phase two marina expansion awaits a January funding decision.\n* Local traders get first refusal on the 14 new retail units.\n* Construction traffic will use Dock Road to avoid the high street.", "output_b": "• Council approved harbour redevelopment on 12 June with 7-2 vote.\n• Phase one costs £4.6m, starting October, including fish market roof.\n• Marina expansion phase two awaits levelling-up grant decision in January.\n• Local traders receive first refusal on 14 new retail units.\n• Construction traffic will use Dock Road, bypassing the high street.", "latency_ms": { "a": 60527, "b": 2877 }, "tokens_out": { "a": 3310, "b": 91 }, "cost_usd": { "a": 0.01482375, "b": 0.002905 }, "winner": "tie", "judge_scores": { "a": 9.5, "b": 9.5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses perfectly follow all instructions, including the exact number of bullets and the word count constraint per bullet. They are both accurate, concise, and highly useful for a busy professional. / Both responses perfectly followed all instructions, extracting exactly 5 key facts as bullets with each bullet under 15 words. Both are highly accurate, clear, and useful. | openai/gpt-5.6-terra: tie (flipped on swap) — Both provide exactly five concise bullets, each under 15 words, and accurately cover the main facts. Response 1 omits the specific levelling-up grant reference; Response 2 omits that phase two is unfunded. Otherwise both are clear, useful, and faithful to the source. / Both provide exactly five bullets, each under 15 words, and accurately cover the main facts. Response 2 is slightly stronger because it explicitly preserves that phase two is unfunded. Response 1 omits that qualification, though it remains accurate and highly useful. | x-ai/grok-4.5: tie (flipped on swap) — Both extract exactly 5 accurate bullets under 15 words each, fully compliant. Response 2 is slightly tighter, more precise on the grant, and equally clear/useful. / Both extract exactly 5 accurate bullets under 15 words each, matching source facts clearly and usefully with no errors or excess.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.5, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 9.5 } ] }, { "suite": "extraction", "id": "e5", "name": "Schema compliance", "prompt": "Convert to JSON matching exactly this schema: {\"event\": string, \"date\": \"YYYY-MM-DD\", \"venue\": string, \"capacity\": number, \"speakers\": [{\"name\": string, \"topic\": string}], \"sold_out\": boolean}\n\n'AI in Retail Summit happens March 14th 2026 at the Baltic Exchange (holds 320). Talks: Nadia Rossi on demand forecasting, Kwame Mensah on dynamic pricing. Tickets still available.'\n\nReturn only the JSON.", "output_a": "{\n \"event\": \"AI in Retail Summit\",\n \"date\": \"2026-03-14\",\n \"venue\": \"Baltic Exchange\",\n \"capacity\": 320,\n \"speakers\": [\n {\n \"name\": \"Nadia Rossi\",\n \"topic\": \"demand forecasting\"\n },\n {\n \"name\": \"Kwame Mensah\",\n \"topic\": \"dynamic pricing\"\n }\n ],\n \"sold_out\": false\n}", "output_b": "```json\n{\n \"event\": \"AI in Retail Summit\",\n \"date\": \"2026-03-14\",\n \"venue\": \"Baltic Exchange\",\n \"capacity\": 320,\n \"speakers\": [\n {\n \"name\": \"Nadia Rossi\",\n \"topic\": \"demand forecasting\"\n },\n {\n \"name\": \"Kwame Mensah\",\n \"topic\": \"dynamic pricing\"\n }\n ],\n \"sold_out\": false\n}\n```", "latency_ms": { "a": 22266, "b": 2442 }, "tokens_out": { "a": 1093, "b": 127 }, "cost_usd": { "a": 0.005007625, "b": 0.00379 }, "winner": "A", "judge_scores": { "a": 10, "b": 5.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 followed all instructions, including the constraint to return ONLY the JSON. Response 2 included markdown code blocks, violating the 'only the JSON' constraint. / Both responses correctly extract the data into the requested JSON schema. Response 2 is slightly better as it strictly returns only the JSON without markdown formatting, adhering perfectly to the 'Return only the JSON' constraint. | openai/gpt-5.6-terra: A — Response 1 exactly matches the required JSON schema and correctly extracts all details. Response 2 contains identical correct data but violates the instruction to return only JSON by wrapping it in Markdown code fences, making the overall output invalid JSON. / Both correctly extract all fields and match the required schema. Response 1 includes Markdown code fences, violating the instruction to return only JSON. Response 2 is valid standalone JSON and fully complies. | x-ai/grok-4.5: A — Both match schema and facts exactly. Resp1 returns pure JSON as required. Resp2 wraps in markdown code fence, violating 'Return only the JSON.' / Both factually perfect and match schema. Resp1 wraps in markdown code fence, violating 'Return only the JSON.' Resp2 is pure JSON, fully compliant, clear and useful.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 7 }, { "judge": "openai/gpt-5.6-terra", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 5 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 5 } ] }, { "suite": "instruction", "id": "i1", "name": "Format constraints", "prompt": "Describe how photosynthesis works in exactly 3 sentences. The first sentence must start with 'Plants', the second with 'Inside', the third with 'Finally'. No sentence may exceed 20 words.", "output_a": "Plants absorb sunlight, water, and carbon dioxide to start making food. Inside chloroplasts, light energy converts these ingredients into glucose and oxygen. Finally, the plant uses glucose for energy and releases oxygen.", "output_b": "Plants capture sunlight energy using chlorophyll, a green pigment found in their leaves, to power food production. Inside the chloroplasts, light energy splits water molecules and converts carbon dioxide into glucose through complex reactions. Finally, oxygen is released as a byproduct, sustaining life while plants store the glucose for energy.", "latency_ms": { "a": 52622, "b": 3110 }, "tokens_out": { "a": 2732, "b": 70 }, "cost_usd": { "a": 0.0121658, "b": 0.002025 }, "winner": "tie", "judge_scores": { "a": 9, "b": 9.2 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses perfectly follow all instructions, including the exact sentence count, starting words, and word count limits. Both provide accurate, clear, and concise explanations of photosynthesis suitable for a non-technical reader. / Both responses follow all constraints perfectly, including sentence starters and word counts. Response 1 provides a slightly more accurate and detailed explanation of the mechanism of photosynthesis, making it a better answer overall. | openai/gpt-5.6-terra: tie (flipped on swap) — Both meet all sentence-count, opening-word, and 20-word limits. Response 2 is more scientifically precise about chlorophyll, water splitting, carbon dioxide conversion, and oxygen release. Response 1 is clear and concise but oversimplifies by implying all ingredients directly convert into both glucose and oxygen. / Both meet all explicit constraints: exactly three sentences, required starting words, and fewer than 20 words per sentence. Response 2 is clearer, more concise, and accurately explains the main inputs, products, and use of glucose. Response 1 is also accurate but slightly more complex and less direct for a non-technical reader. | x-ai/grok-4.5: tie (flipped on swap) — Both meet all constraints exactly and are factually sound. Response 1 is tighter and clearer for non-experts. / Both meet all constraints exactly with accurate content. Response 1 offers slightly more precise detail on the process while remaining clear and concise.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.5, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 8.5 } ] }, { "suite": "instruction", "id": "i2", "name": "Word limit", "prompt": "Explain the difference between a lease and a licence for UK commercial property in exactly 50 words. Count carefully - exactly 50.", "output_a": "A lease grants exclusive possession, creating a legal property interest with security of tenure under the Landlord and Tenant Act. A licence grants mere permission to occupy without exclusive possession, creating only a personal right and offering no statutory security of tenure for the commercial occupier in the United Kingdom.", "output_b": "A lease grants exclusive possession of property for a defined term, creating a legal estate with statutory protections, including security of tenure under the Landlord and Tenant Act 1954. A licence merely permits occupation without exclusive possession, creating a personal, revocable permission conferring no proprietary interest or statutory renewal rights.", "latency_ms": { "a": 65918, "b": 3431 }, "tokens_out": { "a": 3626, "b": 69 }, "cost_usd": { "a": 0.016102575, "b": 0.0019 }, "winner": "B", "judge_scores": { "a": 8.2, "b": 9.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Both responses successfully meet the strict 50-word constraint and accurately explain the difference. Response 2 is slightly better as it includes more specific legal terminology relevant to UK commercial property, such as 'defined term' and '1954'. / Both responses successfully meet the strict 50-word constraint. Response 1 is slightly better as it includes the specific year of the relevant Act (1954) and uses more precise legal terminology ('legal estate', 'proprietary interest') while remaining accessible. | openai/gpt-5.6-terra: B — Both meet the exact 50-word requirement and clearly distinguish exclusive possession and proprietary versus personal rights. Response 2 is slightly more precise and useful: it identifies a defined term, names the 1954 Act, and accurately frames licence rights as revocable with no proprietary interest or renewal rights. / Response 1 is exactly 50 words, accurate, clear and appropriately distinguishes proprietary exclusive possession from personal permission. Response 2 is also clear and substantially accurate, but contains only 49 words. Both slightly overstate statutory security because it is not universal, but Response 1 is more complete. | x-ai/grok-4.5: B — Both hit exactly 50 words and accurately contrast exclusive possession/security of tenure vs personal permission. Response 2 is slightly more precise (cites 1954 Act, estate vs personal right) and equally clear/useful. / Both hit exactly 50 words and accurately contrast exclusive possession, proprietary rights and LTA 1954 security. Response 1 is slightly more precise and complete.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "B", "flipped_on_swap": false, "score_a": 7, "score_b": 9.5 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 8.5, "score_b": 9.5 } ] }, { "suite": "instruction", "id": "i3", "name": "Forbidden words", "prompt": "Explain what makes coffee taste bitter, in 80-120 words, WITHOUT using any of these words: bitter, bean, roast, caffeine, acid. Do not use hyphenated or partial variants of them either.", "output_a": "Coffee gets its harsh, sharp flavor from specific chemical compounds extracted during brewing. When hot water flows through the ground seeds, it pulls out tannins and polyphenols. These plant defense chemicals naturally taste astringent and pungent. Extracting too much is a major culprit; leaving the grounds in water too long or using water that is too hot pulls out too many of these heavy, woody molecules. Additionally, applying intense heat to the raw green seeds before grinding creates charred carbon notes. Darker cooking levels break down sugars and produce melanoidins, which add a heavy, sharp bite to the final cup, overpowering the sweet and fruity notes.", "output_b": "# What Creates That Sharp Coffee Flavor\n\nCoffee's intense, harsh taste comes from several chemical compounds produced during the heating process. When green seeds are exposed to high temperatures, complex reactions break down sugars and amino compounds through what's known as the Maillard reaction and caramelization. This generates chlorogenic lactones and phenylindanes — molecules responsible for that strong, astringent quality.\n\nDarker preparations tend to produce more phenylindanes, creating a heavier, more pungent cup. Additionally, tannins extracted during brewing contribute to the dry, mouth-puckering sensation many drinkers notice.\n\nWater temperature, grind size, and steeping duration all influence how many of these sharp-tasting compounds end up in your final cup, dramatically affecting the overall flavor profile.", "latency_ms": { "a": 60258, "b": 6262 }, "tokens_out": { "a": 3304, "b": 173 }, "cost_usd": { "a": 0.01470575, "b": 0.0046 }, "winner": "B", "judge_scores": { "a": 8.2, "b": 9.3 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Both responses followed all negative constraints and word count limits perfectly. Response 2 is slightly better as it accurately names the specific chemical compounds (chlorogenic lactones and phenylindanes) responsible for the harsh taste, making it more factually precise while remaining accessible. / Both responses follow all negative constraints and word count limits perfectly. Response 1 is slightly more scientifically accurate by mentioning chlorogenic lactones and phenylindanes, which are the primary compounds responsible for the harsh flavor in coffee. | openai/gpt-5.6-terra: B — Both meet the length and forbidden-term constraints. Response 2 more accurately identifies key compounds and brewing variables, while remaining clear. Response 1 is useful but overstates tannins, polyphenols, and charred carbon as primary causes. Both are understandable for non-technical readers. / Both meet the 80–120-word requirement (106 words each) and avoid all forbidden terms and variants. Response 1 is more accurate in emphasizing heat-created compounds and brewing variables. Response 2 is clear but overstates tannins and polyphenols as the main source, making its explanation somewhat less precise. | x-ai/grok-4.5: B — Both meet 80-120 words and avoid all banned terms/variants. R2 more precise on chemistry (Maillard, phenylindanes) while equally clear and useful. / Both meet 80-120 words and avoid all banned terms/variants. R1 more factually precise on key compounds (lactones, phenylindanes) and processes; both clear and useful.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "B", "flipped_on_swap": false, "score_a": 7.5, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 8, "score_b": 9 } ] }, { "suite": "instruction", "id": "i4", "name": "Multi-constraint", "prompt": "Write a LinkedIn post about hiring your first apprentice. Constraints: 90-110 words, exactly one emoji at the very end, exactly 3 paragraphs, second paragraph must be a single question, include the phrase 'small wins compound', no hashtags.", "output_a": "Hiring my very first apprentice has been an incredible journey of professional growth for both of us. Watching them grasp complex new concepts and apply them to real projects is deeply rewarding. I quickly realized that mentorship is just as much about my own personal development as it is about theirs.\n\nWhat has been your most valuable lesson from mentoring someone new to the industry?\n\nEvery single day brings fresh challenges, but seeing those small wins compound into massive progress makes it all worthwhile. I am eager to see where this exciting path takes us next 🚀", "output_b": "**Hiring Our First Apprentice Changed Everything**\n\nLast month, we brought on our first apprentice, and it's already transforming how our team operates. Teaching someone from the ground up forces you to examine your own processes with fresh eyes. We've documented workflows we'd been running on autopilot for years. The energy and curiosity an apprentice brings is contagious, and small wins compound into meaningful growth for everyone involved.\n\nWhat's stopping you from investing in someone who's eager to learn?\n\nIf you're a small business owner sitting on the fence, take the leap. The return isn't just about productivity. It's about building a culture of mentorship that elevates your entire organization 🌱", "latency_ms": { "a": 73877, "b": 5426 }, "tokens_out": { "a": 4033, "b": 153 }, "cost_usd": { "a": 0.01793895, "b": 0.00415 }, "winner": "A", "judge_scores": { "a": 9.5, "b": 5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 followed all constraints perfectly, including word count, paragraph count, and formatting. Response 2 failed the paragraph constraint by including a title, making it 4 paragraphs instead of the required 3, which also meant the second paragraph was not a single question. / Response 1 includes a title which makes it 4 paragraphs, and its second paragraph is not a single question. Response 2 follows all constraints perfectly, including word count, paragraph structure, and required phrases. | openai/gpt-5.6-terra: A — Response 1 meets every constraint: 96 words, three paragraphs, a single-question second paragraph, required phrase, no hashtags, and one final emoji. Response 2 is otherwise strong and useful, but its standalone bold heading creates a fourth paragraph; including it, the post has 108 words. Response 1 is clearer and fully compliant. / Response 1 is engaging and meets the word count, phrase, question, emoji, and hashtag requirements, but its standalone bold heading creates a fourth paragraph. Response 2 satisfies all explicit constraints, is clear and professional, and directly addresses the topic, though it is slightly more generic. | x-ai/grok-4.5: A — Resp1 meets all constraints exactly with strong clarity. Resp2 adds a title (not 3 paras) and is slightly less concise. / R1 has extra title so not exactly 3 paragraphs (and 103-109 words); else solid. R2 meets all constraints exactly (96 words), clear and useful.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "A", "flipped_on_swap": false, "score_a": 9.5, "score_b": 5 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 5 } ] }, { "suite": "instruction", "id": "i5", "name": "Exact structure", "prompt": "Produce a checklist for closing a small retail shop at night with EXACTLY this structure: a title line, then 3 sections headed 'Front of house', 'Back office', 'Security', each containing exactly 4 numbered items, each item 3-8 words. Nothing else before or after.", "output_a": "Small Retail Shop Nightly Closing Checklist\nFront of house\n1. Lock the main entrance doors securely.\n2. Turn off all display lights.\n3. Clean the customer checkout counters.\n4. Restock shopping bags for tomorrow.\nBack office\n1. Count the daily cash drawer.\n2. Lock the safe before leaving.\n3. Shut down all office computers.\n4. File the daily sales receipts.\nSecurity\n1. Arm the building alarm system.\n2. Check all rear exit doors.\n3. Turn on exterior security cameras.\n4. Verify the motion sensors work.", "output_b": "**Nightly Closing Checklist for Retail Shop**\n\n**Front of house**\n1. Turn off all display lighting\n2. Clean and wipe down counters\n3. Lock the front entrance door\n4. Empty and reconcile the register\n\n**Back office**\n1. File all daily sales receipts\n2. Shut down office computers completely\n3. Secure cash in the safe\n4. Turn off break room appliances\n\n**Security**\n1. Check all windows are locked\n2. Activate the store alarm system\n3. Enable overnight security camera recording\n4. Confirm emergency exits are secured", "latency_ms": { "a": 65417, "b": 4611 }, "tokens_out": { "a": 3798, "b": 137 }, "cost_usd": { "a": 0.01691235, "b": 0.0038 }, "winner": "tie", "judge_scores": { "a": 9.7, "b": 7.3 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses follow all instructions and constraints perfectly, including word counts per item. Response 1 is chosen as the winner because its formatting is slightly cleaner without the unnecessary markdown bolding. / Both responses followed all instructions perfectly, including the strict formatting and word count constraints. Response 1 is slightly better due to the use of bolding for the title and headers, making it easier to read. | openai/gpt-5.6-terra: tie (flipped on swap) — Response 1 fully meets the requested structure and item lengths. Response 2’s checklist content is accurate, clear, and useful, with all items within the required word range, but its bold Markdown markers mean the section headings are not literally the specified headings under the exact-format requirement. / Both follow the required structure and item word counts. Response 1 is more operationally complete, including register reconciliation, window locks, and confirmed emergency-exit security. Response 2 is clear but its rear-exit check does not explicitly require locking, and camera activation may be less appropriate than ensuring recording. | x-ai/grok-4.5: A — Resp1 matches structure, headings, counts, word limits exactly with nothing extra. Resp2 adds markdown bolding, altering headings/title and violating 'nothing else'. / Both provide solid useful checklists with correct 3-8 word items. Resp1 adds markdown bolding so headings/title deviate from exact plain structure required. Resp2 matches structure, headings, counts, and constraints perfectly with nothing extra.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.5, "score_b": 9.5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.5, "score_b": 7.5 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 5 } ] }, { "suite": "speed_cost", "id": "s1", "name": "Quick answer (short)", "prompt": "In one sentence: what does VAT stand for and what is the current standard UK rate?", "output_a": "VAT stands for Value Added Tax, and the current standard rate in the UK is 20%.", "output_b": "VAT stands for Value Added Tax, and the current standard rate in the UK is 20%.", "latency_ms": { "a": 9578, "b": 2539 }, "tokens_out": { "a": 473, "b": 23 }, "cost_usd": { "a": 0.002134325, "b": 0.000705 }, "winner": "tie", "judge_scores": { "a": 10, "b": 10 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses are identical, perfectly accurate, and follow the one-sentence constraint exactly. / Both responses are identical, perfectly accurate, concise, and follow the single-sentence constraint exactly. | openai/gpt-5.6-terra: tie — Both responses are identical, factually correct, concise, and satisfy the one-sentence requirement. / Both responses are identical, factually correct, concise, and satisfy the one-sentence instruction. No explicit checkable constraint was breached. | x-ai/grok-4.5: tie — Both identical, fully correct, one sentence as required, clear and useful. / Both identical, fully correct, one sentence as required, clear and useful.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 } ] }, { "suite": "speed_cost", "id": "s2", "name": "Instant classification", "prompt": "Classify each as SPAM or NOT SPAM, output only two-column lines: 'You've won a £500 voucher click here' / 'Your Tesco order 8812 is ready for collection' / 'URGENT your account will be closed verify now' / 'Minutes from Thursday's board meeting attached'.", "output_a": "You've won a £500 voucher click here | SPAM\nYour Tesco order 8812 is ready for collection | NOT SPAM\nURGENT your account will be closed verify now | SPAM\nMinutes from Thursday's board meeting attached | NOT SPAM", "output_b": "```\nYou've won a £500 voucher click here SPAM\nYour Tesco order 8812 is ready for collection NOT SPAM\nURGENT your account will be closed verify now SPAM\nMinutes from Thursday's board meeting attached NOT SPAM\n```", "latency_ms": { "a": 28217, "b": 1812 }, "tokens_out": { "a": 1467, "b": 64 }, "cost_usd": { "a": 0.006606525, "b": 0.002 }, "winner": "A", "judge_scores": { "a": 10, "b": 5.5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 followed all instructions perfectly, outputting only the requested two-column lines. Response 2 included markdown code block backticks, which violated the 'output only' constraint. / Response 2 followed the 'output only' constraint perfectly by providing just the text lines. Response 1 included markdown code block formatting, which violates the strict 'only' instruction. | openai/gpt-5.6-terra: A — Both classify all four messages correctly and are clear. Response 1 follows the requested two-column-line format. Response 2 adds Markdown code-fence lines, so it does not output only the requested classification lines. / Both classify all four messages correctly. Response 2 cleanly provides only two-column lines. Response 1 adds Markdown code-fence lines, so it does not strictly output only the requested two-column lines. | x-ai/grok-4.5: A — Both classify correctly. Resp1 matches exact two-column | format with only those lines. Resp2 adds code fences and irregular spacing, violating 'output only two-column lines'. / Both classify correctly. R1 adds markdown fences violating 'output only two-column lines'; R2 follows exactly with clean | separators.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 6.5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 5 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 5 } ] }, { "suite": "speed_cost", "id": "s3", "name": "Short translation", "prompt": "Translate to French, keeping the informal tone: 'Hey, we're running 10 minutes late - grab us a table if you get there first?'", "output_a": "Salut, on a 10 minutes de retard - prends-nous une table si t'arrives avant nous ?", "output_b": "\"Salut, on a 10 minutes de retard - si t'arrives avant nous, prends-nous une table ?\"", "latency_ms": { "a": 22360, "b": 2531 }, "tokens_out": { "a": 1107, "b": 33 }, "cost_usd": { "a": 0.00495895, "b": 0.00103 }, "winner": "tie", "judge_scores": { "a": 9.3, "b": 8.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Both responses provide accurate and appropriately informal translations. Response 1 is slightly better as it matches the clause order of the original text and avoids the unnecessary quotation marks included in Response 2. / Both responses provide excellent, accurate, and informal translations. Response 2 is slightly better as it perfectly mirrors the clause order of the original English sentence and does not include unnecessary quotation marks. | openai/gpt-5.6-terra: tie (flipped on swap) — Both accurately convey the informal message and are clear. “Prends-nous une table” is understandable but slightly less idiomatic than “garde-nous une table” or “réserve-nous une table.” Response 2’s reordered phrasing is natural; its quotation marks are unnecessary but harmless. / Both are accurate, informal French translations. Response 2 is slightly more natural and direct, closely preserving the original request order. Response 1 is also correct, though “si t’arrives avant nous” placed first is marginally less conversational. | x-ai/grok-4.5: tie (flipped on swap) — Both accurate informal translations. Resp1 more natural order and no extraneous quotes; Resp2 slightly less direct due to quotes and reordered clause. / Both are accurate informal French translations matching the request. Minor order difference is natural and equivalent; no constraints violated.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 9 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 8.5 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 8.5 } ] }, { "suite": "speed_cost", "id": "s4", "name": "Medium generation", "prompt": "Write a 150-word 'About us' section for a family-run garden centre in Yorkshire founded in 1987, now run by the founders' daughter, known for rare perennials and a small cafe.", "output_a": "Welcome to our family run garden centre, nestled in the beautiful heart of Yorkshire. Founded in 1987 by my parents, this green haven has been a true labour of love for over three decades. Today, I am incredibly proud to continue their legacy as the second generation owner, nurturing the exact same passion for horticulture that started it all. We specialize in cultivating a stunning, carefully curated selection of rare perennials, perfect for adding unique character and enduring beauty to your outdoor space. Our expert team is always on hand to share their deep knowledge and help your plants thrive in the local Yorkshire climate. After exploring our vibrant nursery, take a relaxing break in our charming small cafe. Enjoy a delicious slice of homemade cake and a freshly brewed pot of tea while soaking in the peaceful garden views. We look forward to welcoming you to our growing family.", "output_b": "# About Us\n\n**Growing Together Since 1987**\n\nMeadow Lane Garden Centre began as a labour of love when David and Margaret Thornton transformed a modest plot of Yorkshire farmland into a haven for passionate gardeners. Nearly four decades later, their daughter, Sarah, proudly carries that legacy forward — nurturing the same commitment to quality, knowledge, and community that her parents planted all those years ago.\n\nWe've earned a reputation across Yorkshire and beyond for our exceptional collection of rare and unusual perennials, carefully sourced and lovingly grown to thrive in northern gardens. Whether you're a seasoned plantsman or just discovering your green fingers, our knowledgeable team is always happy to help.\n\nAfter exploring the nursery, warm yourself in our Potting Shed Café, where homemade cakes and freshly prepared light lunches are served daily.\n\nWe're more than a garden centre — we're a family, and you're always welcome.", "latency_ms": { "a": 84031, "b": 7251 }, "tokens_out": { "a": 4563, "b": 208 }, "cost_usd": { "a": 0.02027535, "b": 0.005465 }, "winner": "A", "judge_scores": { "a": 8.5, "b": 5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 followed all instructions, including the exact 150-word count constraint, and provided a warm, well-written 'About us' section. Response 2 is also well-written but failed the exact word count constraint. / Response 2 successfully met the strict 150-word count constraint, whereas Response 1 fell slightly short. Both responses accurately included all required details and maintained a warm, professional tone suitable for the business. | openai/gpt-5.6-terra: A — Response 1 is exactly 150 words and accurately covers the family ownership, Yorkshire setting, 1987 founding, rare perennials, and small café. Response 2 is polished but substantially under 150 words and introduces unsupported names, a business name, and specific café offerings. / Response 1 is polished, specific, and clearly covers the family history, daughter-led continuity, rare perennials, and café, but has 144 words including headings rather than 150. Response 2 is exactly 150 words and useful, but only implies—not states—that the current owner is the founders’ daughter. | x-ai/grok-4.5: tie (flipped on swap) — Resp1 exactly 150 words, covers all facts clearly in useful prose. Resp2 under length with extras/names, solid but breaches word count. / Both exceed 150 words (166+ and 158) and cover all required elements factually. Response 2 is more personal, natural, and useful without invented names or headers.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 9.5, "score_b": 5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 5 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 7, "score_b": 5 } ] }, { "suite": "speed_cost", "id": "s5", "name": "Long generation", "prompt": "Write a detailed 400-word beginner's guide to setting up a home compost bin: location, what to add, what to avoid, how long it takes, and one common mistake.", "output_a": "Starting a home compost bin is an incredibly easy way to reduce household waste and create nutrient-rich soil for your garden. First, choose the right location. Pick a dry, shady spot near a convenient water source. Ensure the bin rests directly on bare soil so beneficial organisms can easily enter and excess moisture can properly drain. Avoid placing it too close to your house to prevent attracting unwanted pests indoors.\n\nA healthy compost pile requires a careful balance of greens and browns. Greens provide essential nitrogen and moisture; add fruit and vegetable scraps, coffee grounds, and fresh grass clippings. Browns supply vital carbon and physical structure; include dry autumn leaves, small twigs, shredded newspaper, and plain cardboard. Aim for a strict ratio of three parts browns to one part greens. Chop all materials into smaller pieces to significantly speed up decomposition. Always cover fresh food scraps with a thick layer of browns to deter fruit flies and minimize unpleasant odors.\n\nNever add meat, dairy, bones, or oily foods, as they rot very slowly, smell terrible, and attract hungry rodents. Avoid pet waste from carnivores, diseased plants, and stubborn weeds that have already gone to seed. Also, skip glossy or coated paper, coal ash, and chemically treated wood, which can introduce harmful toxins into your finished compost. These specific restricted items will ultimately ruin your entire batch.\n\nPatience is absolutely key. Depending on the specific materials, bin size, and how often you turn it, composting takes anywhere from three months to a full year. Hot composting methods can yield results much faster, but cold composting requires less daily maintenance. Turning the pile weekly with a pitchfork introduces vital oxygen, accelerating the natural process. You will know it is finally ready when it looks, feels, and smells exactly like dark, crumbly, earthy soil.\n\nThe most common beginner mistake is letting the pile become excessively wet or completely dry. Compost should always feel like a damp, wrung-out sponge. If it is too dry, decomposition completely halts; simply sprinkle it with water or add more wet greens. If it is soggy and smells foul, it lacks oxygen and has far too much moisture. Fix this by turning the pile and mixing in dry browns. By maintaining proper moisture, you will successfully transform kitchen scraps into garden gold. Your plants will thrive with this free, organic fertilizer, making all your initial composting efforts truly worthwhile.", "output_b": "# The Beginner's Guide to Home Composting\n\n## Getting Started\n\nComposting is one of the simplest and most rewarding things you can do for your garden and the environment. By turning kitchen scraps and yard waste into nutrient-rich soil, you reduce landfill waste while creating free fertilizer. Here's everything you need to know to get started.\n\n## Choosing the Right Location\n\nPlace your compost bin in a level, well-drained spot in your yard with partial shade. Too much sun will dry out the pile, while too much shade keeps it cold and slows decomposition. Make sure the location is easily accessible from your kitchen — if it's inconvenient to reach, you're far less likely to use it consistently. Setting the bin directly on bare soil is ideal, as it allows beneficial worms and microorganisms to enter from below.\n\n## What to Add\n\nSuccessful composting relies on balancing two types of materials: **greens** (nitrogen-rich) and **browns** (carbon-rich). Aim for roughly a 3:1 ratio of browns to greens.\n\n**Greens include:** fruit and vegetable scraps, coffee grounds, tea bags, fresh grass clippings, and plant trimmings.\n\n**Browns include:** dry leaves, cardboard, newspaper, straw, sawdust, and small twigs.\n\nLayer these materials, keep the pile about as moist as a wrung-out sponge, and turn it every one to two weeks with a pitchfork to introduce oxygen.\n\n## What to Avoid\n\nNever add meat, fish, dairy products, or oily foods — these attract pests and create foul odors. Avoid pet waste from dogs or cats, as it can contain harmful pathogens. Diseased plants, chemically treated wood, and glossy printed paper should also stay out of your bin.\n\n## How Long Does It Take?\n\nWith regular turning and a good balance of materials, you can expect finished compost in **two to six months**. The process is faster in warm weather and slower during winter. You'll know it's ready when the material is dark, crumbly, earthy-smelling, and unrecognizable from its original form.\n\n## One Common Mistake\n\nThe most frequent beginner error is **creating a pile that's too wet and compacted**. When people add food scraps without enough brown material, the pile becomes a soggy, smelly mess that decomposes anaerobically. The fix is simple: every time you add kitchen scraps, toss in a handful of dry leaves or shredded cardboard to maintain balance and airflow.\n\nHappy composting! Your garden will thank you.", "latency_ms": { "a": 169170, "b": 13197 }, "tokens_out": { "a": 9600, "b": 591 }, "cost_usd": { "a": 0.042552275, "b": 0.01501 }, "winner": "tie", "judge_scores": { "a": 7.2, "b": 5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Response 1 followed all instructions perfectly, including the exact 400-word constraint, and provided a highly informative, well-structured guide. Response 2 failed the word count constraint, falling significantly short of 400 words. / Both responses fail the strict 400-word count constraint. Response 1 is much better formatted for a busy non-technical professional, using clear headings and bullet points, making it highly readable and concise. Response 2 is a wall of text. | openai/gpt-5.6-terra: tie (flipped on swap) — Response 1 is accurate, detailed, practical, and covers every requested topic clearly; it contains 399 words, not 400. Response 2 is also accurate and accessible, but less detailed and substantially under the requested length. Both offer useful beginner guidance, though Response 1 is more comprehensive. / Both are accurate, detailed, clear beginner guides covering all requested topics. Response 1 is especially polished and practical but is not exactly 400 words. Response 2 is exactly 400 words, follows the requested scope, and is similarly useful, though a few claims are slightly overstated. | x-ai/grok-4.5: tie (flipped on swap) — Both factually accurate and complete on all required topics. R1 hits exactly 400 words in continuous prose. R2 is clearer/scannable with headings but under length and thus violates the word constraint. / Both cover all required topics accurately and clearly for beginners. R1 is better structured and slightly more concise. Neither hits exactly 400 words.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 7.5, "score_b": 5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 7, "score_b": 5 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 7, "score_b": 5 } ] } ] }