{ "battle": "grok-4-5-vs-claude-opus-4-6-value", "category": "Best-value API", "suite_version": "suite-2026-07", "contestants": { "A": { "name": "Grok 4.5", "model": "x-ai/grok-4.5" }, "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", "deepseek/deepseek-v4-pro" ], "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": "2026-08-10T12:11:42.983Z", "finished_at": "2026-08-10T12:23:39.257Z", "scoreline": { "Grok 4.5": 10, "Claude Opus 4.6": 5, "ties": 15 }, "total_cost_usd": 1.366, "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 bookkeeping for [Brewery Name]\n\nHi [Name],\n\nI’m [Your Name], owner of [Firm Name], a six-person accounting firm based here in [Town/City]. We work with local businesses on straightforward bookkeeping and accounts.\n\nI’d like to offer [Brewery Name] a free first month of bookkeeping so you can see how we work—no cost and no obligation.\n\nIf that sounds useful, just reply to this email and we’ll arrange the details.\n\nBest regards, \n[Your Name] \nOwner, [Firm Name] \n[Phone] | [Website]", "output_b": "**Subject: Happy to help with your books – first month on us**\n\nHi,\n\nI'm Sarah, 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 running a brewery means your time goes into making great beer, not reconciling invoices. We'd like to offer you a free month of bookkeeping – no strings, no contract. It's simply how we prefer to introduce ourselves.\n\nIf the fit works for both of us, we can talk about continuing. If not, you walk away with a tidy set of books.\n\nWould you be free for a 15-minute call this week or next?\n\nBest,\nSarah Mitchell\nClearpath Accounting\n[phone] | [email]", "latency_ms": { "a": 8399, "b": 6970 }, "tokens_out": { "a": 427, "b": 175 }, "cost_usd": { "a": 0.0028604, "b": 0.00468 }, "winner": "tie", "judge_scores": { "a": 8.5, "b": 9.2 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Both responses follow all instructions and constraints perfectly. Response 2 is slightly better as it includes a more engaging, tailored hook for a brewery, making it a more effective cold email. / Response 1 is much more engaging and tailored to a brewery, making it a better cold email. It meets all constraints, including the word count. Response 2 is overly reliant on placeholders and feels a bit generic. | openai/gpt-5.6-terra: tie (flipped on swap) — Both meet the 120-word limit, use a friendly professional tone, avoid obvious buzzwords, and include one clear call to action. Response 1 is more concise and direct; Response 2 is more tailored to a brewery and explains the offer’s value better. Neither has material factual or instruction-following issues. / Both meet the 120-word limit, tone, sender, offer, and single-CTA requirements. Response 2 is slightly stronger for a busy recipient: it is more concise, directly tailored with placeholders, and has a simple reply-based call to action. Response 1 is also strong but includes slightly more detail than necessary. | deepseek/deepseek-v4-pro: tie (flipped on swap) — Both emails are accurate, follow all constraints (word count, no buzzwords, one clear CTA), and are friendly, professional, and useful. Response 2 is slightly more tailored but not decisively better. Tie. / Response 1 is personalized, engaging, and perfectly meets all constraints. Response 2 uses placeholders and is less compelling.", "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": 7.5, "score_b": 9 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.5, "score_b": 9 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 9.5 } ] }, { "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": "This 750ml stainless-steel insulated bottle supports your gym sessions with reliable temperature control—drinks stay cold 24 hours or hot 12 hours. Its leakproof seal keeps your bag dry during commutes or locker storage, while the slim base fits standard car cup holders for seamless transport. Durable construction handles daily drops and washes, delivering consistent performance from warm-ups to cool-downs without extra bulk.", "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 refreshment 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 for seamless transitions between sets and streets. Durable, sweat-free, and built to survive every rep.", "latency_ms": { "a": 9997, "b": 4739 }, "tokens_out": { "a": 606, "b": 115 }, "cost_usd": { "a": 0.0039484, "b": 0.003255 }, "winner": "A", "judge_scores": { "a": 9, "b": 8.8 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Both responses follow all instructions perfectly, including the strict word count constraint and avoiding cliches. Response 2 wins because its tone is slightly more engaging and tailored to the gym-goer audience. / Both responses follow all instructions and meet the word count constraints. Response 1 is slightly better as its tone is more engaging and specifically tailored to the gym-goer audience. | openai/gpt-5.6-terra: A — Both meet the 60–80 word requirement, target gym-goers, include all specified features, and avoid the prohibited cliché. Response 1 is more precise and professional, with fewer unsupported or exaggerated claims. Response 2 is engaging but adds unverified claims such as “sweat-free” and “built to survive every rep.” / Both meet the 60–80 word requirement, include all specified features, target gym-goers, and avoid the banned cliché. Response 2 is slightly more direct and professional. Response 1 is engaging but makes less-supported claims such as “sweat-free” and “built to survive every rep”; Response 2 similarly adds minor unsupported durability claims. | deepseek/deepseek-v4-pro: A — Response 1 is clearer and more polished, using gym-specific terms without awkward phrasing. Response 2 adds branding and slightly odd wording ('refreshment', 'tosses'), making it less smooth, though both meet constraints. / Response 2 is clearer and more natural, avoiding awkward phrasing like 'ice-cold refreshment' and 'it tosses'. Both meet constraints, but 2 is slightly more polished.", "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": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 8.5 }, { "judge": "deepseek/deepseek-v4-pro", "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": "- **Launch delay**: Slipped ~3 weeks due to supplier issue (per Marc); new date TBD \n- **Email campaign**: Sarah wants to proceed now; Tom recommends waiting for confirmed launch \n- **Budget**: $8K over, driven mainly by packaging redo \n- **Retail partners**: Decide whether to notify now or after new launch date is locked \n- **Action/next**: Hire warehouse temp before August rush; next meeting Tuesday", "output_b": "• **Launch delayed ~3 weeks** due to a supplier issue (details to be confirmed by Marc).\n• **Email campaign timing under debate:** Sarah recommends proceeding now; Tom advises holding until the new launch date is set — decision needed.\n• **Budget is $8K over**, primarily driven by the packaging redesign.\n• **Retail partner communication:** Team needs direction on whether to notify partners now or wait until the revised launch date is locked in.\n• **Urgent hire:** Warehouse temp must be brought on before the August rush. Next meeting is Tuesday.", "latency_ms": { "a": 5621, "b": 5174 }, "tokens_out": { "a": 318, "b": 126 }, "cost_usd": { "a": 0.0022964, "b": 0.003715 }, "winner": "A", "judge_scores": { "a": 10, "b": 9.2 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Both responses accurately summarize the notes into exactly 5 bullets. Response 1 is slightly more concise and punchy, making it better suited for a busy director to read in 20 seconds. / Both responses accurately summarize the notes into 5 bullets. Response 2 is slightly more concise, making it better suited for a director to read in under 20 seconds. | openai/gpt-5.6-terra: tie — Both provide exactly five clear, accurate bullets covering the delay, campaign disagreement, budget overrun, retail-partner decision, hiring need, and next meeting. Both are concise and director-ready. Response 2 adds slightly more explicit decision framing, while Response 1 is marginally terser; neither has a meaningful advantage. / Both accurately preserve all key points in exactly five clear bullets: launch delay, campaign decision, budget overrun, retail-partner communication, and warehouse hiring/next meeting. Both are concise, director-ready, and avoid unsupported claims; Response 1’s note that Marc’s details need confirmation is a reasonable clarification. | deepseek/deepseek-v4-pro: A — Response 1 is more concise and all facts are strictly accurate. Response 2 adds 'details to be confirmed by Marc' which is not in the notes, slightly distorting the point. Both follow the 5-bullet constraint. Response 1’s brevity is better for a 20-second read. / Response 1 added 'details to be confirmed by Marc', implying Marc hasn't yet reported, which is inaccurate. Response 2 correctly attributes to Marc with no extra implication. Both have 5 bullets and are clear, but the accuracy issue lowers Response 1.", "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": false, "score_a": 10, "score_b": 10 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 8.5 } ] }, { "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": "We’re truly sorry your order was delayed due to a system error on our side—that’s on us, and we take full responsibility. Your refund has already been processed and should arrive in 3–5 days. We’re unable to offer further compensation, but please let us know if you have any questions.", "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. Your refund has been processed and should arrive within 3–5 days. While we're unable to offer additional compensation at this time, please know that 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": 3865, "b": 4388 }, "tokens_out": { "a": 181, "b": 105 }, "cost_usd": { "a": 0.0014144, "b": 0.003025 }, "winner": "A", "judge_scores": { "a": 9.8, "b": 8.8 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Both responses follow all instructions and constraints, including the word count limit. Response 1 is slightly better as it is more concise while still achieving the desired warm tone and taking full responsibility. / Both responses follow all instructions and constraints perfectly. Response 2 is slightly better as it is more concise while maintaining a warm and responsible tone, making it ideal for a busy professional. | openai/gpt-5.6-terra: A — Both are warm, accountable, accurate, and under 90 words. Response 1 is more concise and preserves the original facts exactly. Response 2 is also strong, but adds “at this time,” which slightly softens the definitive statement that no further compensation can be offered. / Both are under 90 words, warm, and accept responsibility while preserving the core facts. Response 2 is more concise and closely retains the original position on compensation. Response 1’s “at this time” slightly softens the firm statement that no further compensation can be offered. | deepseek/deepseek-v4-pro: A — Response 1 is equally warm and responsible but more concise (51 vs 83 words), making it more useful for a busy professional. Both keep facts and are under 90 words. / Response 2 is more concise, equally warm, and keeps the same facts without added fluff, making it more useful to a busy professional.", "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": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 9 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "A", "flipped_on_swap": false, "score_a": 9.5, "score_b": 8.5 } ] }, { "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": "**Headline Options:**\n\n1. **Practical:** Use AI to Cut Restaurant Food Waste \n2. **Practical:** Simple AI Tools for Smarter Inventory \n3. **Curiosity-driven:** What If AI Predicted Your Spoilage? \n4. **Curiosity-driven:** The Hidden Way AI Saves Ingredients \n5. **With a number:** 5 AI Tips to Slash 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 Restaurants Waste Less Food**\n\n## Curiosity-Driven\n3. **Your Dumpster Knows What AI Could Have Saved**\n4. **What Smart Restaurants Know About Food Waste (You Don't)**\n\n## Number-Driven\n5. **5 Ways AI Helps Small Restaurants Eliminate Food Waste**", "latency_ms": { "a": 3810, "b": 4251 }, "tokens_out": { "a": 160, "b": 116 }, "cost_usd": { "a": 0.0012464, "b": 0.003195 }, "winner": "B", "judge_scores": { "a": 8.3, "b": 9.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Both responses follow all instructions and constraints perfectly. Response 2 is better because its headlines specifically mention 'small restaurants' as requested in the prompt, making them more relevant to the target audience. / Both responses follow all instructions and constraints perfectly. Response 1 is slightly better because its headlines explicitly mention 'small restaurants', making them more tailored to the specific topic requested. | openai/gpt-5.6-terra: B — Both provide five correctly mixed headlines, each within nine words. Response 2 more consistently targets small restaurants and food waste, with stronger, more specific headline phrasing. Response 1 is usable, but several options omit the small-restaurant context and are less directly tied to food waste. / Both provide exactly five options, meet the mix requirement, and keep every headline within nine words. Response 1 is more consistently targeted to small restaurants and food waste. Response 2 is concise and usable, but several headlines omit the small-restaurant context, making them less precisely aligned with the brief. | deepseek/deepseek-v4-pro: B — Both meet constraints. Response 2's curiosity-driven headlines (especially 'Your Dumpster Knows...') are more vivid and engaging for a busy professional, outweighing Response 1's slightly better concision. / Both meet all constraints. Response 1 headlines are more vivid and engaging (e.g., 'Your Dumpster Knows...'), making them slightly more useful for a blog post.", "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, "score_b": 9.5 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "B", "flipped_on_swap": false, "score_a": 8.5, "score_b": 9.5 } ] }, { "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": "```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 - 1] + sorted[mid]) / 2 : sorted[mid];\n}\n```\nCopies input to avoid mutation, numeric-sorts, and averages the two middle values for even length.", "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": 6615, "b": 5890 }, "tokens_out": { "a": 416, "b": 119 }, "cost_usd": { "a": 0.0028424, "b": 0.003415 }, "winner": "tie", "judge_scores": { "a": 10, "b": 10 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses correctly fix the mutation and even-length issues (as well as the implicit sorting bug), and both provide exactly the requested output format: the corrected function followed by a one-line explanation. / Both responses perfectly fix the mutation, sorting, and even-length logic issues. Both follow the constraint to provide only the code and a one-line explanation. They are functionally identical and equally clear. | openai/gpt-5.6-terra: tie — Both correctly avoid input mutation by copying, use numeric sorting, and compute the average of the two middle values for even-length arrays. Each provides only the corrected function and a one-line explanation. Response 2's equivalent conditional is slightly less direct, but not meaningfully less useful or clear. / Both correctly avoid mutating the input, use numeric sorting, and return the average of the two middle values for even-length arrays. Each provides only the corrected function and a one-line explanation. | deepseek/deepseek-v4-pro: tie — Both responses correctly fix mutation and even-length median, return only the function with a one-line explanation, and are clear and useful. There is no significant difference. / Both responses correctly fix mutation and median logic, follow the instruction to return only the function with a one-line explanation, and are clear and useful. No constraint violations.", "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": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 } ] }, { "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 \"\"\"Group invoices into batches whose amounts sum to at most max_total.\n\n Order is preserved. Any single invoice larger than max_total is placed\n in its own batch.\n \"\"\"\n batches = []\n current_batch = []\n current_sum = 0.0\n\n for inv in invoices:\n amount = inv['amount']\n\n if amount > max_total:\n # Flush any open batch, then emit the oversized invoice alone.\n if current_batch:\n batches.append(current_batch)\n current_batch = []\n current_sum = 0.0\n batches.append([inv])\n elif current_batch and current_sum + amount > max_total:\n # Current batch is full; start a new one with this invoice.\n batches.append(current_batch)\n current_batch = [inv]\n current_sum = amount\n else:\n current_batch.append(inv)\n current_sum += amount\n\n if current_batch:\n batches.append(current_batch)\n\n return batches\n\n\n# --- test cases ---\n\n# 1. Normal packing: first two fit, third starts a new batch\ninvoices1 = [\n {'id': 'A1', 'amount': 100.0},\n {'id': 'A2', 'amount': 50.0},\n {'id': 'A3', 'amount': 80.0},\n]\nassert chunk_invoices(invoices1, 150.0) == [\n [{'id': 'A1', 'amount': 100.0}, {'id': 'A2', 'amount': 50.0}],\n [{'id': 'A3', 'amount': 80.0}],\n]\n\n# 2. Oversized invoice goes in its own batch\ninvoices2 = [\n {'id': 'B1', 'amount': 200.0},\n {'id': 'B2', 'amount': 50.0},\n]\nassert chunk_invoices(invoices2, 100.0) == [\n [{'id': 'B1', 'amount': 200.0}],\n [{'id': 'B2', 'amount': 50.0}],\n]\n\n# 3. Everything fits in one batch\ninvoices3 = [\n {'id': 'C1', 'amount': 30.0},\n {'id': 'C2', 'amount': 40.0},\n {'id': 'C3', 'amount': 20.0},\n]\nassert chunk_invoices(invoices3, 100.0) == [\n [\n {'id': 'C1', 'amount': 30.0},\n {'id': 'C2', 'amount': 40.0},\n {'id': 'C3', 'amount': 20.0},\n ],\n]\n\nprint(\"All tests passed!\")\n```", "output_b": "\n\n```python\ndef chunk_invoices(invoices, max_total):\n \"\"\"\n Group invoices into batches where each batch's total 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...\n if current_batch and current_sum + amount > max_total:\n # Finalize the current batch\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 # Don't forget the last batch\n if current_batch:\n batches.append(current_batch)\n \n return batches\n\n\n# ─── Test Cases ───────────────────────────────────────────────────────────────\n\n# Test 1: Basic chunking — invoices split across multiple 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) → 230 > 200 → stop\n# Batch 2: A3(50) + A4(90) = 140 ≤ 200; adding A5(60) → 200 ≤ 200 → include\n# Batch 2: A3(50) + A4(90) + A5(60) = 200 ≤ 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: Single invoice exceeds max_total — it gets its own batch\ninvoices_2 = [\n {'id': 'B1', 'amount': 50.0},\n {'id': 'B2', 'amount': 500.0}, # exceeds max_total of 200\n {'id': 'B3', 'amount': 30.0},\n {'id': 'B4', 'amount': 40.0},\n]\nresult_2 = chunk_invoices(invoices_2, 200.0)\n# Batch 1: B1(50); adding B2(500) → 550 > 200 → finalize [B1]\n# Batch 2: B2(500); adding B3(30) → 530 > 200 → finalize [B2] (oversized, alone)\n# Batch 3: B3(30) + B4(40) = 70 ≤ 200\nassert result_2 == [\n [{'id': 'B1', 'amount': 50.0}],\n [{'id': 'B2', 'amount': 500.0}],\n [{'id': 'B3', 'amount': 30.0}, {'id': 'B4', 'amount': 40.0}],\n], f\"Test 2 failed: {result_2}\"\nprint(\"Test 2 passed ✓\")\n\n# Test 3: Empty list and single-item list edge cases\nassert chunk_invoices([], 100.0) == [], \"Test 3a failed: empty list should return []\"\n\ninvoices_3 = [{'id': 'C1', 'amount': 75.0}]\nresult_3 = chunk_invoices(invoices_3, 100.0)\nassert result_3 == [[{'id': 'C1', 'amount': 75.0}]], 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** before adding each invoice: if the current batch is non-empty **and** adding the invoice would exceed `max_total`, finalize the current batch and start a fresh one.\n3. **Always add** the invoice to the current batch — this handles the \"oversized invoice\" case naturally: if an invoice's amount alone exceeds `max_total`, it starts a fresh batch and gets added as the sole item. The next invoice will then trigger a new batch.\n4. **Finalize** the last batch after the loop ends.\n\n### Key Design Decisions\n\n| Concern | Decision |\n|---|---|\n| **Oversized invoices** | Automatically isolated — they start a new batch, and the next invoice triggers another new batch |\n| **Exact boundary** (`sum == max_total`) | Allowed (uses `>` not `>=`) — see Test 1 where batch 2 sums to exactly 200 |\n| **Empty input** | Returns `[]` cleanly |\n| **Order preservation** | Guaranteed — invoices are never reordered or skipped |", "latency_ms": { "a": 20950, "b": 21334 }, "tokens_out": { "a": 1659, "b": 1559 }, "cost_usd": { "a": 0.0103144, "b": 0.03946 }, "winner": "A", "judge_scores": { "a": 9.8, "b": 9.3 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses correctly implement the function and include the required test cases. Response 2 is the winner because its algorithm is much more elegant; the standard greedy packing logic naturally isolates oversized items without needing a special conditional branch like Response 1 does. / Both responses provide correct, efficient Python code that perfectly meets all requirements. Response 1 uses a clever, elegant algorithm that naturally handles oversized items without extra branching, while Response 2 uses explicit branching which is also highly readable. Both include the requested assertions. | openai/gpt-5.6-terra: A — Both implementations correctly preserve order, enforce the maximum for normal batches, isolate oversized invoices, and include three labeled test cases using assert. Response 1 is more concise and directly handles oversized invoices explicitly. Response 2 is also correct, but its lengthy explanation and extra output are less efficient for the requested task. / Both implementations correctly preserve order, enforce batch limits, isolate oversized invoices, and include the requested tests. Response 2 handles oversized invoices more explicitly and is more concise. Response 1 is also correct, but its test section includes an extra assertion under its third labeled test and is substantially more verbose than necessary. | deepseek/deepseek-v4-pro: A — Both functions are correct and meet all task requirements. R1 presents the solution concisely; R2 adds extensive commentary that, while clear, slightly reduces concision for a busy professional. / Both implement correctly with 3 assert tests. R1 includes extra commentary, reducing concision. R2 is clearer and more concise for a busy non-technical user.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "tie" }, "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": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 9 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 9 } ] }, { "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": "**What it does:** \nThis is a simple memoization helper. `memo` takes any function `fn` and returns a wrapper. The wrapper turns the arguments into a string key via `JSON.stringify`, checks a shared `Map` cache, and either returns the stored result or calls `fn`, stores the result, then returns it. Repeated calls with the same arguments skip re-execution.\n\n**One risk:** \nThe cache is a module-level `Map` that grows forever and is shared by every function passed to `memo`. This can cause unbounded memory growth (a leak) and incorrect results if two different functions happen to receive arguments that stringify to the same key.", "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-computation.\n3. If not, it calls the original function, stores the result, and returns it.\n\nThis is useful for expensive or frequently repeated calculations with the same inputs.\n\n## Key Risk: Memory Leak\n\nThe `cache` is a **module-level `Map` that grows forever**. Every unique set of arguments adds an entry that is never removed. In a long-running application, this can consume increasing amounts of memory. Additionally, all memoized functions **share the same cache**, so different functions with identical arguments could return incorrect results (collisions).\n\nA fix would be to scope the cache *inside* `memo` and consider a size limit or eviction strategy (e.g., LRU).", "latency_ms": { "a": 9012, "b": 7447 }, "tokens_out": { "a": 431, "b": 233 }, "cost_usd": { "a": 0.0029404, "b": 0.006325 }, "winner": "A", "judge_scores": { "a": 9.8, "b": 7.2 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 is concise, accurate, and stays well under the 150-word limit. Response 2 is also excellent in its explanation but violates the strict word count constraint by exceeding 150 words. / Both responses accurately explain the code and identify the risks within the word limit. Response 2 is slightly more concise and direct, making it an excellent quick explanation for a junior developer. | openai/gpt-5.6-terra: tie (flipped on swap) — Both accurately explain memoization and identify the shared, unbounded cache risk, and both are under 150 words. Response 1 is more concise and directly tailored to the requested explanation plus one risk. Response 2 is clear and useful but slightly more verbose and introduces an additional risk and fix beyond the request. / Both accurately explain memoization, JSON-based cache keys, and the shared module-level cache risk. Both are under 150 words, clear for a junior developer, and identify unbounded memory growth; they also correctly note cross-function cache collisions. Response 2 is slightly more concise, but both fully satisfy the task. | deepseek/deepseek-v4-pro: A — Response 1 concisely explains memoization and the risk of unbounded cache growth and key collisions. Response 2 is also correct but adds unnecessary fix suggestion, making it less concise for a busy professional. / Response 1 exceeds the 150-word limit, violating the constraint. Response 2 is concise, correct, and highlights the shared cache risk clearly. It is better suited for a busy non-technical professional due to brevity.", "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": "tie", "flipped_on_swap": true, "score_a": 10, "score_b": 9.5 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "A", "flipped_on_swap": false, "score_a": 9.5, "score_b": 5 } ] }, { "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.id, c.country, c.name\n),\nranked AS (\n SELECT\n country,\n name,\n total_spend,\n RANK() OVER (PARTITION BY country ORDER BY total_spend DESC) AS rank\n FROM customer_spend\n)\nSELECT country, name, total_spend, rank\nFROM ranked\nWHERE rank <= 3\nORDER BY country, rank;\n```", "output_b": "\n\n```sql\nSELECT\n country,\n name,\n total_spend,\n spend_rank AS rank\nFROM (\n SELECT\n c.country,\n c.name,\n SUM(o.total) AS total_spend,\n RANK() OVER (\n PARTITION BY c.country\n ORDER BY SUM(o.total) DESC\n ) AS spend_rank\n FROM customers c\n JOIN orders o ON o.customer_id = c.id\n WHERE o.created_at >= '2025-01-01'\n AND o.created_at < '2026-01-01'\n GROUP BY c.country, c.id, c.name\n) ranked\nWHERE spend_rank <= 3\nORDER BY country, spend_rank;\n```\n\n## How It Works\n\n### Step-by-step breakdown:\n\n1. **Inner query – Aggregation & Ranking**\n - **JOIN** `customers` to `orders` on `customer_id` to associate each order with a customer and their country.\n - **WHERE clause** filters orders to the year 2025 using a *sargable* range condition (`>= '2025-01-01' AND < '2026-01-01'`), which allows index usage — unlike wrapping `created_at` in `EXTRACT(...)` or `DATE_PART(...)`.\n - **GROUP BY** `c.country, c.id, c.name` computes each customer's `total_spend` via `SUM(o.total)`. Including `c.id` ensures two customers with the same name in the same country are kept separate.\n - **`RANK() OVER (PARTITION BY c.country ORDER BY SUM(o.total) DESC)`** assigns a rank within each country. `RANK()` is chosen so that tied customers receive the same rank (e.g., two customers tied at #3 both get rank 3).\n\n2. **Outer query – Filtering**\n - `WHERE spend_rank <= 3` keeps only the top 3 per country.\n - `ORDER BY country, spend_rank` gives a clean, readable result.\n\n### Why `RANK()` vs `ROW_NUMBER()` vs `DENSE_RANK()`?\n\n| Function | Behavior on ties | Effect on \"top 3\" |\n|---|---|---|\n| **`ROW_NUMBER()`** | Breaks ties arbitrarily | Always exactly 3 rows per country |\n| **`RANK()`** | Ties share a rank, next rank is skipped | May return >3 rows if ties exist at rank 3 |\n| **`DENSE_RANK()`** | Ties share a rank, no gap | May return even more rows than `RANK()` |\n\n`RANK()` is the most common choice for \"top N\" leaderboards since it fairly handles ties while still reflecting the true position.", "latency_ms": { "a": 8632, "b": 12963 }, "tokens_out": { "a": 590, "b": 682 }, "cost_usd": { "a": 0.0038664, "b": 0.017425 }, "winner": "A", "judge_scores": { "a": 9.8, "b": 9.3 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Both responses provide accurate, optimal SQL queries that meet all constraints. Response 1 is preferred for being more concise, providing exactly what was asked without unnecessary technical explanations that a busy non-technical professional wouldn't need. / Both responses provide accurate and correct SQL queries that meet all constraints. Response 2 is slightly better as it uses CTEs (WITH clauses) which makes the query much more readable and maintainable compared to the nested subquery approach in Response 1. | openai/gpt-5.6-terra: A — Both queries correctly aggregate 2025 spend, rank customers within each country using RANK(), and filter ranks through 3. Response 1 is more concise and directly satisfies the request. Response 2 is equally correct but includes substantial explanatory material beyond what a busy professional needs. / Both queries correctly aggregate 2025 orders per customer, rank spending within each country using a window function, and return ranks up to 3 with the requested columns. Response 2 is more concise and directly delivers the requested single query. Response 1 is also correct but includes unnecessary explanatory material for the task. | deepseek/deepseek-v4-pro: tie (flipped on swap) — Both queries correctly return top 3 per country using a window function, handle ties with RANK, filter to 2025, and present required columns. Response 2 adds explanatory text but the core task is equally satisfied. / Both queries correct; Response 1 adds clear explanation of tie handling and index usage, more useful for a non-technical 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": 10, "score_b": 9 }, { "judge": "openai/gpt-5.6-terra", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 9 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.5, "score_b": 10 } ] }, { "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:** `/^[A-Z]{1,2}\\d[A-Z\\d]?\\s*\\d[A-Z]{2}$/i`\n\n**One-line JS example:** \n`const isValid = /^[A-Z]{1,2}\\d[A-Z\\d]?\\s*\\d[A-Z]{2}$/i.test(formField.value);`\n\n**Edge case not handled:** Special postcodes such as `GIR 0AA` (and similar non-standard formats).", "output_b": "## UK Postcode Regex\n\n### The Regex\n\n```regex\n/^[A-Z]{1,2}\\d[A-Z\\d]?\\s?\\d[A-Z]{2}$/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-code (letter or digit, e.g., the `1` in `SW1A` or `A` itself) |\n| `\\s?` | Optional space between outward and inward codes |\n| `\\d` | Inward code sector digit |\n| `[A-Z]{2}$` | Two-letter inward code unit |\n| `i` flag | Case-insensitive (allows lowercase) |\n\n### Matches\n\n```\nSW1A 1AA ✅ (Westminster)\nM1 1AE ✅ (Manchester)\nB33 8TH ✅ (Birmingham)\nb338th ✅ (lowercase, no space)\nEC1A 1BB ✅ (London)\n```\n\n### One-Line JavaScript Validation\n\n```javascript\nconst isValidPostcode = (value) => /^[A-Z]{1,2}\\d[A-Z\\d]?\\s?\\d[A-Z]{2}$/i.test(value.trim());\n```\n\n**Usage in a form context:**\n\n```javascript\n// Example: validate on form submit\ndocument.querySelector('form').addEventListener('submit', (e) => {\n const postcode = document.getElementById('postcode').value;\n if (!isValidPostcode(postcode)) {\n e.preventDefault();\n alert('Please enter a valid UK postcode');\n }\n});\n```\n\n### ⚠️ Edge Case NOT Handled\n\n**Special/non-geographic postcodes like `GIR 0AA`** (the old Girobank postcode) or **BFPO codes** (British Forces Post Office, e.g., `BFPO 1`) are not matched. These use formats that fall outside the standard `A(A)9(9/A) 9AA` pattern. Additionally, this regex does **not** validate that the postcode *actually exists* — it only checks the structural format. For instance, `ZZ9 9ZZ` would pass despite being fictitious.", "latency_ms": { "a": 11505, "b": 13013 }, "tokens_out": { "a": 598, "b": 609 }, "cost_usd": { "a": 0.0039124, "b": 0.01561 }, "winner": "tie", "judge_scores": { "a": 9, "b": 8 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 is perfectly concise, directly answering all parts of the prompt without unnecessary fluff. Response 2 includes a lot of extra information and code that wasn't requested, making it less ideal for a busy professional. / Response 2 is perfectly concise, providing exactly what was asked without unnecessary filler. Response 1 includes extra code blocks and explanations that were not requested, making it less ideal for a busy professional looking for a quick answer. | openai/gpt-5.6-terra: tie (flipped on swap) — Both regexes match the requested examples, lowercase, and no-space forms, and correctly exclude GIR 0AA. Response 1 is more direct and its JavaScript explicitly validates a form field. Response 2 is overly long for the request and has a slightly confusing breakdown of the optional outward-code character. / Both provide a case-insensitive structural regex, one-line JavaScript validation, and an edge case. Response 1 is more useful because it trims form input and explains limitations. Response 2 is concise but uses \\s*, allowing multiple whitespace characters rather than a single optional space, and does not trim input. Neither regex validates real postcode allocation. | deepseek/deepseek-v4-pro: abstain — unparseable", "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": 8 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": true, "score_a": 8, "score_b": 8 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "abstain", "flipped_on_swap": false, "score_a": 0, "score_b": 0 } ] }, { "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": "The current weekly profit (do not take the deal) is calculated as follows:\n\n- Revenue: \\(450 \\times 3.20 = 1440\\)\n- Ingredients: \\(450 \\times 1.10 = 495\\)\n- Labour: \\(450 \\times 0.90 = 405\\)\n- Fixed costs: \\(480\\)\n- Total costs: \\(495 + 405 + 480 = 1380\\)\n- Profit: \\(1440 - 1380 = 60\\)\n\nUnder the supplier deal the bakery must pay for 600 loaves of ingredients regardless, but it can increase production/sales to the new maximum of 520 loaves. The associated weekly profit is:\n\n- Revenue: \\(520 \\times 3.20 = 1664\\)\n- Ingredients: \\(600 \\times 0.85 = 510\\)\n- Labour: \\(520 \\times 0.90 + 60 = 528\\)\n- Fixed costs: \\(480\\)\n- Total costs: \\(510 + 528 + 480 = 1518\\)\n- Profit: \\(1664 - 1518 = 146\\)\n\nThe deal raises weekly profit from \\(£60\\) to \\(£146\\). The bakery should take the deal (and raise output to 520 loaves).", "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| 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 Breakdown of Why It Works\n\nDespite **paying for 80 wasted loaves of ingredients** (80 × £0.85 = £68 wasted), the deal still wins because:\n\n- ✅ **Ingredient saving on loaves actually sold:** 520 × (£1.10 − £0.85) = **£130 saved**\n- ❌ **Wasted ingredients:** 80 × £0.85 = **£68 lost**\n- ✅ **Extra revenue from 70 more loaves:** 70 × £3.20 = **£224 gained**\n- ❌ **Extra labour:** **£60 spent**\n- **Net improvement: £130 − £68 + £224 − £60 = +£149** ✓ (cross-checks)\n\n---\n\n## Recommendation\n\n**Yes — take the deal.** It nearly **triples weekly profit** from £60 to £209, even after absorbing £68/week in wasted ingredients. The margin of benefit (£149/week) is large enough to remain profitable even if sales dip slightly below the 520 maximum. The break-even point where Option B merely matches Option A's £60 profit would require a significant shortfall in sales, providing a comfortable safety margin.", "latency_ms": { "a": 130624, "b": 15698 }, "tokens_out": { "a": 6106, "b": 787 }, "cost_usd": { "a": 0.0370824, "b": 0.02037 }, "winner": "tie", "judge_scores": { "a": 7.2, "b": 7.2 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Response 2 correctly interprets the '£60/week extra labour cost overall' as the total additional labour cost, whereas Response 1 incorrectly applies the per-loaf labour rate to the new total and then adds the £60 on top. Response 2 also provides a much clearer breakdown. / Response 1 correctly interprets the extra £60 labour cost as the total additional labour required. Response 2 double-counts the labour for the extra 70 loaves by applying the £0.90 rate to 520 and then adding £60. Response 1 is also much better formatted. | openai/gpt-5.6-terra: A — Response 1 correctly includes £0.90 labour for all 520 loaves plus the £60 extra labour cost, giving £146 profit and a clear recommendation. Response 2 omits labour for the additional 70 loaves, overstating profit by £63 (£209 instead of £146); its recommendation remains correct but its calculations and claimed margin are materially wrong. / Response 1 omits the normal £0.90-per-loaf labour cost for the additional 70 loaves, understating labour by £63 and overstating deal profit by £63 (£209 rather than £146). Its recommendation remains correct. Response 2 correctly includes £468 normal labour plus £60 extra labour, shows both calculations, and gives a clear recommendation. | deepseek/deepseek-v4-pro: tie (flipped on swap) — Response 1 miscalculates labour cost under deal (double-counts extra £60), yielding wrong profit £146 instead of £209. Response 2 is accurate, well-structured, and includes clear breakdown. Response 2 wins. / Response 1 incorrectly calculates labour cost as £465 instead of £528, yielding wrong profit (£209 vs correct £146). Response 2 is accurate, concise, and directly answers the question. Response 2 wins.", "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": 6, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 5 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": true, "score_a": 5.5, "score_b": 6.5 } ] }, { "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": "**7-Day Launch Week Plan**\n\n- **Day 1:** Founder finalizes sales page, pricing, and email sequence; VA sets up basic tracking links. \n- **Day 2:** Founder writes and schedules cart-open email + social posts; VA loads email sequence and checks list hygiene (2,000). \n- **Day 3:** Founder goes live / posts value content to warm list; VA launches £100 of ads (retargeting + lookalikes) and monitors. \n- **Day 4:** Founder sends cart-open broadcast + personal outreach; VA replies to support queries and adjusts ad creative (£100). \n- **Day 5:** Founder hosts short Q&A or scarcity reminder; VA pushes remaining £100 ads and segments engagers. \n- **Day 6:** Founder sends last-chance email + testimonials; VA handles fulfillment setup and customer questions. \n- **Day 7:** Founder closes cart, sends closing email, and reviews numbers; VA pauses ads and delivers course access.\n\n**Single riskiest dependency:** Email deliverability + open rates on the 2,000-person list (if it underperforms, ads and VA time cannot fully 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\" email to full list with early-bird price (48hr expiry) + post personal story on primary social channel | Founder |\n| **Tue (D2)** | Launch £300 ad campaign — one retargeting audience (email list custom/lookalike) and one cold interest audience, both driving to sales page | VA |\n| **Wed (D3)** | Send early-bird deadline reminder email (scarcity); reply personally to every inbound question to boost conversions | Founder |\n| **Thu (D4)** | Go live (IG/LinkedIn/YouTube) for a 20-min free teaching session + live Q&A with CTA to course; VA monitors comments and drops link | Founder + VA |\n| **Fri (D5)** | Send social-proof email (testimonial, beta feedback, or \"X people enrolled so far\"); VA pauses underperforming ad set and reallocates spend | Founder (email) / VA (ads) |\n| **Sat (D6)** | Publish a short objection-handling FAQ post on social; VA sends personal DMs/emails to anyone who clicked but didn't buy | Founder (content) / VA (outreach) |\n| **Sun (D7)** | Send final \"doors close tonight\" email sequence (morning + 6 hrs before + 1 hr before); close cart at midnight | Founder |\n\n---\n\n## 🚩 Riskiest Dependency\n\n**The email list's deliverability and engagement.** With 2,000 subscribers, email is responsible for the vast majority of expected revenue (ads at £300 will only generate a thin layer of traffic). If open rates are poor — due to a cold list, spam-folder issues, or a recent platform migration — the entire launch underperforms and there is no budget lever to compensate in-week. **Mitigation:** Send a warm-up / value email 3–5 days *before* Day 1 to flush bounces, re-engage openers, and confirm deliverability rates before you're committed.", "latency_ms": { "a": 8769, "b": 18324 }, "tokens_out": { "a": 351, "b": 530 }, "cost_usd": { "a": 0.0024324, "b": 0.01363 }, "winner": "B", "judge_scores": { "a": 6.7, "b": 8.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Response 2 provides a much better launch strategy, correctly assuming prep work is done before launch week. Its table format is clear, and the mitigation advice for the riskiest dependency adds great value for a non-technical professional. / Response 1 provides a much more realistic and effective launch plan (pre-launch work is done before Day 1). Response 2 wastes the first few days of 'launch week' on prep work. Both followed formatting constraints well. | openai/gpt-5.6-terra: B — Both provide seven daily actions with named owners and identify a plausible single risk. Response 2 is more conversion-focused and operationally sequenced, with clear launch, urgency, social proof, and close activities. Response 1 has a minor sequencing ambiguity: ads and live/value content begin before its stated cart-open broadcast on Day 4. / Response 1 is more launch-ready: it opens sales immediately, uses the list, ads, live event, proof, and deadline sequence coherently. Its main weakness is no explicit allocation of the VA’s 10 hours. Response 2 spends early launch days on unfinished setup and delays cart opening, reducing selling time; it also lacks clear VA-hour prioritisation. | deepseek/deepseek-v4-pro: B — Response 2 is clearer with a table, names owners explicitly, adds a pre-condition, and provides a risk mitigation. Response 1 is adequate but less structured. Both are correct and met the constraint. / Response 1 is more specific, actionable, and structured with a clear table, pre-condition, and risk mitigation. Response 2 has vague timing (e.g., finalizing sales page on Day 1) and less scannable owner attribution, making it less useful for a busy professional.", "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": 6.5, "score_b": 9 }, { "judge": "openai/gpt-5.6-terra", "verdict": "B", "flipped_on_swap": false, "score_a": 6.5, "score_b": 8 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "B", "flipped_on_swap": false, "score_a": 7, "score_b": 9 } ] }, { "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": "**Recommend (B): freelancers.**\n\n**Cost:** Freelancers total ~£24k/year (400×60). A permanent hire is £55k salary plus ~10-15% on-costs (NI, pension, etc.), so £60k+. Outsourcing the expected overflow is far cheaper.\n\n**Flexibility:** Contractors scale to actual demand and stop when work drops; a hire is fixed overhead even in quiet periods.\n\n**Quality risk:** Freelancers bring variance (vetting, context loss, availability). A mid-level employee offers continuity and team knowledge, but only if you can keep them utilised.\n\n**Non-obvious factor:** IR35/off-payroll rules and management overhead. Inside-IR35 determinations or status disputes can erase the cost edge and add admin; plus onboarding/knowledge transfer still consumes senior time.\n\nFor pure overflow at 60 days, contractors win on cost and flexibility. Hire only if you expect sustained >~100-120 billable days and want tighter quality control. Revisit in 6 months with actual utilisation data.", "output_b": "## Recommendation: Option B (Freelancers) — with conditions\n\n**Cost**\nOption A: ~£55k salary + ~£16-20k in employer NI, pension, equipment, and overhead ≈ **£71-75k effective cost**.\nOption B: 60 days × £400 = **£24k** — less than half, even if days creep to 90 (~£36k).\n\n**Flexibility**\nFreelancers scale to zero in quiet periods and up during peaks. A permanent hire is a fixed cost regardless of workload. For a 12-person agency with variable project flow, this elasticity is significant.\n\n**Quality Risk**\nThe real vulnerability. Freelancers require onboarding each engagement, may not absorb your codebase conventions, and create key-person risk if one becomes a de facto team member. *Mitigate by maintaining a vetted shortlist of 3-4 freelancers and enforcing internal code-review standards.*\n\n**Non-obvious factor: Hiring signal**\nA permanent hire at this stage signals growth to clients and recruits alike. But a *bad* mid-level hire in a 12-person team is culturally devastating — roughly 8% of your workforce. The cost of a mis-hire (re-recruitment, lost morale, ~6 months of drag) dwarfs the freelancer premium.\n\n**Bottom line:** Start with B now. Re-evaluate for A when overflow consistently exceeds ~100 days/year, giving you conviction about the role's scope before committing.", "latency_ms": { "a": 14897, "b": 13285 }, "tokens_out": { "a": 632, "b": 352 }, "cost_usd": { "a": 0.0041284, "b": 0.0092 }, "winner": "tie", "judge_scores": { "a": 9.3, "b": 9 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses follow all instructions perfectly and stay under the 200-word limit. Response 1 is slightly better as its non-obvious factor (IR35/off-payroll rules) is highly specific and critical for UK-based agencies (indicated by the £ symbol), making it more practically useful. / Both responses follow all instructions perfectly and are well under the 200-word limit. Response 1 provides a slightly more accurate estimate of UK employer on-costs and offers a highly insightful non-obvious factor regarding team culture and mis-hire risk. | openai/gpt-5.6-terra: tie (flipped on swap) — Both meet the under-200-word requirement and address all requested factors. Response 1 is more financially grounded, concise, and offers a genuinely non-obvious IR35/management consideration. Response 2 is useful but its £71–75k permanent-hire estimate is weakly substantiated, and its ~100-day threshold does not align with that stated cost. / Both meet the under-200-word requirement and cover all requested factors. Response 1 gives more realistic employer-cost detail and a strong mis-hire point, but its 100-day threshold is somewhat assertive. Response 2 is more concise and adds a useful UK-specific IR35 consideration, though its on-cost estimate may understate total employment cost. | deepseek/deepseek-v4-pro: abstain — unparseable", "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": 8.5 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "abstain", "flipped_on_swap": false, "score_a": 0, "score_b": 0 } ] }, { "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": "### Setup and rates\n- Tank capacity: \\(2400\\) L \n- Pump A (fill): \\(+40\\) L/min \n- Pump B (drain): \\(-25\\) L/min \n\n### Phase 1: 09:00–09:20 (A only)\nDuration \\(= 20\\) min. \nVolume added \\(= 20 \\times 40 = 800\\) L. \nVolume at 09:20: \\(800\\) L.\n\n### Phase 2: 09:20–10:00 (A and B together)\nNet rate \\(= 40 - 25 = +15\\) L/min. \nDuration \\(= 40\\) min. \nVolume added \\(= 40 \\times 15 = 600\\) L. \nVolume at 10:00: \\(800 + 600 = 1400\\) L.\n\n### Phase 3: after 10:00 (A only)\nRemaining volume \\(= 2400 - 1400 = 1000\\) L. \nTime required \\(= 1000 / 40 = 25\\) min. \n\nThus 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 litres**\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 litres**\n\n**Running total at 10:00:** 800 + 600 = **1,400 litres**\n\n## Phase 3: Only Pump A running again (10:00 onwards)\n\nRemaining volume: 2,400 − 1,400 = **1,000 litres**\nTime needed: 1,000 ÷ 40 = **25 minutes**\n\n## Answer\n\nThe tank is full at **10:25**.", "latency_ms": { "a": 7773, "b": 4828 }, "tokens_out": { "a": 540, "b": 235 }, "cost_usd": { "a": 0.0035724, "b": 0.006295 }, "winner": "tie", "judge_scores": { "a": 10, "b": 10 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses correctly solve the math problem step-by-step, arriving at the correct time of 10:25. Both are clear, concise, and easy to follow. / Both responses correctly solve the math problem, show clear step-by-step working, and arrive at the correct time. They are both highly readable and concise. | openai/gpt-5.6-terra: tie — Both responses correctly calculate 800 L by 09:20, 600 L during the 40-minute overlap, and 1,400 L by 10:00. They then correctly find that 1,000 L takes 25 minutes at 40 L/min, giving 10:25. Both show clear working and are concise. / Both responses correctly calculate 800 L by 09:20, 600 L net from 09:20–10:00, and 1,000 L remaining at 10:00. At 40 L/min, this takes 25 minutes, yielding 10:25. Both show clear, complete working and are concise. | deepseek/deepseek-v4-pro: tie — Both responses are factually accurate, clearly presented, and meet all requirements. No constraints were violated. / Both responses are correct, show all working, and are equally clear and concise. No constraints were given, so no violations. They are equally 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": "deepseek/deepseek-v4-pro", "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": "**Correlation ≠ causation + non-linearity.**\n\nThe drop after March doesn’t prove the programme caused it (other factors may explain the change). Even if it did, effects rarely scale linearly—doubling spend won’t automatically halve churn again due to diminishing returns, saturation, or confounds.", "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 simultaneous product improvement, 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. Extrapolating linearly from one data point is unfounded.", "latency_ms": { "a": 4129, "b": 6631 }, "tokens_out": { "a": 149, "b": 160 }, "cost_usd": { "a": 0.0012084, "b": 0.004365 }, "winner": "B", "judge_scores": { "a": 8.7, "b": 9.7 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Both responses correctly identify the two flaws (causation vs correlation and linear extrapolation) and stay under the 100-word limit. Response 2 is better structured, more detailed, and easier for a non-technical professional to understand. / Both responses correctly identify the two main flaws (causation vs correlation and linear extrapolation) and stay under the 100-word limit. Response 1 is better structured and provides a clearer explanation for a non-technical professional. | openai/gpt-5.6-terra: B — Both correctly identify unsupported causation and unjustified linear extrapolation, and both are under 100 words. Response 2 is more useful because it gives concrete alternative explanations and explains diminishing returns more clearly. Response 1 is accurate and concise but slightly less actionable. / Both correctly identify unsupported causal attribution and unjustified linear extrapolation, and both are under 100 words. Response 1 is slightly stronger because it clearly separates the two flaws and gives concrete alternative explanations plus the diminishing-returns rationale. Response 2 is accurate and more concise but less fully explained. | deepseek/deepseek-v4-pro: tie (flipped on swap) — Both correctly identify correlation/causation and non-linearity flaws. Response 1 is more concise; Response 2 more structured. Both are accurate, clear, and under 100 words. Equally useful. / Response 1 explicitly names both logical fallacies and explains them clearly. Response 2 is accurate but omits naming the post hoc fallacy, making it slightly less educational for a non-technical reader.", "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": 9.5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 9.5 } ] }, { "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\": \"quote for the workshop series mentioned, ideally 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": 2897, "b": 3489 }, "tokens_out": { "a": 198, "b": 79 }, "cost_usd": { "a": 0.0015304, "b": 0.002415 }, "winner": "A", "judge_scores": { "a": 9.8, "b": 5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 followed the constraint to return ONLY valid JSON, whereas Response 2 included markdown code blocks, violating the strict output constraint. / Response 2 followed the negative constraint to return ONLY valid JSON, whereas Response 1 included markdown code block formatting which makes the raw output invalid JSON. | openai/gpt-5.6-terra: A — Both extract all fields accurately and provide clear intent. Response 1 is valid JSON exactly as requested. Response 2 wraps the JSON in Markdown code fences, so its full output is not valid JSON and violates the return-only-valid-JSON constraint. / Both accurately extract all requested fields. Response 1 violates the explicit requirement to return only valid JSON by including Markdown code fences. Response 2 is valid JSON, complete, concise, and directly usable. | deepseek/deepseek-v4-pro: A — Response 1 includes the qualifier 'ideally' from the source, preserving intent accuracy. Response 2 omits it, reducing nuance. Response 2 also wraps JSON in a markdown code block, violating the 'only valid JSON' constraint. / Response 1 includes markdown code fences, violating 'Return only valid JSON'. Response 2 directly returns a clean JSON object, fully adhering to constraints. Both extract accurately, but formatting breach makes 1 less compliant.", "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": 10, "score_b": 5 }, { "judge": "deepseek/deepseek-v4-pro", "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": 9313, "b": 2552 }, "tokens_out": { "a": 631, "b": 111 }, "cost_usd": { "a": 0.0041864, "b": 0.00338 }, "winner": "tie", "judge_scores": { "a": 10, "b": 10 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses follow all instructions perfectly, accurately calculating the percentage changes to one decimal place and formatting the output as a markdown table with the requested columns. / Both responses perfectly follow the instructions, accurately calculate the percentages to one decimal place, and format the output as a markdown table with the requested columns. | openai/gpt-5.6-terra: tie — Both responses accurately extract all four products, calculate percentage changes correctly to one decimal, use n/a for Gamma’s undefined change, and provide the requested markdown table columns. Response 2’s explicit plus sign and Unicode minus are acceptable; Response 1’s unsigned positive percentage is also clear and standard. / Both responses accurately extract all quarterly figures, calculate percentage changes correctly to one decimal, use n/a for the undefined Gamma change, and provide the requested markdown table with all required columns. Response 1 includes an optional plus sign; Response 2 omits it, which is still clear and correct. | deepseek/deepseek-v4-pro: tie — Both responses correctly compute and format changes to one decimal with n/a where undefined, and follow all markdown and column instructions perfectly. No factual errors or broken constraints. / Both correctly formatted markdown tables with accurate Q1/Q2 data and precise one-decimal change percentages; Gamma shows n/a where undefined. They differ only in plus sign on Alpha's positive change, which isn’t required. No constraints broken.", "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": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 } ] }, { "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": 5982, "b": 2324 }, "tokens_out": { "a": 383, "b": 75 }, "cost_usd": { "a": 0.0026804, "b": 0.00244 }, "winner": "A", "judge_scores": { "a": 9.8, "b": 5.5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 followed all instructions perfectly, including the exact formatting constraint of one line per person with no extra text. Response 2 violated the formatting constraint by adding conversational filler and bullet points. / Response 2 followed the formatting constraint perfectly, providing exactly one line per person with no extra text or bullet points. Response 1 included unnecessary introductory text and bullet points, violating the strict format requested. | openai/gpt-5.6-terra: A — Response 1 accurately extracts all four people, roles, and organisations in the required line format. Response 2 adds non-format text and bullet prefixes, and changes Priya Shah’s organisation from the stated “Halcyon” to “Halcyon Health,” which is not explicitly supported. / Response 1 adds an introductory line and bullet markers, so it does not use exactly one plain formatted line per person. It also incorrectly changes Priya Shah's organisation from Halcyon to Halcyon Health. Response 2 follows the required four-line format and accurately preserves all stated roles and organisations. | deepseek/deepseek-v4-pro: A — Response 1 follows the requested plain-list format without extra text. Response 2 adds an introduction and bullet points, breaking the format. Both misassign Ben Tran's organization (should be Halcyon Health), but Response 1 is more concise and useful. / Response 2 is perfectly accurate, follows the format exactly without extra text, and is concise. Response 1 adds an unnecessary intro line and incorrectly expands Priya Shah’s organization to 'Halcyon Health' instead of the stated 'Halcyon'.", "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": 10, "score_b": 5 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "A", "flipped_on_swap": false, "score_a": 9.5, "score_b": 6.5 } ] }, { "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 harbour redevelopment on 12 June by 7-2.\n- Phase one costs £4.6m and begins in October.\n- Phase one includes the fish market roof.\n- Marina expansion unfunded, awaits January grant decision.\n- Local traders get first refusal on 14 retail units.", "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": 6222, "b": 2718 }, "tokens_out": { "a": 419, "b": 91 }, "cost_usd": { "a": 0.0029244, "b": 0.002905 }, "winner": "B", "judge_scores": { "a": 7.7, "b": 9.8 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Both responses follow all constraints, including the exact number of bullets and word limits per bullet. Response 2 is better because it captures all five distinct main points from the text, whereas Response 1 splits one point into two and misses the final point about construction traffic. / Both responses followed all constraints perfectly. Response 1 is better because it captures all five distinct key facts from the original text, whereas Response 2 splits one fact into two and omits the final sentence entirely. | openai/gpt-5.6-terra: B — Both provide exactly five concise bullets under 15 words. Response 1 accurately captures key details but omits the construction-traffic routing and does not identify the levelling-up grant. Response 2 covers five broader key facts, including traffic routing and the specific grant, though it omits that phase two is unfunded. / Response 1 accurately captures all five main facts, including construction traffic routing, within the required format and word limit. Response 2 meets the count and length constraints but splits phase-one details into two bullets, omits the construction traffic fact, and does not identify marina expansion as phase two. | deepseek/deepseek-v4-pro: B — Response 2 includes all key facts from the text, including construction traffic routing, while Response 1 omits that fact. Both follow constraints exactly. Response 2 is more complete and useful. / Response 1 captures all 5 key facts accurately and concisely. Response 2 omits the construction traffic routing fact and splits one fact into two, missing a key piece of information. Both meet word and bullet constraints.", "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": "B", "flipped_on_swap": false, "score_a": 7.5, "score_b": 9.5 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "B", "flipped_on_swap": false, "score_a": 7.5, "score_b": 10 } ] }, { "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": "{\"event\": \"AI in Retail Summit\", \"date\": \"2026-03-14\", \"venue\": \"Baltic Exchange\", \"capacity\": 320, \"speakers\": [{\"name\": \"Nadia Rossi\", \"topic\": \"demand forecasting\"}, {\"name\": \"Kwame Mensah\", \"topic\": \"dynamic pricing\"}], \"sold_out\": false}", "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": 4304, "b": 2479 }, "tokens_out": { "a": 280, "b": 127 }, "cost_usd": { "a": 0.0020824, "b": 0.00379 }, "winner": "A", "judge_scores": { "a": 10, "b": 5.8 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 followed the negative constraint to return ONLY the JSON, omitting markdown formatting. Response 2 included markdown code blocks, violating the 'only' constraint. / Response 2 followed all instructions perfectly, including the negative constraint to return ONLY the JSON. Response 1 included markdown code blocks, violating the 'only the JSON' constraint. | openai/gpt-5.6-terra: tie (flipped on swap) — Response 1 exactly matches the required JSON schema and correctly extracts all details. Response 2 contains the same correct data but adds Markdown code fences, violating the instruction to return only JSON; the complete output is therefore not valid raw JSON. / Both responses accurately extract all fields, match the required schema and data types, correctly infer sold_out as false, and provide valid JSON only. Response 1's Markdown code fence does not violate the requested JSON content in this evaluation context; both are equally clear and usable. | deepseek/deepseek-v4-pro: A — Both contain correct JSON with accurate data. Response 2 encloses it in a code block, violating the 'return only the JSON' instruction; violation reported but not score-deducted per rules. / Both responses accurately extract all data and match the schema exactly. Response 1 adds a code block, breaking the 'only JSON' rule, but the JSON content is correct. Response 2 is pure JSON. Both are 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": "tie", "flipped_on_swap": true, "score_a": 10, "score_b": 7.5 }, { "judge": "deepseek/deepseek-v4-pro", "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 photosynthesis. \nInside chloroplasts chlorophyll uses light energy converting them into sugars and oxygen. \nFinally oxygen releases while glucose fuels the plant growth and energy needs.", "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": 6338, "b": 3699 }, "tokens_out": { "a": 398, "b": 70 }, "cost_usd": { "a": 0.0026624, "b": 0.002025 }, "winner": "B", "judge_scores": { "a": 7.3, "b": 9.5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: B — Both responses follow all constraints perfectly, including sentence starters and word counts. Response 2 is the winner because it is more scientifically accurate, better articulated, and clearer for a non-technical reader. / Both responses follow all constraints perfectly. Response 1 is better written, more scientifically precise, and flows more naturally than Response 2, which lacks some punctuation and has slightly awkward phrasing. | openai/gpt-5.6-terra: B — Both meet all sentence-count, starting-word, and word-limit constraints. Response 2 is clearer, more grammatically polished, and more accurate about chloroplast processes. Response 1 is concise but has awkward phrasing and the incorrect construction “oxygen releases” rather than “oxygen is released.” / Response 1 fully meets the format and length requirements, is clear, and accurately summarizes key photosynthesis steps. Response 2 meets the explicit constraints but is less grammatically clear and less accurate: it implies sunlight is converted into sugars and oxygen, and “oxygen releases” is awkward. | deepseek/deepseek-v4-pro: B — Response 2 is more factually precise and clearer, with well-phrased sentences. Both adhere perfectly to all constraints: exactly 3 sentences, correct starters, and no sentence exceeding 20 words. / Response 1 accurately describes photosynthesis with precise language and perfect adherence to constraints. Response 2, while factually okay, is less clear and has awkward phrasing, lowering its usefulness and clarity. No rule violations detected.", "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": 7.5, "score_b": 10 }, { "judge": "openai/gpt-5.6-terra", "verdict": "B", "flipped_on_swap": false, "score_a": 6.5, "score_b": 9 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "B", "flipped_on_swap": false, "score_a": 7.8, "score_b": 9.5 } ] }, { "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 of commercial property for a fixed term under UK law, creating a legal interest in land enforceable against third parties. A licence merely permits occupation without exclusive possession, remaining a personal permission revocable by the licensor and offering fewer statutory protections than a lease does.", "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": 20332, "b": 5340 }, "tokens_out": { "a": 1453, "b": 69 }, "cost_usd": { "a": 0.0089644, "b": 0.0019 }, "winner": "tie", "judge_scores": { "a": 9.3, "b": 9.5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses perfectly meet the 50-word constraint and accurately explain the difference. Response 1 explicitly uses the prompt's terminology, while Response 2 includes a highly relevant reference to the Landlord and Tenant Act 1954. Both are excellent. / Both responses perfectly meet the 50-word constraint and accurately explain the difference. Response 1 is slightly better as it specifically mentions the Landlord and Tenant Act 1954, which is the crucial piece of legislation for UK commercial leases. | openai/gpt-5.6-terra: tie (flipped on swap) — Both are exactly 50 words, accurate, clear and useful. Response 2 more precisely identifies the proprietary versus personal distinction and relevant renewal-rights context. Its security-of-tenure wording is slightly broad because protection can be excluded. Response 1 is also strong but its statement that a licence is revocable is somewhat overgeneralised. / Both are exactly 50 words and clearly distinguish exclusive possession from personal permission. Response 1 slightly overstates Landlord and Tenant Act 1954 protection, which is not universal. Response 2 is more carefully qualified, accurate, and accessible, though it simplifies some exceptions. | deepseek/deepseek-v4-pro: tie (flipped on swap) — Response 2 explicitly names the Landlord and Tenant Act 1954, clarifying statutory protections and renewal rights, making it marginally more useful and precise. Both are accurate and meet the 50-word limit exactly. / Both responses exactly meet the 50-word constraint and are factually accurate, clearly explaining the lease/licence distinction. Response 1 names the key Act; Response 2 uses simpler language but still captures the legal difference. Equally useful and clear for a busy professional.", "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": 8.5, "score_b": 8.5 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.8, "score_b": 9.9 } ] }, { "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": "The harsh taste in coffee arises mainly from over-extraction during brewing. Water hotter than ideal dissolves too many polyphenols and other compounds from the ground seeds. Prolonged contact time between water and grounds similarly draws out excessive amounts of these substances. A very fine particle size exposes more surface, amplifying the effect. High-temperature processing of the seeds beforehand can generate additional pungent elements. Imbalance in the brew ratio, with too little liquid for the amount of grounds, concentrates these flavors. Proper control of temperature, duration, and grind helps minimize the unwanted sharpness.\n", "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 on your palate.\n\nBrewing method also plays a role. Longer extraction times pull more tannins and dissolved solids into your cup, amplifying the sharp, sometimes unpleasant notes. Water temperature, grind size, and steep duration all influence the final flavor profile. Darker preparations tend to produce more pronounced harshness than lighter ones.", "latency_ms": { "a": 27686, "b": 7318 }, "tokens_out": { "a": 1447, "b": 161 }, "cost_usd": { "a": 0.0089624, "b": 0.0043 }, "winner": "tie", "judge_scores": { "a": 8.2, "b": 8.8 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses follow all constraints perfectly, including word count and forbidden words. Response 1 is slightly more useful to a non-technical professional as it focuses on practical brewing variables they can control, whereas Response 2 leans slightly more into chemical names. / Both responses follow all constraints, including word count and forbidden words. Response 1 provides a more accurate and comprehensive explanation of the chemical compounds responsible for the harsh flavor, making it more useful and informative. | openai/gpt-5.6-terra: B — Both meet the 80–120-word range and avoid all forbidden terms and variants. Response 1 is clear and practical but somewhat oversimplifies the causes. Response 2 gives a more accurate chemical explanation alongside brewing factors, while remaining understandable and well organized. / Both meet the 80–120 word requirement and avoid all forbidden terms and variants. Response 1 more accurately identifies heat-driven compounds and brewing variables, while remaining clear. Response 2 is useful but overstates over-extraction as the main cause; processing chemistry is also a major driver. | deepseek/deepseek-v4-pro: tie (flipped on swap) — Response 1 is more concise and practical, focusing on over-extraction factors. Response 2 is accurate but more technical with chemical names. Both avoid forbidden words and meet word count. / Response 1 provides a more comprehensive explanation covering chemical compounds and brewing factors, while Response 2 focuses narrowly on brewing. Both are accurate and follow constraints, but 1 is more thorough.", "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": 8.5, "score_b": 9 }, { "judge": "openai/gpt-5.6-terra", "verdict": "B", "flipped_on_swap": false, "score_a": 7.5, "score_b": 9 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 8.5 } ] }, { "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 first apprentice has been an incredible experience that has taught me so much about leadership and patience. From the initial interviews to the onboarding process, every step reinforced the importance of investing in the next generation of talent in our industry. Watching them learn and contribute has brought fresh energy to the team.\n\nWhat advice would you give to someone considering taking on their first apprentice?\n\nThe journey is just beginning but already I can see how small wins compound over time leading to significant progress for everyone involved. Excited for what lies ahead.\n🚀", "output_b": "**Hiring Our First Apprentice Changed Everything**\n\nLast month, we brought on our first apprentice, and it's already transformed how our team operates. Teaching someone from scratch 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 genuinely contagious, and small wins compound into remarkable growth for everyone involved.\n\nWhat would your team look like if you invested in someone's potential rather than just their experience?\n\nIf you're on the fence about hiring an apprentice, take the leap. The return on patience and mentorship far exceeds what any résumé could promise. You won't just build a skilled team member—you'll build a stronger culture 🌱", "latency_ms": { "a": 16885, "b": 6077 }, "tokens_out": { "a": 1039, "b": 168 }, "cost_usd": { "a": 0.0065324, "b": 0.004525 }, "winner": "tie", "judge_scores": { "a": 6.3, "b": 5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses violate the paragraph constraint by having 4 paragraphs (Response 1 puts the emoji on a new line, Response 2 includes a title). Response 2 also fails the word count constraint (120 words) and the second paragraph constraint. Response 1 is closer to the constraints. / Response 1 fails the word count constraint (121 words) and has 4 paragraphs. Response 2 meets the word count but technically has 4 paragraphs due to the emoji being on its own line. Response 2 followed more constraints overall. | openai/gpt-5.6-terra: tie — Response 1 is within 90–110 words, includes the required phrase and question, but the standalone emoji creates a fourth paragraph. Response 2 is engaging and useful, but exceeds the word limit and its heading creates an additional paragraph. Response 1 better satisfies the core brief. / Response 1 is well written but exceeds the 90–110-word limit (about 113 words excluding the title). Response 2 meets the word range, phrase, question, and emoji requirements, but places the emoji in a separate fourth paragraph, violating the exactly-three-paragraph constraint. | deepseek/deepseek-v4-pro: A — Response 1 meets all constraints perfectly, is concise (96 words), and delivers a clear, useful message. Response 2 exceeds the word limit (115 words), making it less concise despite good content. / Response 2 meets all constraints: 90-110 words (96), one emoji at end, exactly 3 paragraphs, second paragraph a single question, includes 'small wins compound', no hashtags. Response 1 exceeds word count (121) and uses 4 paragraphs, violating the instructions.", "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": 5, "score_b": 5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": false, "score_a": 5, "score_b": 5 }, { "judge": "deepseek/deepseek-v4-pro", "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 Night Closing Checklist\nFront of house\n1. Lock all entrance doors securely\n2. Turn off all display lights\n3. Clean and tidy sales floor\n4. Empty trash bins completely\nBack office\n1. Count and secure cash drawer\n2. Shut down computer systems\n3. File daily sales reports\n4. Turn off office equipment\nSecurity\n1. Set the alarm system\n2. Check all window locks\n3. Activate security cameras\n4. Verify back door is locked", "output_b": "Nightly Closing Checklist for Retail Shop\n\nFront of house\n1. Turn off all display lights\n2. Lock the front entrance door\n3. Empty and reconcile the register\n4. Sweep and tidy the sales floor\n\nBack office\n1. File all daily sales receipts\n2. Shut down office computers completely\n3. Secure the safe with cash\n4. Turn off break room appliances\n\nSecurity\n1. Activate the store alarm system\n2. Check all windows are locked\n3. Review security camera footage briefly\n4. Lock the rear exit door", "latency_ms": { "a": 8768, "b": 4965 }, "tokens_out": { "a": 516, "b": 129 }, "cost_usd": { "a": 0.0034124, "b": 0.0036 }, "winner": "tie", "judge_scores": { "a": 9.7, "b": 9.8 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses perfectly follow all instructions, including the exact structure, section headers, item counts, and word count constraints per item. Both provide highly useful and clear checklists for a retail shop closing. / Both responses followed all instructions and constraints perfectly, including the exact structure and word counts per item. Response 1 is slightly better formatted with blank lines between sections, making it easier to read for a busy professional. | openai/gpt-5.6-terra: tie (flipped on swap) — Both responses exactly provide one title, the three required headings, and four numbered 3–8-word items under each. Both are accurate, practical, concise, and clear for a retail closing checklist. Response 2’s internal blank lines do not add substantive content or violate the required structure. / Both follow the required structure exactly, with valid headings, four numbered items per section, and 3–8 words per item. Response 1 is slightly more operationally complete, including register reconciliation, safe security, and rear-exit locking. Response 2's instruction to activate cameras is less generally appropriate, as cameras normally remain continuously active. | deepseek/deepseek-v4-pro: tie (flipped on swap) — Response 1 perfectly follows all constraints with standard, precise closing tasks. Response 2 also follows constraints but includes slightly less typical items (reviewing footage) and ambiguous phrasing, making it marginally less useful. / Both responses perfectly meet all constraints: exact structure, headings, item counts, word limits. Content is accurate, useful, clear, and concise. No violations detected. Scores tie at 10.", "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": 9.5, "score_b": 10 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": true, "score_a": 10, "score_b": 9.5 } ] }, { "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 UK rate is 20%.", "output_b": "VAT stands for Value Added Tax, and the current standard rate in the UK is 20%.", "latency_ms": { "a": 1893, "b": 1937 }, "tokens_out": { "a": 92, "b": 23 }, "cost_usd": { "a": 0.0007824, "b": 0.000705 }, "winner": "tie", "judge_scores": { "a": 10, "b": 10 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses perfectly follow the instructions, providing the correct definition and current UK rate of VAT in exactly one clear and concise sentence. They are virtually identical. / Both responses perfectly follow the instructions, providing the correct definition and current UK rate of VAT in exactly one sentence. They are equally clear, concise, and useful. | openai/gpt-5.6-terra: tie — Both responses are factually correct, each uses one sentence as required, and both clearly state that VAT means Value Added Tax and that the UK standard rate is 20%. Նրանք are equally concise and useful. / Both responses are factually correct, use one sentence, directly answer both parts, and are equally clear and concise. | deepseek/deepseek-v4-pro: tie — Both responses are identical, factually correct, follow the one-sentence instruction, and are clear and concise. / Both responses correctly define VAT and state the 20% rate in one sentence, meeting all constraints with equal clarity and usefulness.", "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": "deepseek/deepseek-v4-pro", "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\n'Your Tesco order 8812 is ready for collection' / NOT SPAM\n'URGENT your account will be closed verify now' / SPAM\n'Minutes 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": 5185, "b": 2222 }, "tokens_out": { "a": 321, "b": 68 }, "cost_usd": { "a": 0.0022464, "b": 0.0021 }, "winner": "A", "judge_scores": { "a": 10, "b": 6.3 }, "judge_reasoning": "google/gemini-3.1-pro-preview: A — Response 1 followed all instructions perfectly, outputting exactly the requested text. Response 2 included markdown code blocks, violating the 'output only' constraint. / Response 2 followed the 'output only' constraint perfectly by not including markdown code blocks, whereas Response 1 included backticks. Both correctly classified the items into two columns. | openai/gpt-5.6-terra: A — Both classify all four messages correctly. Response 1 follows the requested two-column-line-only format. Response 2 adds Markdown code fences, so it includes content beyond the requested lines despite otherwise clear, correct classifications. / Both classify all four messages correctly. Response 1 uses clear two-column content but adds code-fence lines, violating the instruction to output only two-column lines. Response 2 provides exactly four concise classification lines and is fully compliant. | deepseek/deepseek-v4-pro: tie (flipped on swap) — Response 1 uses plain two-column lines as instructed. Response 2 wraps output in a code block with extra formatting, violating the output constraint. Both classify correctly. / Both responses correctly classify all items, follow the two-column format, and contain no extra text. Classifications are accurate, and the outputs are clear and immediately usable. No constraint violations.", "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": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": true, "score_a": 10, "score_b": 7.5 } ] }, { "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 ?**", "output_b": "\"Hé, on a 10 minutes de retard - si t'arrives avant nous, prends-nous une table ?\"", "latency_ms": { "a": 3734, "b": 2510 }, "tokens_out": { "a": 172, "b": 33 }, "cost_usd": { "a": 0.0012844, "b": 0.00103 }, "winner": "tie", "judge_scores": { "a": 9.5, "b": 9.5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses provide excellent, informal translations. Response 1 is slightly more natural for a text message context by using 'Salut' instead of 'Hé' and keeping the original clause order. / Both translations are accurate and maintain the informal tone perfectly. Response 1 is slightly better as 'Hé' is a more direct translation of 'Hey' in this context compared to 'Salut' (Hi), and it doesn't include unnecessary markdown formatting. | openai/gpt-5.6-terra: tie (flipped on swap) — Both are accurate, informal French translations. Response 2 more explicitly preserves “if you get there first” with “si t’arrives avant nous,” making the timing relationship clearer. Response 1’s “si t’arrives avant” is understandable but slightly less precise. / Both are accurate, informal French translations. Response 2 is slightly more natural and concise, with smoother word order. Response 1 is understandable but “si t’arrives avant nous” is a little less direct than the original’s “if you get there first.” | deepseek/deepseek-v4-pro: tie — Both translations accurately convey the meaning in informal French. They use casual greetings and contractions, matching the tone perfectly. No constraints were specified, so none are violated. / Both translations are accurate, maintain informal tone with appropriate greetings and contractions. No errors, both clear and useful.", "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": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 } ] }, { "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": "**About Us**\n\nNestled in the heart of Yorkshire, our family-run garden centre has been a cherished local treasure since 1987. Founded with a passion for plants and community, it began as a modest dream that has blossomed over nearly four decades. Today, it is lovingly run by the founders’ daughter, who continues the family tradition with the same dedication and green-fingered expertise.\n\nWe specialise in rare and unusual perennials, carefully selected to bring unique colour, texture, and interest to gardens of every size. From elusive heritage varieties to hard-to-find treasures, our knowledgeable team is always on hand to share advice and help you discover something special.\n\nAfter browsing our beds and borders, relax in our cosy small café, where homemade cakes, fresh coffee, and friendly smiles await. Whether you’re a seasoned gardener or simply seeking inspiration, we invite you to experience the warmth, quality, and personal touch that have defined us since the beginning. Come and grow with us.", "output_b": "# About Us\n\nWelcome to Thornfield Garden Centre, a proud Yorkshire family business rooted in passion and tradition since 1987.\n\nFounded by Margaret and David Hartley on a modest plot of farmland, our garden centre began as a simple dream: to share the beauty of exceptional plants with our community. Today, their daughter, Sarah, carries that dream forward with the same dedication and green-fingered expertise her parents instilled in her from childhood.\n\nWe've earned a reputation across Yorkshire and beyond for our outstanding collection of rare and unusual perennials, carefully sourced and lovingly nurtured right here in our own nurseries. Whether you're a seasoned plantsman or a curious beginner, our knowledgeable team is always delighted to help.\n\nAfter exploring our gardens, we invite you to relax in The Potting Shed, our charming café serving homemade cakes, hearty lunches, and proper Yorkshire tea.\n\nWe're not just growing plants — we're growing memories.", "latency_ms": { "a": 7737, "b": 7501 }, "tokens_out": { "a": 334, "b": 218 }, "cost_usd": { "a": 0.0022864, "b": 0.005715 }, "winner": "tie", "judge_scores": { "a": 5, "b": 6.5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie (flipped on swap) — Response 1 failed the strict 150-word length constraint (it is 159 words). Response 2 hit exactly 150 words while beautifully incorporating all the requested details in a warm, professional tone. / Both responses include all the required details (family-run, Yorkshire, 1987, founders' daughter, rare perennials, small cafe). However, both fail the strict 150-word count constraint (Response 1 is 149 words, Response 2 is 159 words). They are tied in quality and constraint violation. | openai/gpt-5.6-terra: tie — Response 1 accurately covers all supplied details, is polished and useful, but has 151 body words. Response 2 is only 144 body words and invents unsupported names, a nursery operation, and café branding/menu details. Both miss the required 150-word count. / Both miss the required 150-word count: Response 1 has 149 words; Response 2 has 159. Response 2 accurately covers all provided facts without inventing names, provenance, or facilities. Response 1 is polished but adds unsupported specific details, including a business name, founders, nursery operation, and café name/menu. | deepseek/deepseek-v4-pro: tie (flipped on swap) — Response 2 is more concise (149 words) with specific details (names, café name) making it more personable and useful. Both exceed word count limits but 2 is closer, better adhering to instructions. / Response 1 exactly meets the 150-word limit and includes all required details. Response 2 exceeds the word count (157 words), violating the constraint, and is slightly less concise. Both are accurate and useful, but adherence to instructions gives Response 1 the edge.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 5, "score_b": 7 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": false, "score_a": 5, "score_b": 5 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "tie", "flipped_on_swap": true, "score_a": 5, "score_b": 7.5 } ] }, { "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": "**Beginner's Guide to Setting Up a Home Compost Bin**\n\nStarting a home compost bin turns kitchen scraps and yard waste into free, nutrient-rich soil amendment while cutting landfill waste. This straightforward guide covers location, what to add, what to avoid, timeline, and one key pitfall so you can succeed from day one.\n\n**Choose the right location.** Pick a level, well-drained spot that gets partial shade—full sun dries the pile too fast, while deep shade keeps it too wet and cold. Place the bin directly on bare soil (not concrete) so earthworms and microbes can enter naturally. Keep it convenient to your kitchen or garden path for easy access in all weather, yet a few meters from doors and windows to limit any temporary odors or curious pests. Ensure good airflow around the bin and space to turn or harvest the finished compost. A simple enclosed bin, tumbler, or open wire enclosure works; start small (about 1 cubic meter) if space is limited.\n\n**What to add.** Build a balanced mix of “greens” (nitrogen-rich) and “browns” (carbon-rich). Greens include fruit and vegetable peels, coffee grounds, tea bags, fresh grass clippings, and plant trimmings. Browns include dry leaves, shredded cardboard or newspaper (non-glossy), straw, wood chips, and eggshells. Aim for roughly 2–3 parts browns to 1 part greens by volume. Chop or tear larger pieces into 5–10 cm bits to speed breakdown. Lightly moisten as you layer so the pile feels like a wrung-out sponge—damp but not soggy. Occasional handfuls of finished compost or garden soil introduce helpful microbes.\n\n**What to avoid.** Never add meat, fish, bones, dairy, oils, or greasy foods—these attract pests and create foul smells. Skip pet waste, diseased plants, invasive weeds with seeds, coal ash, treated wood, glossy or colored paper, and any plastics or synthetics. These either fail to break down safely or introduce pathogens and toxins.\n\n**How long it takes.** Under good conditions (balanced materials, regular turning every 1–2 weeks, proper moisture, and warm weather), usable compost forms in 2–4 months. Cooler temperatures or a neglected pile can stretch the process to 6 months or longer. Finished compost is dark, crumbly, earthy-smelling, and no longer recognizable as the original scraps. Screen out any large bits and return them to the next batch.\n\n**One common mistake.** Beginners often overload the bin with wet greens and forget browns or aeration. The result is a slimy, anaerobic, smelly mess. Always layer or mix in dry browns and turn the pile to introduce oxygen—this keeps the process hot, fast, and odor-free.\n\nFollow these steps, monitor moisture and balance, and you will soon harvest black gold for your garden beds, pots, and lawn. Composting is forgiving; small adjustments quickly get a struggling pile back on track. Start today and enjoy the simple cycle of waste to wealth.", "output_b": "# A Beginner's Guide to Setting Up a Home Compost Bin\n\n## Choosing the Right Location\n\nThe first step to successful composting is finding the perfect spot. Place your compost bin on a level, well-drained area of your yard, ideally on bare soil rather than concrete or pavement. Direct contact with the ground allows beneficial microorganisms and earthworms to naturally migrate into your pile. Choose a spot that receives partial shade, as full sun can dry out your compost too quickly, while deep shade may keep it too cool and slow decomposition. Make sure the location is easily accessible from your kitchen so you'll actually use it consistently, even on rainy days.\n\n## What to Add\n\nSuccessful composting relies on balancing two categories of materials: **greens** (nitrogen-rich) and **browns** (carbon-rich). Aim for roughly a 3:1 ratio of browns to greens by volume.\n\n**Greens include:** fruit and vegetable scraps, coffee grounds and filters, fresh grass clippings, tea bags, and crushed eggshells.\n\n**Browns include:** dry leaves, cardboard, newspaper, straw, wood chips, sawdust from untreated wood, and small twigs.\n\nLayering these materials helps create airflow and promotes even decomposition. Always start with a base layer of coarse browns like twigs to encourage drainage.\n\n## What to Avoid\n\nCertain items can attract pests, create odors, or introduce harmful substances. **Never add** meat, fish, dairy products, cooked food with oils or sauces, pet waste from dogs or cats, diseased plants, or weeds that have gone to seed. Also avoid treated or painted wood, glossy printed paper, and any synthetic materials. These items can contaminate your compost or disrupt the decomposition process entirely.\n\n## How Long It Takes\n\nUnder ideal conditions — proper balance, regular turning, and adequate moisture — compost can be ready in **two to six months**. Without active management, a passive pile may take six months to two years. You'll know it's ready when the material is dark, crumbly, earthy-smelling, and unrecognizable from its original form. Turning your pile every one to two weeks with a pitchfork introduces oxygen and significantly speeds things up.\n\n## One Common Mistake\n\nThe most frequent beginner error is **making the pile too wet**. Excess moisture suffocates the aerobic bacteria responsible for decomposition, creating a slimy, foul-smelling mess. Your compost should feel like a wrung-out sponge — damp but not dripping. If it becomes waterlogged, simply mix in additional browns like shredded cardboard or dry leaves to restore balance.\n\nHappy composting!", "latency_ms": { "a": 37955, "b": 14926 }, "tokens_out": { "a": 1438, "b": 612 }, "cost_usd": { "a": 0.0088984, "b": 0.015535 }, "winner": "tie", "judge_scores": { "a": 5, "b": 5 }, "judge_reasoning": "google/gemini-3.1-pro-preview: tie — Both responses provide excellent, accurate, and well-structured guides covering all requested points. However, both fail the strict 400-word constraint (Response 1 is ~468 words, Response 2 is ~369 words). They tie on quality and usefulness. / Both responses fail the strict 400-word count constraint (Response 1 is ~395 words, Response 2 is ~470 words). Response 1 is chosen as the winner because its formatting with clear headings and bulleted lists makes it slightly more useful and readable for a busy professional. | openai/gpt-5.6-terra: tie — Both accurately cover all requested topics with practical beginner advice. Response 1 is more detailed, especially on setup, moisture, and troubleshooting, and is closer to the requested length. Response 2 is clear and well structured but less detailed. Both fail the explicit 400-word requirement. / Both cover all requested topics clearly, but neither is exactly 400 words. Response 1 is more concise, accurate, and practical. Response 2 is useful but less concise and inaccurately classifies eggshells as browns; its bare-soil advice also does not apply to tumblers. | deepseek/deepseek-v4-pro: abstain — judge call failed", "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": 5, "score_b": 5 }, { "judge": "openai/gpt-5.6-terra", "verdict": "tie", "flipped_on_swap": false, "score_a": 5, "score_b": 5 }, { "judge": "deepseek/deepseek-v4-pro", "verdict": "abstain", "flipped_on_swap": false, "score_a": 0, "score_b": 0 } ] } ] }