{ "battle": "deepseek-vs-gpt-api-value", "category": "Best-value API", "suite_version": "suite-2026-07", "contestants": { "A": { "name": "DeepSeek V4 Pro", "model": "deepseek/deepseek-v4-pro" }, "B": { "name": "GPT-5.6 Terra", "model": "openai/gpt-5.6-terra" } }, "judge": "anthropic/claude-sonnet-5", "judge_panel": [ "anthropic/claude-sonnet-5", "google/gemini-3.1-pro-preview", "x-ai/grok-4.5" ], "protocol": "Three judges from different labs. Each scores both position orders; a judge's verdict counts only if it survives its own swap. The task goes to the majority of the panel; no majority is a tie.", "proxy_note": null, "started_at": "2026-08-07T18:49:55.328Z", "finished_at": "2026-08-07T18:54:31.914Z", "scoreline": { "DeepSeek V4 Pro": 4, "GPT-5.6 Terra": 7, "ties": 19 }, "total_cost_usd": 1.2704, "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 bookkeeping trial – first month on us \n\nHi [First Name], \n\nI’m [Name], owner of [Firm Name]. We’re a small, 6-person accounting practice based in [Town/City], and we work exclusively with local independent businesses. \n\nI’ve followed [Brewery Name] from a distance – you’ve built something genuine here. I also know that running a brewery leaves little time for chasing receipts and reconciling invoices. \n\nWe’d like to offer you a free first month of bookkeeping. No charge, no obligation beyond that. If you find it useful, we can discuss a simple monthly arrangement. If not, we part ways with no hard feelings. \n\nWould a 15-minute call this week work to walk you through how we’d approach it? \n\nBest regards, \n[Name] \n[Firm Name] \n[Phone Number]", "output_b": "**Subject:** Free first month of bookkeeping for [Brewery Name]\n\nHi [Name],\n\nI’m [Your Name], owner of [Firm Name], a six-person accounting firm based in [Town].\n\nWe work with local businesses that need clear, reliable bookkeeping without adding more to their plate. For breweries, that can mean keeping on top of supplier invoices, stock costs, VAT and cash flow.\n\nI’d like to offer [Brewery Name] your first month of bookkeeping free, with no obligation to continue afterwards. We’ll get your records organised and give you a straightforward view of where things stand.\n\nWould you be open to a 15-minute call next week to see if we could help?\n\nBest regards, \n[Your Name] \n[Firm Name] \n[Phone]", "latency_ms": { "a": 3372, "b": 2444 }, "tokens_out": { "a": 189, "b": 162 }, "cost_usd": { "a": 0.0005429, "b": 0.001026 }, "winner": "tie", "judge_scores": { "a": 7.3, "b": 8.8 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both meet word limit, tone, single CTA, no buzzwords. Response 2 is slightly more specific to brewery needs (VAT, stock costs), adding relevance; Response 1 is slightly more personable. Both are clear and concise, near equal quality. / Both meet constraints and word count, friendly professional tone, clear CTA. Response 1 is more concise and universally applicable; Response 2's claim of 'following from a distance' feels slightly presumptuous/less generic but still fine. | google/gemini-3.1-pro-preview: B — Response 1 slightly exceeds the 120-word limit (approx 122 words). Response 2 follows all constraints, stays under the word limit, and provides a highly relevant, professional, and concise email tailored to a brewery. / Response 1 followed all instructions, including the strict word count limit, and provided a highly relevant, professional, and concise email. Response 2 exceeded the 120-word limit constraint. | x-ai/grok-4.5: tie — Both meet max 120 words, tone, single CTA, no buzzwords, and all specs accurately. Equally clear, useful, professional cold emails. / Both under 120 words, meet all specs (friendly/pro, one CTA, no buzzwords, free trial offer). Clear, useful cold emails of equal quality.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8, "score_b": 8.5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 5, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 } ] }, { "suite": "writing", "id": "w2", "name": "Product description", "prompt": "Write a 60-80 word product description for a stainless-steel insulated water bottle (750ml, keeps drinks cold 24h/hot 12h, leakproof, fits car cup holders). Target audience: gym-goers. Avoid cliches like 'stay hydrated in style'.", "output_a": "Your toughest sets demand a water bottle that works as hard as you do. This 750ml stainless-steel insulated bottle keeps water ice-cold for 24 hours, so the last sip is as crisp as the first. The leakproof lid survives being tossed into your gym bag, and the slim base fits standard car cup holders for the drive home. Double-wall vacuum insulation also locks in heat for 12 hours if you prefer a post-workout coffee. Built for grit, not glamour.", "output_b": "Built for training days, this 750ml stainless-steel insulated bottle keeps water cold for up to 24 hours and coffee or tea hot for 12. Its leakproof lid handles gym bags, locker shelves, and commutes without spills, while the slim base fits most car cup holders. Durable, reusable, and easy to carry, it delivers reliable temperature control from warm-up to recovery.", "latency_ms": { "a": 1962, "b": 6672 }, "tokens_out": { "a": 101, "b": 323 }, "cost_usd": { "a": 0.00032885, "b": 0.002002 }, "winner": "A", "judge_scores": { "a": 9, "b": 8.5 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both meet word count (77 and 60), avoid the banned cliche, and cover all required product features clearly and concisely for a gym audience. Response 2 is slightly more concise, but both are equally accurate and useful. / Both meet word count and avoid banned cliché, factually accurate. R1 is more concise and professional-friendly; R2 is slightly more flowery, bordering on cliché with 'grit, not glamour,' making it marginally less concise. | google/gemini-3.1-pro-preview: A — Both responses follow all instructions and meet the word count constraints. Response 1 is slightly better tailored to the gym-goer audience with more engaging, active language. / Both responses follow all instructions and constraints perfectly, including the word count. Response 2 is slightly better as its tone is more engaging and tailored specifically to the gym-goer target audience. | x-ai/grok-4.5: A — Both hit 60-80 words, cover all specs accurately, target gym-goers, avoid the cliche. Resp1 more vivid and gym-specific; Resp2 solid but plainer. / Both hit 60-80 words, cover all specs, target gym-goers, avoid banned cliche. R2 more vivid and audience-tuned without fluff.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 9.5, "score_b": 8.5 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 8 } ] }, { "suite": "writing", "id": "w3", "name": "Summarise messy notes", "prompt": "Turn these messy meeting notes into a clean 5-bullet summary a director could read in 20 seconds:\n\n'ok so launch slipped again, marc says supplier issue?? maybe 3 wks. sarah wants to push the email campaign anyway but tom thinks wait. budget - we're 8k over, mostly the packaging redo. Q: do we tell retail partners now or after new date confirmed. also NEED to hire the warehouse temp before august rush. next mtg tues.'", "output_a": "- Launch delayed ~3 weeks due to a supplier issue (per Marc). \n- Budget is $8K over, driven mostly by the packaging redesign. \n- Email campaign: Sarah wants to proceed now; Tom advises waiting until the new launch date is firm. \n- Retail partners: Decision needed on whether to inform them now or after the new date is confirmed. \n- Urgent: Hire warehouse temp before August rush. Next meeting Tuesday.", "output_b": "- Launch is delayed again due to a supplier issue; current estimate is approximately three weeks.\n- Marketing timing is unresolved: Sarah recommends proceeding with the email campaign, while Tom recommends waiting for a confirmed launch date.\n- The project is currently $8K over budget, primarily due to the packaging redesign.\n- Team needs to decide when to notify retail partners—immediately or once the revised launch date is confirmed.\n- Hire a warehouse temporary worker before the August peak; next meeting is Tuesday.", "latency_ms": { "a": 16728, "b": 2143 }, "tokens_out": { "a": 707, "b": 102 }, "cost_usd": { "a": 0.00091067088, "b": 0.000715 }, "winner": "tie", "judge_scores": { "a": 9.3, "b": 9 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie — Both accurately capture all key points in exactly 5 clear bullets, easily readable in 20 seconds. Very similar quality, wording slightly differs but both equally correct, concise, and useful. / Both accurately capture all key points in exactly 5 clear bullets, easily readable in 20 seconds. Response 2 is slightly more concise and scannable with bolded labels, but both are equally correct and complete. | google/gemini-3.1-pro-preview: A — Both responses perfectly follow the instructions and constraints, providing 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 follow all instructions and provide exactly 5 bullets. Response 2 is slightly more concise and uses bold-like structuring (e.g., 'Email campaign:', 'Retail partners:') which makes it easier for a busy director to scan in 20 seconds. | x-ai/grok-4.5: tie — Both deliver accurate, complete 5-bullet summaries matching all notes, equally clear/concise and director-ready. No constraints breached. / Both deliver accurate, complete 5-bullet summaries that are clear, concise, and director-ready. No material differences in fidelity or usability.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 } ] }, { "suite": "writing", "id": "w4", "name": "Tone rewrite", "prompt": "Rewrite this complaint reply so it is warm, takes responsibility, and keeps the same facts, in under 90 words:\n\n'Your order was delayed because of a system error on our side. The refund has been processed and will arrive in 3-5 days. We cannot offer further compensation. Let us know if you have questions.'", "output_a": "I’m so sorry for the delay with your order – it was caused by a system error on our end. We’ve processed a full refund, which should reach you in 3–5 business days. While we’re unable to offer additional compensation, we truly appreciate your understanding. Please let us know if you have any other questions.", "output_b": "We’re very sorry for the delay with your order. A system error on our side caused the issue, and we understand how frustrating this must have been. Your refund has now been processed and should arrive within 3–5 days. While we’re unable to offer additional compensation, we appreciate your patience and are here if you have any questions.", "latency_ms": { "a": 15884, "b": 4007 }, "tokens_out": { "a": 808, "b": 73 }, "cost_usd": { "a": 0.000698033364, "b": 0.000511 }, "winner": "B", "judge_scores": { "a": 8.3, "b": 9.2 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both responses accurately preserve facts, take responsibility warmly, and stay under 90 words. Response 2 slightly more empathetic with 'understand how frustrating this must have been.' Both concise and clear, minimal difference in quality. / Both are warm, concise, under 90 words, and retain facts. Response 2 adds \"full refund,\" an unsupported detail not in the original, slightly reducing factual accuracy. Response 1 stays strictly faithful to the facts while still being warm and empathetic. | google/gemini-3.1-pro-preview: B — Both responses follow all instructions and constraints perfectly. Response 2 is slightly better as it adds a touch more empathy ('we understand how frustrating...') while strictly maintaining the original facts without adding 'business' to the days. / Both responses meet all constraints and are under 90 words. Response 1 is slightly better as it sticks strictly to the original facts, whereas Response 2 adds 'business' to the days, which wasn't in the original text. | x-ai/grok-4.5: B — Both warm, responsible, under 90 words, keep core facts. R1 slightly alters facts (adds 'full', 'business days'). R2 more precise on facts plus empathy. Both clear/useful. / Both warm, responsible, under 90 words, clear. R1 keeps facts exact (no 'business'/'full'); R2 slightly alters them. Equal otherwise.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8, "score_b": 8.5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 8, "score_b": 9 } ] }, { "suite": "writing", "id": "w5", "name": "Headlines", "prompt": "Write 5 headline options (max 9 words each) for a blog post about how small restaurants can use AI to reduce food waste. Mix: 2 practical, 2 curiosity-driven, 1 with a number.", "output_a": "**Practical**\n1. AI tools that help small restaurants slash food waste.\n2. Cut food costs with simple AI-powered inventory tracking.\n\n**Curiosity-driven**\n3. Your fridge is leaking money. AI can stop it.\n4. The secret ingredient smart kitchens now rely on.\n\n**Number**\n5. 5 AI tricks to shrink your restaurant’s waste fast.", "output_b": "- How Small Restaurants Can Cut Food Waste With AI \n- Use AI to Track Waste and Save on Food Costs \n- The Surprising Way AI Helps Restaurants Waste Less \n- Can AI Predict What Your Customers Will Actually Order? \n- 5 Ways AI Can Reduce Restaurant Food Waste", "latency_ms": { "a": 5533, "b": 1627 }, "tokens_out": { "a": 75, "b": 60 }, "cost_usd": { "a": 0.000225069, "b": 0.000411 }, "winner": "A", "judge_scores": { "a": 8.7, "b": 5 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both meet category mix but each has one headline exceeding the 9-word limit (R1 #3=10 words, R2 #2=10 words). Content quality and clarity are comparable, so it's a tie. / Response 1 has a 10-word headline exceeding the 9-word limit, violating the constraint. Response 2 stays within limits, is well-organized with clear category labels, and offers creative, useful headlines for a non-technical audience. | google/gemini-3.1-pro-preview: A — Response 1 followed all instructions, including the word count constraint (max 9 words per headline) and clearly labeled the mix. Response 2 failed the word count constraint on the second headline (10 words). / Response 1 failed the maximum word count constraint (headline 2 is 10 words). Response 2 followed all instructions, including the word count constraint, and clearly labeled the categories as requested by the mix. | x-ai/grok-4.5: A — Resp1 meets all constraints with clear mix and labels. Resp2 exceeds 9-word limit on one headline; otherwise solid mix and useful. / Both cover topic and mix well. R1 one headline exceeds 9 words; unlabeled. R2 all compliant, clearly categorized, punchier and more useful.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 7, "score_b": 5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 5 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 5 } ] }, { "suite": "coding", "id": "c1", "name": "Bug fix", "prompt": "This JavaScript function should return the median of a numeric array but gives wrong answers for even-length arrays and mutates the input. Fix both issues, return only the corrected function with a one-line explanation:\n\nfunction median(arr) {\n arr.sort();\n const mid = Math.floor(arr.length / 2);\n return arr[mid];\n}", "output_a": "```javascript\nfunction median(arr) {\n const sorted = [...arr].sort((a, b) => a - b);\n const mid = Math.floor(sorted.length / 2);\n return sorted.length % 2 ? sorted[mid] : (sorted[mid - 1] + sorted[mid]) / 2;\n}\n```\n// Copies input, sorts numerically, and returns the average of the two middle values for even-length arrays.", "output_b": "function median(arr) {\n const sorted = [...arr].sort((a, b) => a - b);\n const mid = Math.floor(sorted.length / 2);\n return sorted.length % 2 ? sorted[mid] : (sorted[mid - 1] + sorted[mid]) / 2;\n}\n// Copies before numeric sorting and averages the two middle values for even-length arrays.", "latency_ms": { "a": 8886, "b": 1530 }, "tokens_out": { "a": 400, "b": 82 }, "cost_usd": { "a": 0.000360395412, "b": 0.000566 }, "winner": "tie", "judge_scores": { "a": 7.8, "b": 9.3 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie — Both responses provide identical correct fixes with one-line explanations. Response 1 uses code block formatting which is slightly cleaner, but functionally they are equivalent in correctness and clarity. / Both responses provide identical correct fixes with a one-line explanation as requested. Response 2 uses a markdown code block, slightly deviating from 'return only the corrected function' but functionally equivalent. Both are equally correct, clear, and useful. | google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses correctly fix the mutation and even-length issues, as well as the implicit numeric sorting bug. Both follow the constraint to return only the function and a one-line explanation. / Both responses correctly fix the bugs (mutation, numeric sorting, even-length logic) and provide a one-line explanation. Response 1 is slightly better as it strictly provides only the code and explanation without markdown formatting, adhering closer to the 'return only' constraint. | x-ai/grok-4.5: B — Both fix mutation via copy and even-length median via average with numeric sort. Resp1 adds markdown fences, violating 'only the corrected function'. Resp2 is clean and exact. / Both correctly fix mutation via copy and even-length median via average with numeric sort. R1 follows 'return only the function + one-line expl' exactly; R2 adds markdown fences.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.5, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 5, "score_b": 9 } ] }, { "suite": "coding", "id": "c2", "name": "Small feature", "prompt": "Write a Python function `chunk_invoices(invoices, max_total)` that takes a list of dicts like {'id': 'A1', 'amount': 120.5} and groups them into batches where each batch's summed amount does not exceed max_total. A single invoice larger than max_total goes in its own batch. Preserve order. Include 3 test cases using assert.", "output_a": "```python\ndef chunk_invoices(invoices, max_total):\n \"\"\"\n Groups a list of invoices into batches such that each batch's total amount\n does not exceed max_total. A single invoice larger than max_total is placed\n in its own batch. Order of invoices is preserved.\n\n Args:\n invoices: List of dicts with keys 'id' and 'amount'.\n max_total: Maximum allowed sum of amounts per batch (numeric).\n\n Returns:\n List of batches, where each batch is a list of invoices.\n \"\"\"\n batches = []\n current_batch = []\n current_sum = 0.0\n\n for inv in invoices:\n amount = float(inv['amount'])\n # If the current batch is empty, start a new batch\n if not current_batch:\n current_batch.append(inv)\n current_sum = amount\n # If this single invoice exceeds max_total, close the batch immediately\n if amount > max_total:\n batches.append(current_batch)\n current_batch = []\n current_sum = 0.0\n else:\n # Check if adding this invoice would exceed the limit\n if current_sum + amount <= max_total:\n current_batch.append(inv)\n current_sum += amount\n else:\n # Close the current batch and start a new one with this invoice\n batches.append(current_batch)\n current_batch = [inv]\n current_sum = amount\n # If the new invoice alone exceeds max_total, close it immediately\n if amount > max_total:\n batches.append(current_batch)\n current_batch = []\n current_sum = 0.0\n\n # Don't forget the last batch\n if current_batch:\n batches.append(current_batch)\n\n return batches\n\n\nif __name__ == '__main__':\n # Test case 1: Normal case, batches split due to limit\n invoices1 = [\n {'id': 'A1', 'amount': 120.5},\n {'id': 'A2', 'amount': 50},\n {'id': 'A3', 'amount': 80}\n ]\n expected1 = [\n [{'id': 'A1', 'amount': 120.5}],\n [{'id': 'A2', 'amount': 50}, {'id': 'A3', 'amount': 80}]\n ]\n assert chunk_invoices(invoices1, 150) == expected1, \"Test case 1 failed\"\n\n # Test case 2: Single invoice exceeding max_total\n invoices2 = [\n {'id': 'B1', 'amount': 200}\n ]\n expected2 = [\n [{'id': 'B1', 'amount': 200}]\n ]\n assert chunk_invoices(invoices2, 150) == expected2, \"Test case 2 failed\"\n\n # Test case 3: Mix of normal, exceeding, and small invoices\n invoices3 = [\n {'id': 'C1', 'amount': 10},\n {'id': 'C2', 'amount': 200},\n {'id': 'C3', 'amount': 20},\n {'id': 'C4', 'amount': 5}\n ]\n expected3 = [\n [{'id': 'C1', 'amount': 10}],\n [{'id': 'C2', 'amount': 200}],\n [{'id': 'C3', 'amount': 20}, {'id': 'C4', 'amount': 5}]\n ]\n assert chunk_invoices(invoices3, 100) == expected3, \"Test case 3 failed\"\n\n print(\"All test cases passed.\")\n```", "output_b": "```python\ndef chunk_invoices(invoices, max_total):\n \"\"\"\n Group invoices into ordered batches whose total amount is at most max_total.\n\n An invoice whose amount is greater than max_total is placed in its own batch.\n \"\"\"\n batches = []\n current_batch = []\n current_total = 0\n\n for invoice in invoices:\n amount = invoice[\"amount\"]\n\n # Oversized invoices always get their own batch.\n if amount > max_total:\n if current_batch:\n batches.append(current_batch)\n current_batch = []\n current_total = 0\n\n batches.append([invoice])\n continue\n\n # Start a new batch if adding this invoice would exceed the limit.\n if current_batch and current_total + amount > max_total:\n batches.append(current_batch)\n current_batch = []\n current_total = 0\n\n current_batch.append(invoice)\n current_total += amount\n\n if current_batch:\n batches.append(current_batch)\n\n return batches\n\n\n# Test 1: Normal batching\ninvoices = [\n {\"id\": \"A1\", \"amount\": 120.5},\n {\"id\": \"A2\", \"amount\": 50},\n {\"id\": \"A3\", \"amount\": 80},\n {\"id\": \"A4\", \"amount\": 30},\n]\nassert chunk_invoices(invoices, 200) == [\n [{\"id\": \"A1\", \"amount\": 120.5}, {\"id\": \"A2\", \"amount\": 50}],\n [{\"id\": \"A3\", \"amount\": 80}, {\"id\": \"A4\", \"amount\": 30}],\n]\n\n# Test 2: Oversized invoice gets its own batch\ninvoices = [\n {\"id\": \"A1\", \"amount\": 40},\n {\"id\": \"A2\", \"amount\": 250},\n {\"id\": \"A3\", \"amount\": 60},\n]\nassert chunk_invoices(invoices, 100) == [\n [{\"id\": \"A1\", \"amount\": 40}],\n [{\"id\": \"A2\", \"amount\": 250}],\n [{\"id\": \"A3\", \"amount\": 60}],\n]\n\n# Test 3: Empty input\nassert chunk_invoices([], 100) == []\n```", "latency_ms": { "a": 26948, "b": 3540 }, "tokens_out": { "a": 2244, "b": 479 }, "cost_usd": { "a": 0.00274100544, "b": 0.002958 }, "winner": "B", "judge_scores": { "a": 8.7, "b": 9.5 }, "judge_reasoning": "anthropic/claude-sonnet-5: B — Both correctly implement logic and pass tests. Response 2 is more concise, cleaner, includes edge case (empty input), while Response 1 is more verbose with redundant logic. Both satisfy constraints equally well. / Both implementations are correct and pass their asserts. Response 1 is slightly cleaner, with simpler logic and top-level asserts as requested, while Response 2's logic is more convoluted with redundant checks and wraps tests in a main guard. | google/gemini-3.1-pro-preview: B — Both responses correctly implement the requested function and include the required test cases. Response 2 is the winner because its logic is cleaner, more concise, and easier to read compared to the slightly repetitive logic in Response 1. / Both responses correctly implement the requested function and include the required test cases. Response 1 is slightly better because its logic for handling the chunking is cleaner and more concise. | x-ai/grok-4.5: tie (flipped on swap) — Both correctly implement ordered batching with oversized singles and 3 asserts. R2 is cleaner, more concise, and handles edge cases elegantly without redundancy. / Both implement correct order-preserving batching with oversized handling and exactly 3 asserts. Equally clear, useful, and compliant.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "B", "flipped_on_swap": false, "score_a": 8, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 9.5 } ] }, { "suite": "coding", "id": "c3", "name": "Explain code", "prompt": "Explain to a junior developer, in under 150 words, what this code does and one risk of using it:\n\nconst cache = new Map();\nfunction memo(fn) {\n return (...args) => {\n const k = JSON.stringify(args);\n if (!cache.has(k)) cache.set(k, fn(...args));\n return cache.get(k);\n };\n}", "output_a": "This code creates a simple memoization helper. `memo` takes a function `fn` and returns a new function that caches results. When the returned function is called, it converts the arguments into a string key using `JSON.stringify`. If that key isn’t in the global `cache` Map, it calls `fn`, stores the result, and then returns it. On subsequent calls with the same arguments, it skips the computation and returns the cached value.\n\n**One risk:** The single global `cache` is shared across *all* memoized functions. This can cause **key collisions**—two different functions called with the same arguments (e.g., `fnA(1,2)` and `fnB(1,2)`) will overwrite each other’s cached results, returning wrong data. It also creates a **memory leak** because the cache never clears, growing indefinitely as new argument combinations are encountered.", "output_b": "This code creates a simple memoization helper: it remembers function results so repeated calls with the same arguments can return the cached value instead of running the function again.\n\n- `cache` stores results in a `Map`.\n- `memo(fn)` returns a wrapped version of `fn`.\n- Arguments are converted to a JSON string (`k`) and used as the cache key.\n- If that key is missing, it calls `fn(...args)` and saves the result.\n- Later calls with the same serialized arguments return the saved result.\n\nExample: wrapping an expensive calculation can make repeated calls faster.\n\n**Risk:** The cache is global and never clears, so it can grow forever and cause memory leaks. Also, `JSON.stringify` may produce unreliable keys for some values, such as functions, `undefined`, circular objects, or objects whose property order differs.", "latency_ms": { "a": 10122, "b": 3065 }, "tokens_out": { "a": 371, "b": 174 }, "cost_usd": { "a": 0.00033929304, "b": 0.001123 }, "winner": "tie", "judge_scores": { "a": 9.2, "b": 8.5 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both accurate and concise. Response 2 covers more nuanced risks (JSON.stringify limitations) plus memory leak, slightly more useful; Response 1 mentions collision issue uniquely valid too. Both under 150 words. Response 2 slightly more comprehensive and clear. / Both correctly explain memoization within word limit. Response 2 identifies a more critical and accurate risk (shared global cache causing collisions across different functions), which Response 1 omits, making it slightly more useful and precise. | google/gemini-3.1-pro-preview: A — Response 1 correctly identifies the critical bug in the provided code (the global cache is shared across all memoized functions, causing collisions), making it highly educational for a junior developer. Both meet the word count constraint. / Both responses explain the code clearly and stay under the word limit. Response 2 wins because it correctly identifies a critical, specific bug in this implementation: the global cache is shared across all memoized functions, leading to key collisions. | x-ai/grok-4.5: tie (flipped on swap) — Both accurately explain memoization and a key risk (shared cache/memory), stay under 150 words, and suit juniors clearly. Equal quality. / Both accurate, under 150 words, clear for juniors. R2 better highlights shared-cache collisions as primary risk; R1 solid but less precise on that.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 8.5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 8.5 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 8.5 } ] }, { "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 customers c\n INNER JOIN orders o ON c.id = o.customer_id\n WHERE EXTRACT(YEAR FROM o.created_at) = 2025\n GROUP BY c.country, c.name, c.id -- include id in group to handle same name\n),\nranked_customers AS (\n SELECT\n country,\n name,\n total_spend,\n DENSE_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_customers\nWHERE rank <= 3\nORDER BY country, rank;\n```", "output_b": "```sql\nWITH customer_spend AS (\n SELECT\n c.country,\n c.name,\n c.id AS customer_id,\n SUM(o.total) AS total_spend\n FROM customers c\n JOIN orders o\n ON o.customer_id = c.id\n WHERE o.created_at >= DATE '2025-01-01'\n AND o.created_at < DATE '2026-01-01'\n GROUP BY c.country, c.name, c.id\n),\nranked_customers AS (\n SELECT\n country,\n name,\n total_spend,\n RANK() OVER (\n PARTITION BY country\n ORDER BY total_spend DESC\n ) AS rank\n FROM customer_spend\n)\nSELECT\n country,\n name,\n total_spend,\n rank\nFROM ranked_customers\nWHERE rank <= 3\nORDER BY country, rank, name;\n```", "latency_ms": { "a": 17602, "b": 2754 }, "tokens_out": { "a": 921, "b": 191 }, "cost_usd": { "a": 0.0007738848, "b": 0.001214 }, "winner": "B", "judge_scores": { "a": 8.3, "b": 9.2 }, "judge_reasoning": "anthropic/claude-sonnet-5: B — Both correct and use window functions. Response 2 uses date range instead of EXTRACT, which is more index-friendly and performant, a minor best-practice advantage. Otherwise very similar in clarity and correctness. / Both correct and functional. Response 1 uses RANK() and date range (index-friendly, more standard for top-N ties). Response 2 uses EXTRACT (less index-friendly) and DENSE_RANK, which could yield more than 3 rows per country if ties exist, slightly less accurate for 'top 3'. | google/gemini-3.1-pro-preview: B — Both responses provide correct SQL queries that meet all requirements. Response 2 is slightly better because its date filtering approach (using >= and <) is sargable and allows the database to use indexes efficiently, whereas Response 1's use of EXTRACT() prevents index usage. / Both responses provide correct SQL queries that meet all requirements. Response 1 is slightly better because its date filtering approach is sargable (index-friendly), whereas Response 2 uses EXTRACT which prevents index usage on the created_at column. | x-ai/grok-4.5: tie (flipped on swap) — Both correctly use window functions for top-3 per country on 2025 spend with required columns. Minor differences in date filter and RANK vs DENSE_RANK are equally valid. Clear, concise, useful SQL. / Both correct single PG queries using window fn for top-3 2025 spend by country. R1 better date filter + RANK + stable ORDER; R2 uses EXTRACT/DENSE_RANK (minor). No constraints broken. Clear useful SQL.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "B", "flipped_on_swap": false, "score_a": 7.5, "score_b": 8.5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 9 } ] }, { "suite": "coding", "id": "c5", "name": "Regex", "prompt": "Write a regex that matches UK postcodes like 'SW1A 1AA', 'M1 1AE', 'B33 8TH' (allow lowercase and optional space), and a one-line JavaScript example using it to validate a form field. Briefly note one edge case your regex does NOT handle.", "output_a": "**Regex:** \n```regex\n/^[A-Z]{1,2}[0-9][A-Z0-9]? ?[0-9][A-Z]{2}$/i\n```\n- `^[A-Z]{1,2}` – one or two letters (outward area) \n- `[0-9]` – a digit (outward district) \n- `[A-Z0-9]?` – optional letter or digit (for codes like SW1A or B33) \n- ` ?` – optional space \n- `[0-9][A-Z]{2}$` – inward code: one digit followed by two letters \n- `/i` flag makes it case‑insensitive (accepts lowercase)\n\n**One‑line JavaScript validation example:**\n```javascript\nconst isValid = /^[A-Z]{1,2}[0-9][A-Z0-9]? ?[0-9][A-Z]{2}$/i.test(document.getElementById('postcode').value);\n```\n\n**Edge case not handled:** \nThe regex does **not** reject postcodes that use letters officially excluded by Royal Mail (e.g., `Q` in the first position, or `C`, `I`, `K`, `M`, `O`, `V` in the inward code). It would accept `Q1 1AA` even though `Q` is never used as the initial letter.", "output_b": "```regex\n/^(?:GIR ?0AA|(?:[A-PR-UWYZ]\\d[\\dA-HJKSTUW]?|[A-PR-UWYZ][A-HK-Y]\\d[\\dABEHMNPRVWXY]?) ?\\d[ABD-HJLNP-UW-Z]{2})$/i\n```\n\n```js\nconst isValidPostcode = /^(?:GIR ?0AA|(?:[A-PR-UWYZ]\\d[\\dA-HJKSTUW]?|[A-PR-UWYZ][A-HK-Y]\\d[\\dABEHMNPRVWXY]?) ?\\d[ABD-HJLNP-UW-Z]{2})$/i.test(document.querySelector('[name=\"postcode\"]').value.trim());\n```\n\nThis accepts lowercase and an optional single space, e.g. `sw1a1aa` or `SW1A 1AA`.\n\nEdge case: it does not handle special non-geographic formats such as `BFPO` military postcodes.", "latency_ms": { "a": 40270, "b": 11647 }, "tokens_out": { "a": 1879, "b": 737 }, "cost_usd": { "a": 0.0015826779, "b": 0.004493 }, "winner": "tie", "judge_scores": { "a": 8, "b": 8.5 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both meet requirements; Response 2's regex is more accurate to official UK postcode rules, providing a better edge case note. Response 1 is simpler but less precise. Both provide valid JS one-liners and edge case notes as required. / Both provide valid regex, JS one-liner, and edge case note. Response 1's regex is more accurate to real UK postcode rules; Response 2's is simpler but explicitly notes its own limitation. Both fulfill instructions well; roughly equal quality. | google/gemini-3.1-pro-preview: tie (flipped on swap) — Response 1 provides a simpler, easier-to-understand regex with helpful explanations, making it more useful for a non-technical professional. Response 2 provides a highly complex (though more strictly accurate) regex without explanation. / Response 1 provides a highly accurate, production-ready regex for UK postcodes and perfectly follows all instructions. Response 2 provides a naive regex that allows many invalid formats, though it correctly identifies this flaw as its edge case. | x-ai/grok-4.5: tie (flipped on swap) — Both meet all requirements with working regex/JS/edge note. R2 more accurate/complete for real UK formats; R1 simpler but looser. / Both meet all task requirements with working regexes, JS one-liners, and edge notes. R1 more precise on real UK rules; R2 clearer/explanatory for non-tech users. Equal overall.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 7.5, "score_b": 8 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8, "score_b": 8.5 } ] }, { "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": "**Current weekly profit:**\n- Revenue: 450 loaves × £3.20 = £1,440\n- Ingredients: 450 × £1.10 = £495\n- Labour: 450 × £0.90 = £405\n- Fixed costs: £480\n- Total costs: £495 + £405 + £480 = £1,380\n- **Profit: £1,440 – £1,380 = £60**\n\n**Proposed deal:**\n- Commit to 600 loaves’ worth of ingredients at £0.85/loaf → ingredients cost = 600 × £0.85 = £510 (80 loaves’ worth wasted)\n- Maximum output rises to 520 loaves with an extra £60 total labour cost → total labour = £405 + £60 = £465\n- Revenue: 520 × £3.20 = £1,664\n- Fixed costs: £480 (unchanged)\n- Total costs: £510 + £465 + £480 = £1,455\n- **Profit: £1,664 – £1,455 = £209**\n\n**Recommendation:** Accept the deal. Weekly profit increases from £60 to £209, a gain of £149. Even with wasted ingredients and higher labour cost, the lower ingredient price and extra sales volume more than compensate.", "output_b": "### Option 1: Stay with current arrangement (450 loaves)\n\n- Sales revenue: \n \\(450 \\times £3.20 = £1,440\\)\n\n- Ingredient cost: \n \\(450 \\times £1.10 = £495\\)\n\n- Labour cost: \n \\(450 \\times £0.90 = £405\\)\n\n- Fixed costs: £480\n\n**Weekly profit:**\n\n\\[\n£1,440 - £495 - £405 - £480 = \\boxed{£60}\n\\]\n\n---\n\n### Option 2: Take supplier deal and produce/sell 520 loaves\n\nThe bakery must buy ingredients for 600 loaves:\n\n- Ingredient purchase cost: \n \\(600 \\times £0.85 = £510\\)\n\nEven though only 520 loaves are made, the other 80 loaves’ worth of ingredients are wasted.\n\n- Sales revenue: \n \\(520 \\times £3.20 = £1,664\\)\n\n- Ingredients: £510 \n- Labour for 520 loaves: \n \\(520 \\times £0.90 = £468\\)\n\n- Additional labour cost for raising output: £60 \n- Fixed costs: £480\n\n**Weekly profit:**\n\n\\[\n£1,664 - £510 - £468 - £60 - £480\n= \\boxed{£146}\n\\]\n\n---\n\n### Comparison\n\n| Option | Weekly profit |\n|---|---:|\n| Continue at 450 loaves | £60 |\n| Take deal and sell 520 loaves | £146 |\n\nTaking the deal and increasing production to 520 loaves increases weekly profit by:\n\n\\[\n£146 - £60 = \\boxed{£86\\text{ per week}}\n\\]\n\n### Recommendation\n\n**Yes—take the supplier deal, provided the bakery can sell all 520 loaves each week.** Despite wasting ingredients for 80 loaves, the lower ingredient price and extra sales raise profit from £60 to £146 per week.\n\nIf the bakery could only continue selling 450 loaves, the deal would not be attractive: it would still have to pay £510 for ingredients rather than the current £495, with no additional sales to offset that cost.", "latency_ms": { "a": 207390, "b": 9970 }, "tokens_out": { "a": 2128, "b": 835 }, "cost_usd": { "a": 0.001813148904, "b": 0.005137 }, "winner": "tie", "judge_scores": { "a": 7.3, "b": 7.7 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Response 1 correctly treats £60 as total extra labour cost (£405+£60=£465), matching task wording. Response 2 double-counts labour (per-loaf £468 plus extra £60), producing an incorrect profit of £146 instead of correct £209. Response 1 is more accurate and equally clear. / Response 1 correctly scales labour cost with loaves produced (520×£0.90) plus the extra £60, giving profit £146. Response 2 incorrectly keeps labour at the 450-loaf base (£405) plus £60, underestimating labour cost and inflating profit to £209, which is less accurate given labour is stated per loaf. | google/gemini-3.1-pro-preview: A — Response 1 correctly interprets the '£60/week extra labour cost overall' as the total additional labour cost. Response 2 incorrectly applies the per-unit labour cost to the new volume and then adds the £60, double-counting the extra labour. / Response 2 correctly interprets '£60/week extra labour cost overall' as an addition to the current total labour cost, whereas Response 1 adds it on top of a recalculated per-loaf cost. Response 2 is also more concise and easier for a busy professional to read. | x-ai/grok-4.5: B — R1 undercounts labour (misses variable cost on extra loaves), yielding wrong £209 profit. R2 correctly applies 520×£0.90 + £60, profit £146. Both show calcs and recommend clearly; R2 more precise and useful. / Resp1 correctly computes both profits (£60 vs £146) with proper per-loaf labour + £60 extra. Resp2 understates labour (405+60 instead of 468+60), yielding wrong £209. Both show calcs + clear rec; 1 more accurate/useful.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 7, "score_b": 6.5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 9.5, "score_b": 7.5 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 5.5, "score_b": 9 } ] }, { "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": "Here’s a 7‑day launch plan, one line per day with the owner, followed by the riskiest dependency.\n\n**Day 1 – Pre‑launch teaser email** \n*Owner: Founder* – Send a “coming this week” email to the full 2,000 list highlighting the course promise and inviting replies to build anticipation.\n\n**Day 2 – Ad setup and audience seeding** \n*Owner: VA* – Create one retargeting ad set (£150) targeting website visitors and email clicks, and a lookalike ad set (£150) from the email list; ads go live at midday.\n\n**Day 3 – Cart‑open email with early‑bird bonus** \n*Owner: Founder* – Send the launch email revealing the course, limited early‑bird price, and a time‑sensitive bonus; include a direct checkout link.\n\n**Day 4 – Social‑proof push** \n*Owner: VA* – Post a pre‑recorded student testimonial video on social media and send a short email to non‑openers of Day 3, resharing the testimonial and offer.\n\n**Day 5 – Mid‑launch Q&A + retargeting boost** \n*Owner: Founder* – Host a 30‑minute live Q&A session (recorded), then send the replay link to the list; VA reallocates remaining ad budget to the best‑performing ad set.\n\n**Day 6 – Scarcity reminder email** \n*Owner: Founder* – Send an email stressing the early‑bird deadline (24 hours left) and highlighting what students risk missing out on.\n\n**Day 7 – Final “doors closing” email and wrap‑up** \n*Owner: Founder* – Send the last‑chance email, then after the deadline, email the list with a “thank you” and a link to the standard‑price course page; VA pauses all ads.\n\n**Single riskiest dependency** \nThe email open rate on Day 3’s cart‑open email. If the list isn’t engaged and the first sales email sinks (e.g. <15% open rate), there’s no time or budget to rebuild momentum, and the entire launch revenue will fall short with no backup channel.", "output_b": "- **Day 1 — Founder:** Finalise offer, pricing, landing page, checkout, guarantee and FAQ; **VA (2h):** proofread pages, set up tracking pixels/UTMs, test purchase flow. \n- **Day 2 — Founder:** Send launch announcement email to 2,000 subscribers and post across primary social channels; **VA (1h):** schedule reposts and monitor replies/comments. \n- **Day 3 — Founder:** Run live/recorded “why this course” Q&A or webinar and send replay/follow-up email; **VA (2h):** manage registrations, chat/questions and replay link. \n- **Day 4 — Founder:** Launch £150 retargeting/warm-audience ads using best-performing course benefit; **VA (1h):** check spend, link accuracy and basic ad comments. \n- **Day 5 — Founder:** Send value/case-study email addressing the main objection; post testimonial or course preview; **VA (1h):** format testimonial assets and update FAQ from questions received. \n- **Day 6 — Founder:** Review sales, email clicks and ad results; shift remaining £150 to the strongest ad/angle and personally follow up with high-intent replies; **VA (1h):** compile simple performance dashboard. \n- **Day 7 — Founder:** Send final “doors close/launch price ends tonight” email sequence (morning and final-hours reminder) and post urgency updates; **VA (2h):** monitor support inbox, checkout issues and final social comments. \n\n**Single riskiest dependency:** The landing page and checkout must work flawlessly before the first email send; a broken or confusing purchase path would waste the limited list attention and ad budget.", "latency_ms": { "a": 24848, "b": 6613 }, "tokens_out": { "a": 1190, "b": 450 }, "cost_usd": { "a": 0.0009929088, "b": 0.002771 }, "winner": "tie", "judge_scores": { "a": 6, "b": 7.3 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie — Both provide clear 7-day plans with owners per line and a flagged risk, meeting instructions. Response 2 better respects VA's 10h limit explicitly; Response 1 doesn't track VA hours. Both are clear and useful; quality is comparable overall. / Both provide clear 7-day plans with owners and a risk flag, meeting instructions well. Response 1's risk (checkout) is arguably more foundational/critical than Response 2's (open rate), but both are valid and well-structured. Slight edge is negligible; overall quality comparable. | google/gemini-3.1-pro-preview: B — Response 2 followed all instructions, including the strict 'one line per day' constraint, whereas Response 1 used multiple lines per day. Response 2 is also highly practical and clearly accounts for the VA's limited hours. / Response 1 followed all instructions, including the strict 'one line per day' constraint. Response 2 used multiple lines per day (a header and a description). Response 1 also effectively utilized the VA's 10 hours and the £300 budget. | x-ai/grok-4.5: tie — Both violate strict one-line-per-day format with multi-line entries. R2 better allocates limited VA hours, covers prep/checkout risk more usefully for non-tech founder, slightly clearer ownership. / Both violate strict one-line-per-day format with multi-line entries. R1 better allocates limited VA hours and ad budget across days, more realistic owner split, stronger risk flag on checkout. R2 front-loads ads poorly and risks list fatigue.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": false, "score_a": 8, "score_b": 8 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 5, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 5, "score_b": 5 } ] }, { "suite": "reasoning", "id": "r3", "name": "Trade-off analysis", "prompt": "A 12-person agency must choose: (A) hire a mid-level developer at £55k, or (B) contract overflow work to freelancers at roughly £400/day, expected 60 days/year. Give a recommendation in under 200 words covering cost, flexibility, quality risk, and one non-obvious factor.", "output_a": "**Recommendation: Contract freelancers (Option B).**\n\n**Cost:** At £400/day for 60 days, annual cost is £24k. Hiring a mid-level developer costs £55k salary, plus employer NI, pension, equipment, and overhead (often 1.25× salary, ~£69k). Option B is significantly cheaper and matches spend directly to demand – you only pay when overflow occurs.\n\n**Flexibility:** Freelancers let you scale up instantly for spikes without carrying permanent headcount during lean periods. If the 60-day estimate changes, you adjust – unlike a full-time hire who may be underutilised or require redundancy costs if work dries up.\n\n**Quality risk:** Vetted freelancers can deliver specialist skills on demand, but quality varies and they lack institutional knowledge. A full-time developer builds deep understanding of your clients and codebase, reducing rework over time. For discrete overflow tasks, a clear brief and a trusted freelancer network mitigates this risk.\n\n**Non-obvious factor:** Management overhead – freelancers require detailed briefs, handover, and review time from your permanent team, potentially consuming 10–20% of the contracted days. If that erodes billable hours from senior staff, the true cost might narrow the gap. Conversely, a permanent hire frees seniors to focus on higher-value work. Weigh this hidden drain against the cash saving. For occasional, project-based overflow, freelancers remain the pragmatic choice.", "output_b": "**Recommendation: choose B, freelancers, for now.**\n\nAt 60 days/year, freelancer cost is about **£24k** (£400 × 60), materially below a £55k employee. Once employer NI, pension, equipment, training, leave and management time are included, a hire is more likely **£65k–£75k/year**.\n\nFreelancers offer flexibility: scale work up/down with demand and access specialist skills without committing to a permanent role. A hire becomes more economical only if demand is sustained—roughly **150–180 contractor days/year**, depending on employment overheads.\n\nThe main downside is quality and continuity risk: contractors may need more briefing, produce inconsistent work, or leave before knowledge is transferred. Mitigate through a vetted roster, standardised code review/documentation, and assigning an internal owner.\n\nA non-obvious factor: **management bandwidth**. In a 12-person agency, repeatedly scoping, onboarding and reviewing freelancers can consume senior staff time; track that cost. If overflow becomes predictable or starts distracting leadership, revisit a permanent hire.", "latency_ms": { "a": 24959, "b": 5812 }, "tokens_out": { "a": 1091, "b": 327 }, "cost_usd": { "a": 0.0009134112, "b": 0.002033 }, "winner": "B", "judge_scores": { "a": 6.2, "b": 9 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie — Both cover cost, flexibility, quality risk, and a non-obvious factor (management overhead) within word limit, recommending B with sound reasoning. Response 2 adds a useful breakeven threshold, slightly more actionable and concise; Response 1 slightly more detailed. Very close quality overall. / Both cover cost, flexibility, quality risk, and a non-obvious factor (management overhead) within word limit, with similar accuracy and clarity. Response 2 is slightly more structured and quantifies overhead drain, but both are comparably strong and concise. | google/gemini-3.1-pro-preview: B — Response 1 exceeds the 200-word limit constraint. Response 2 follows all instructions, including the word count, and provides a clear, concise, and highly useful recommendation for a non-technical professional. / Response 1 perfectly follows all instructions, including the strict word count constraint, and provides a clear, concise, and highly useful recommendation. Response 2 is also excellent in content but violates the negative constraint by exceeding the 200-word limit. | x-ai/grok-4.5: B — Both accurate on costs/flex/quality/mgmt factor and useful. R1 exceeds 200 words (~248); R2 stays under (~168) while clearer/concise. / Both accurate, cover all required topics well with sound non-obvious factor. R1 more concise and natural; R2 structured but slightly longer/wordier. R2 exceeds 200-word limit.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": false, "score_a": 8.5, "score_b": 8.5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 5, "score_b": 9.5 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 5, "score_b": 9 } ] }, { "suite": "reasoning", "id": "r4", "name": "Maths word problem", "prompt": "A tank holds 2,400 litres. Pump A fills at 40 L/min. Pump B drains at 25 L/min. A runs from 09:00. B accidentally switches on at 09:20. At 10:00 B is switched off. At what time is the tank full? Show your working.", "output_a": "The tank becomes full at **10:25**.\n\n**Step-by-step working:**\n\n1. **09:00 – 09:20 (20 minutes):** \n Only Pump A is running. \n Water added = 40 L/min × 20 min = **800 L**.\n\n2. **09:20 – 10:00 (40 minutes):** \n Both pumps run. Net fill rate = 40 − 25 = 15 L/min. \n Water added = 15 L/min × 40 min = **600 L**. \n Total water at 10:00 = 800 + 600 = **1400 L**.\n\n3. **After 10:00:** \n Pump B is off. Remaining capacity = 2400 − 1400 = **1000 L**. \n Time to fill at 40 L/min = 1000 ÷ 40 = **25 minutes**. \n 10:00 + 25 minutes = **10:25**.", "output_b": "Assuming the tank is empty at 09:00:\n\n- **09:00–09:20:** Pump A fills alone for 20 minutes \n \\[\n 20 \\times 40 = 800\\text{ L}\n \\]\n\n- **09:20–10:00:** Both pumps run for 40 minutes. \n Net filling rate:\n \\[\n 40-25=15\\text{ L/min}\n \\]\n Amount added:\n \\[\n 40 \\times 15=600\\text{ L}\n \\]\n\nBy 10:00, the tank contains:\n\\[\n800+600=1400\\text{ L}\n\\]\n\nRemaining capacity:\n\\[\n2400-1400=1000\\text{ L}\n\\]\n\nAfter 10:00, only Pump A runs at 40 L/min, so time needed is:\n\\[\n\\frac{1000}{40}=25\\text{ minutes}\n\\]\n\nTherefore, the tank is full at **10:25**.", "latency_ms": { "a": 13238, "b": 13131 }, "tokens_out": { "a": 661, "b": 306 }, "cost_usd": { "a": 0.000576384396, "b": 0.001909 }, "winner": "tie", "judge_scores": { "a": 9.7, "b": 9.3 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie — Both responses correctly compute the same answer (10:25) with identical, clear working. Response 2 explicitly states an assumption, which is a minor plus, but both are equally correct, clear, and useful. / Both responses correctly compute the same answer (10:25) with identical, clear working. No constraints violated. Equally useful and concise. | google/gemini-3.1-pro-preview: A — Both responses correctly solve the math problem step-by-step. Response 1 is slightly better for a non-technical professional as it avoids LaTeX formatting, making it cleaner and easier to read. / Both responses correctly solve the math problem and show their working. Response 2 is slightly better for a non-technical professional as it avoids LaTeX math formatting, making it easier to read. | x-ai/grok-4.5: tie — Both correctly compute 10:25 with clear identical working; no constraints breached; equally useful and concise. / Both correctly compute 800L + 600L = 1400L by 10:00 then 25 min more to reach 2400L at 10:25. Identical accuracy, clarity, and usefulness; no constraints violated.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 } ] }, { "suite": "reasoning", "id": "r5", "name": "Catch the flaw", "prompt": "Find the flaw in this argument and explain it in under 100 words: 'Our churn dropped from 6% to 3% after we introduced the loyalty programme in March, so the programme cut churn in half. We should double the loyalty budget to cut churn to 1.5%.'", "output_a": "The flaw is assuming correlation equals causation, while ignoring **regression to the mean**. The 6% churn rate may have been an unusually high outlier, and the drop to 3% could be a natural return to the average, regardless of the loyalty programme. Additionally, the argument assumes a perfectly linear return on investment—doubling the budget won’t necessarily halve the remaining churn, as there’s likely a saturation point where low-hanging fruit has already been captured.", "output_b": "The argument mistakes correlation for causation: churn may have fallen because of seasonality, pricing changes, customer mix, or other factors—not necessarily the loyalty programme. It also assumes the observed effect is entirely attributable to the programme and scales linearly with spending. Without a control group, before/after adjustment, or experiment, neither “cut churn in half” nor “double budget to reach 1.5%” is justified.", "latency_ms": { "a": 8142, "b": 1874 }, "tokens_out": { "a": 100, "b": 90 }, "cost_usd": { "a": 0.00023229, "b": 0.000606 }, "winner": "B", "judge_scores": { "a": 8.5, "b": 9 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both correctly identify correlation/causation flaw and linear extrapolation issue, both under 100 words. Response 1 adds regression to the mean insight; Response 2 adds concrete confounders and control group point. Roughly equal quality and clarity. / Both correctly identify causation flaw and linear extrapolation issue. Response 2 adds specific, insightful concepts (regression to mean, saturation point) making it more analytically rich while staying under word limit. Response 1 is solid but less specific. | google/gemini-3.1-pro-preview: B — Both responses correctly identify the flaws and stay under the word limit. Response 2 is slightly better for a non-technical professional as it uses practical examples like seasonality and pricing changes rather than statistical terms like 'regression to the mean'. / Both responses correctly identify the flaws and stay under the word limit. Response 1 is slightly better for a non-technical professional because it provides practical business examples (seasonality, pricing) rather than statistical concepts like 'regression to the mean'. | x-ai/grok-4.5: B — Both correctly flag correlation≠causation and non-linear scaling under 100 words. R2 is clearer and more useful for non-technical readers by listing concrete alternatives and evidence gaps without jargon. / Both correctly flag correlation/causation and non-linearity under 100 words. R1 is more general and complete without over-specifying regression to the mean; equally clear and useful.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 8 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 8, "score_b": 9 } ] }, { "suite": "extraction", "id": "e1", "name": "Messy to JSON", "prompt": "Extract to JSON with keys name, company, phone, email, intent:\n\n'hiya - jules moreau here from Atelier Nord (the lighting people). best number is 07700 900 457, or jules@ateliernord.fr. we're after a quote for the workshop series u mentioned, ideally b4 sept'\n\nReturn only valid JSON.", "output_a": "{\n \"name\": \"jules moreau\",\n \"company\": \"Atelier Nord\",\n \"phone\": \"07700 900 457\",\n \"email\": \"jules@ateliernord.fr\",\n \"intent\": \"quote for the workshop series before September\"\n}", "output_b": "{\"name\":\"Jules Moreau\",\"company\":\"Atelier Nord\",\"phone\":\"07700 900 457\",\"email\":\"jules@ateliernord.fr\",\"intent\":\"Requesting a quote for the workshop series mentioned, ideally before September.\"}", "latency_ms": { "a": 5999, "b": 1152 }, "tokens_out": { "a": 255, "b": 54 }, "cost_usd": { "a": 0.000244539252, "b": 0.000402 }, "winner": "tie", "judge_scores": { "a": 8.8, "b": 9.5 }, "judge_reasoning": "anthropic/claude-sonnet-5: B — Both correctly extract fields in valid JSON. Response 2 capitalizes name properly and gives slightly more complete intent description. Both meet 'return only JSON' instruction. Minor difference favors Response 2 for polish and completeness. / Both correctly extract data; Response 1 capitalizes name properly, more polished intent phrasing. Response 2 leaves name lowercase, less refined. Both valid JSON, no format violations. | google/gemini-3.1-pro-preview: tie — Both responses perfectly followed the instructions, accurately extracted the requested information, and returned only valid JSON with the correct keys. / Both responses perfectly follow the instructions, extracting the correct information into a valid JSON format with the requested keys. Response 1 capitalizes the name, while Response 2 pretty-prints the JSON. Both are excellent. | x-ai/grok-4.5: tie (flipped on swap) — Both extract all fields accurately into valid JSON only. Minor style diffs in name casing and intent phrasing; equally correct, compliant, useful, and clear. / Both valid JSON extractions. Resp1 superior: proper name casing, complete clear intent. Resp2 has lowercase name and abbreviated intent, slightly less polished/useful.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "B", "flipped_on_swap": false, "score_a": 8, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 9.5 } ] }, { "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": "```markdown\n| 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% |\n```", "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": 14261, "b": 1401 }, "tokens_out": { "a": 664, "b": 98 }, "cost_usd": { "a": 0.000592521504, "b": 0.000694 }, "winner": "tie", "judge_scores": { "a": 9, "b": 9.3 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both correctly compute percentages and use n/a for Gamma. Response 1 wraps in code block, Response 2 renders as proper table with alignment; both equally clear and accurate, minor stylistic differences only. / Both correctly compute percentages and mark Gamma n/a. Response 1 uses consistent +/- signs; Response 2 omits '+' for positive change and wraps in code block unnecessarily, slightly less clean. | google/gemini-3.1-pro-preview: tie — Both responses accurately extract the data, calculate the percentages correctly to one decimal place, and format the output as a Markdown table with the requested columns. / Both responses correctly extract the data, calculate the percentages to one decimal place, handle the undefined change with 'n/a', and format the output as a markdown table. | x-ai/grok-4.5: tie (flipped on swap) — Both produce accurate markdown tables with correct % changes to 1 decimal and n/a; minor style differences only. / Both accurate tables with correct % calcs (11.3, -18.7, n/a, 0.0) and n/a for Gamma. R1 clearer with + sign; R2 omits + and adds unneeded code fence.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 9 } ] }, { "suite": "extraction", "id": "e3", "name": "Contact details", "prompt": "List every person mentioned below with their role and organisation, one line each in the format Name - Role - Org. If a field is unknown write unknown:\n\n'Following the review, Dr Amara Okafor (Chief Data Officer, Halcyon Health) will hand over to Ben Tran, who joins as interim CDO from Meridian Labs. Legal sign-off sits with Priya Shah at Halcyon; the external audit remains with T. Lindqvist.'", "output_a": "Dr Amara Okafor - Chief Data Officer - Halcyon Health \nBen Tran - interim CDO - Halcyon Health \nPriya Shah - unknown - Halcyon Health \nT. Lindqvist - unknown - unknown", "output_b": "Dr Amara Okafor - Chief Data Officer - Halcyon Health \nBen Tran - interim CDO - Meridian Labs \nPriya Shah - unknown - Halcyon \nT. Lindqvist - external audit - unknown", "latency_ms": { "a": 34217, "b": 1268 }, "tokens_out": { "a": 1790, "b": 50 }, "cost_usd": { "a": 0.0014913912, "b": 0.000399 }, "winner": "tie", "judge_scores": { "a": 7.3, "b": 7.7 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both correctly list Okafor. Response1 assigns Ben Tran to Halcyon Health (correct, as he joins there), while Response2 wrongly keeps Meridian Labs. Response2 gives Lindqvist a role (external audit) which is slightly more useful, but overall Response1 is more accurate on the key transition detail. / Response 1 correctly attributes Lindqvist's role but errs on Ben Tran's org (Meridian Labs, his prior org, not where he joins). Response 2 correctly places Ben Tran at Halcyon but wrongly marks Lindqvist's role unknown. Both have one factual slip; overall equally accurate. | google/gemini-3.1-pro-preview: B — Response 2 is more accurate in extracting the roles, specifically identifying 'external audit' for T. Lindqvist, whereas Response 1 incorrectly lists it as unknown. Both follow the formatting constraints perfectly. / Response 1 captures the implied roles (external audit) better than Response 2, though both handle the ambiguous organizational affiliations and roles reasonably well. Response 1 is slightly more faithful to the text's exact wording. | x-ai/grok-4.5: tie (flipped on swap) — R1 correctly assigns Ben Tran interim CDO at Halcyon (joining from Meridian) and full org names; R2 misassigns his org and truncates Halcyon. Both match format and cover all four people. / Resp1 correctly assigns Ben's origin org and T's audit role; Resp2 misplaces Ben at Halcyon and blanks T's role. Both match required format exactly.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 7, "score_b": 6.5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 7.5, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 7.5, "score_b": 7.5 } ] }, { "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 votes to 2.\n- Phase one costs £4.6m, begins in October, and includes the fish market roof.\n- Phase two marina expansion unfunded, depends on levelling-up grant decision in January.\n- Local traders receive first refusal on the 14 new retail units.\n- Construction traffic will use Dock Road, avoiding the high street.", "output_b": "- Council approved harbour redevelopment on 12 June by 7 votes to 2.\n- Phase one costs £4.6m and begins in October.\n- Phase one includes replacing the fish market roof.\n- Marina expansion depends on a January levelling-up grant decision.\n- Local traders get first refusal on 14 new retail units.", "latency_ms": { "a": 3834, "b": 1502 }, "tokens_out": { "a": 346, "b": 71 }, "cost_usd": { "a": 0.00048201408, "b": 0.000538 }, "winner": "A", "judge_scores": { "a": 9.5, "b": 7.2 }, "judge_reasoning": "anthropic/claude-sonnet-5: A — Both meet format and word limits, but Response 1 covers all five distinct facts including construction traffic routing, while Response 2 omits that fact and splits phase one into two bullets, losing coverage. / Both meet 5-bullet, <15-word constraints. Response 2 covers all 5 key facts including construction traffic routing, while Response 1 omits that fact, making Response 2 more complete and useful. | google/gemini-3.1-pro-preview: A — Both responses follow all constraints, including the exact bullet count and word limit per bullet. Response 1 is better because it captures all five distinct facts from the text, whereas Response 2 splits one fact into two and omits the construction traffic detail. / Both responses follow all constraints perfectly. Response 2 is better because it captures the five distinct main points of the text, whereas Response 1 splits one point into two bullets and misses the important detail about construction traffic. | x-ai/grok-4.5: A — Resp1 extracts 5 accurate complete facts, all <15 words, covering every key point. Resp2 inaccurately adds 'replacing', omits traffic and unfunded status, splits one fact. / Both meet exact 5-bullet <15-word constraints. R1 adds unstated 'replacing' and omits key facts (unfunded status, traffic). R2 fully accurate, complete, clearer for busy users.", "judge_pass_winners": { "pass1_a_first": "A", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 7.5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 8 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 9.5, "score_b": 6 } ] }, { "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": "{\"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}", "latency_ms": { "a": 5724, "b": 1121 }, "tokens_out": { "a": 280, "b": 65 }, "cost_usd": { "a": 0.0004410204, "b": 0.000494 }, "winner": "tie", "judge_scores": { "a": 10, "b": 10 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie — Both responses correctly and identically match the required schema with accurate data; only whitespace differs, which is immaterial to JSON validity. / Both responses correctly and identically match the required schema with accurate data, differing only in whitespace formatting, which is inconsequential. | google/gemini-3.1-pro-preview: tie — Both responses perfectly follow the instructions, accurately extracting the data and formatting it into the exact requested JSON schema. They both return only the JSON as requested. / Both responses correctly extract the information, format it exactly according to the provided JSON schema, and return only the JSON as requested. They are functionally identical. | x-ai/grok-4.5: tie — Both outputs are identical valid JSON matching the schema exactly, with correct data extraction and only JSON returned. / Both produce identical valid JSON matching the schema exactly and return only JSON. No differences in accuracy, compliance, or clarity.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 } ] }, { "suite": "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 use sunlight, water, and carbon dioxide to create glucose and release oxygen. \nInside chloroplasts, chlorophyll captures light energy to split water and produce ATP and NADPH. \nFinally, the Calvin cycle uses ATP and NADPH to convert carbon dioxide into glucose.", "output_b": "Plants use sunlight to convert water and carbon dioxide into glucose, storing energy in chemical bonds. \nInside chloroplasts, chlorophyll absorbs light and powers reactions that split water molecules. \nFinally, oxygen is released as a byproduct, while glucose fuels growth and cellular activities.", "latency_ms": { "a": 7179, "b": 2569 }, "tokens_out": { "a": 461, "b": 104 }, "cost_usd": { "a": 0.0005785368, "b": 0.00067 }, "winner": "tie", "judge_scores": { "a": 8.7, "b": 9 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both responses correctly follow the 3-sentence structure, starting words, and word limits, and both accurately describe photosynthesis. Content is equally accurate and clear, making them essentially tied in quality. / Both meet the 3-sentence structure, starting words, and word-count limits. Response 2 is slightly more accurate and detailed, correctly naming Calvin cycle, ATP, NADPH, making it more informative while still concise. | google/gemini-3.1-pro-preview: B — Both responses follow all constraints perfectly. Response 2 wins because it avoids technical jargon like ATP, NADPH, and the Calvin cycle, making it much more useful and accessible to a busy non-technical professional. / Both responses follow all constraints perfectly. Response 1 is better suited for a non-technical professional as it avoids jargon like ATP, NADPH, and the Calvin cycle, making it clearer and more useful. | x-ai/grok-4.5: tie (flipped on swap) — Both meet all constraints exactly with accurate 3-sentence structure. R1 more complete/precise on mechanism; R2 simpler for non-experts but omits key steps. / Both meet all constraints exactly with accurate, clear 3-sentence descriptions suitable for non-experts. Minor detail differences do not distinguish them.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 8.5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 8, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 9, "score_b": 8.5 } ] }, { "suite": "instruction", "id": "i2", "name": "Word limit", "prompt": "Explain the difference between a lease and a licence for UK commercial property in exactly 50 words. Count carefully - exactly 50.", "output_a": "A lease grants exclusive possession, creating a legal estate, security of tenure under the Landlord and Tenant Act 1954, and a proprietary interest. A licence is a revocable, personal permission to occupy, without exclusive possession, conferring no interest in land. This fundamental distinction governs all UK commercial property occupants' rights.", "output_b": "A lease grants exclusive possession of premises for a term, usually at rent, creating a proprietary interest enforceable against third parties. A licence merely permits personal use, without exclusive possession or an estate in land, and is generally revocable. Courts assess substance, not labels, when distinguishing the arrangements in practice.", "latency_ms": { "a": 40008, "b": 4027 }, "tokens_out": { "a": 2932, "b": 279 }, "cost_usd": { "a": 0.0023906064, "b": 0.001706 }, "winner": "tie", "judge_scores": { "a": 9, "b": 9 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both responses are exactly 50 words, accurate, clear, and cover key legal distinctions. Response 2 adds practical note on courts assessing substance, slightly more useful, but both are equally strong overall. / Both hit exactly 50 words and are factually accurate. Response 1 is slightly clearer and more balanced, while Response 2's mention of the 1954 Act, though relevant, adds complexity without added clarity for a non-technical reader. | google/gemini-3.1-pro-preview: A — Both responses successfully meet the strict 50-word constraint and accurately explain the legal difference. Response 1 is slightly better as it explicitly mentions UK-specific legislation (Landlord and Tenant Act 1954) and references UK commercial property directly. / Both responses perfectly meet the 50-word constraint and accurately explain the difference. Response 2 is slightly better as it explicitly mentions the Landlord and Tenant Act 1954, which is a crucial element of UK commercial property leases. | x-ai/grok-4.5: tie (flipped on swap) — Both exactly 50 words, factually accurate on UK lease/licence distinction, clear and useful. Minor edge to 1 for LTA 1954 mention but overall equal. / Both hit exactly 50 words and are factually sound on lease vs licence. Response 1 edges ahead on precision and nuance without overclaiming.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 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 sharp taste of coffee arises from chemical changes during heating. Raw seeds contain compounds that, when exposed to high temperatures, degrade into smaller molecules with a harsh profile. Key culprits include chlorogenic lactones and phenylindanes, which form from breakdown of natural seed components. Additionally, the darkening process creates melanoidins, large molecules that contribute to a lingering, astringent sensation. The degree of heating strongly influences the intensity; longer or more intense thermal exposure increases the concentration of these unpalatable substances. Even the brewing method can extract more of these compounds, especially with prolonged water contact. Thus, the interplay of chemistry and preparation shapes the final cup’s edge.", "output_b": "Coffee’s harsh, drying edge comes from several naturally occurring compounds released during brewing. Longer heating creates more dark, smoky molecules, including phenolic substances and products of sugar and protein reactions. Grinding very fine, using water that is too hot, or letting water contact the grounds too long can pull out excessive amounts of these substances. Stale grounds and scorched brewing equipment can add an ashy note. Some varieties contain more of the relevant compounds than others. Balanced preparation, fresh grinding, suitable water temperature, and a shorter brew time usually produce a smoother cup.", "latency_ms": { "a": 20428, "b": 7285 }, "tokens_out": { "a": 1371, "b": 574 }, "cost_usd": { "a": 0.00167206128, "b": 0.003494 }, "winner": "tie", "judge_scores": { "a": 8.3, "b": 8.3 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both responses stay within word limits, avoid banned words, and accurately explain bitterness causes. R1 is more scientifically detailed; R2 offers more practical brewing tips. Quality and clarity are comparable overall. / Both meet word count and avoid banned terms. Response 1 is clearer, more concise, and more accessible to a non-technical audience, while Response 2 uses more technical jargon, slightly reducing usefulness. | google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses followed all constraints, including word count and forbidden words. Response 1 provides a much better scientific explanation of the chemical compounds responsible for the harsh taste, making it more accurate and useful for the prompt's specific question. / Both responses follow all constraints, including word count and forbidden words. Response 1 is slightly better suited for a non-technical professional as it focuses more on practical brewing factors rather than specific chemical names. | x-ai/grok-4.5: tie (flipped on swap) — Both meet 80-120 words and avoid all banned terms/variants. R1 more chemically precise; R2 clearer, more practical tips for non-experts, slightly higher usefulness and overall. / Both meet 80-120 words and avoid all banned terms/variants. R2 more factually precise on compounds; both clear, useful, compliant.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8, "score_b": 8.5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 8 }, { "judge": "x-ai/grok-4.5", "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": "I just hired my first apprentice. Their resume was sparse but their hunger was undeniable. Initially, I questioned whether I had enough bandwidth or expertise to mentor someone properly. The self-doubt was loud.\n\nBut what if the only thing standing between them and a career breakthrough was someone willing to take a chance?\n\nWatching their confidence grow through daily tasks reminded me that small wins compound. Their curiosity re-energized our entire team’s culture. Mentorship isn’t a distraction from work—it is the work. Now I can’t imagine our team without them. 🌱", "output_b": "Today, we hired our first apprentice—a milestone that feels both exciting and humbling. We’re not just adding capacity; we’re making a commitment to teach, listen, and create a place where someone can build confidence through real work.\n\nWhat could change if more businesses treated potential as something worth investing in?\n\nOur new teammate will bring fresh questions, energy, and perspective, while we provide guidance, patience, and room to learn. The goal isn’t perfection on day one. It’s progress, trust, and the belief that small wins compound. I’m grateful to everyone who helped us reach this moment, and eager to learn alongside them. 🌱", "latency_ms": { "a": 30780, "b": 5734 }, "tokens_out": { "a": 2063, "b": 551 }, "cost_usd": { "a": 0.0016962192, "b": 0.003363 }, "winner": "tie", "judge_scores": { "a": 8.5, "b": 9.3 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both meet word count, structure, emoji, phrase, and question constraints. Response 2 is more polished, warm, and professional, better suited for LinkedIn tone, while Response 1 is slightly more casual and less refined. / Both meet word count, structure, emoji, phrase, and no hashtags. Response 2 has a more authentic, engaging narrative arc with personal vulnerability, making it slightly more compelling and useful for a LinkedIn audience than the more generic Response 1. | google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses follow all constraints perfectly, including word count, paragraph structure, and the required phrase. Response 1 is slightly better as it feels more personal, engaging, and authentic for a LinkedIn post. / Both responses follow all constraints perfectly, including word count, formatting, and required phrases. Response 1 is slightly better suited for a post about just hiring someone, whereas Response 2 speaks as if the apprentice has already been working for some time. | x-ai/grok-4.5: tie (flipped on swap) — Resp1 has 86 words (outside 90-110) else meets format/phrase/emoji; solid content. Resp2 hits all constraints exactly at 101 words with clear professional tone. / Both meet all constraints exactly (word counts 102/90, 3 paras, Q as para 2, phrase present, one end emoji, no hashtags). Equally correct, clear, useful posts.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 8.5 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": true, "score_a": 9.5, "score_b": 9.5 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 7.5, "score_b": 10 } ] }, { "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": "Nightly Retail Closing Checklist\n\n**Front of house**\n1. Tidy and restock all displays\n2. Count and secure cash register drawer\n3. Wipe down all customer-facing surfaces\n4. Switch off all non-essential lights\n\n**Back office**\n5. Lock filing cabinets and confidential documents\n6. Shut down computers and office equipment\n7. Log daily sales and inventory discrepancies\n8. Set thermostat to overnight energy-saving mode\n\n**Security**\n9. Activate alarm system and verify signal\n10. Check all windows and rear exits\n11. Lock front door and test it\n12. Arm surveillance cameras and motion sensors", "output_b": "Nightly Retail Closing Checklist\n\nFront of house\n1. Tidy displays and restock shelves\n2. Clean counters and fitting rooms\n3. Empty bins and sweep floors\n4. Lock customer entrances and exits\n\nBack office\n1. Count cash and reconcile tills\n2. Complete daily sales paperwork\n3. Store valuables in locked safe\n4. Turn off office equipment\n\nSecurity\n1. Check all doors are locked\n2. Activate alarm system carefully\n3. Verify cameras are recording\n4. Switch off nonessential lights", "latency_ms": { "a": 2510, "b": 5908 }, "tokens_out": { "a": 132, "b": 283 }, "cost_usd": { "a": 0.00038544, "b": 0.001764 }, "winner": "B", "judge_scores": { "a": 5.8, "b": 9.7 }, "judge_reasoning": "anthropic/claude-sonnet-5: B — Response 1 uses continuous numbering 1-12 instead of restarting each section at 1, violating exact structure requirement. Response 2 correctly numbers 1-4 in each section, matching required format precisely, with concise 3-8 word items throughout. / Response 1 follows exact structure with 1-4 numbering per section, correct headers, plain text. Response 2 uses continuous numbering (1-12) instead of restarting, adds unrequested markdown bold, violating the specified structure. | google/gemini-3.1-pro-preview: B — Both responses follow the constraints perfectly. Response 2 is slightly better as it uses the exact heading text without markdown formatting and restarts the numbering at 1 for each section, making it a cleaner checklist. / Response 1 follows all instructions perfectly, including the exact section headers and word count constraints. Response 2 includes markdown formatting in the headers which deviates slightly from the exact requested text, and continues numbering across sections instead of restarting. | x-ai/grok-4.5: B — R2 matches structure exactly: plain heads, 4 items each restarting at 1, 3-8 words, nothing else. R1 adds **, continues numbering 5-12. / Resp1 matches structure, headings, 1-4 numbering, word counts exactly. Resp2 uses bold, continuous 5-12 numbering, wrong section content.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "B", "flipped_on_swap": false, "score_a": 5, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 8.5, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 4, "score_b": 10 } ] }, { "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 UK rate is 20%.", "latency_ms": { "a": 12652, "b": 937 }, "tokens_out": { "a": 139, "b": 21 }, "cost_usd": { "a": 0.0001241316, "b": 0.00015 }, "winner": "tie", "judge_scores": { "a": 10, "b": 10 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie — Both responses are identical, correct, concise, one-sentence answers meeting the task requirements exactly. / Both responses are identical, correct, concise, and satisfy the one-sentence constraint. | google/gemini-3.1-pro-preview: tie — Both responses are identical, factually correct, and perfectly follow the one-sentence constraint. / Both responses are identical, factually correct, and perfectly follow the one-sentence constraint. | x-ai/grok-4.5: tie — Both identical, fully correct, one sentence as required, clear and useful. / Both identical, fully correct, one sentence as required, clear and useful.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 10, "score_b": 10 } ] }, { "suite": "speed_cost", "id": "s2", "name": "Instant classification", "prompt": "Classify each as SPAM or NOT SPAM, output only two-column lines: 'You've won a £500 voucher click here' / 'Your Tesco order 8812 is ready for collection' / 'URGENT your account will be closed verify now' / 'Minutes from Thursday's board meeting attached'.", "output_a": "'You've won a £500 voucher click here' SPAM\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": "You've won a £500 voucher click here | SPAM\nYour Tesco order 8812 is ready for collection | NOT SPAM\nURGENT your account will be closed verify now | SPAM\nMinutes from Thursday's board meeting attached | NOT SPAM", "latency_ms": { "a": 7338, "b": 2028 }, "tokens_out": { "a": 413, "b": 79 }, "cost_usd": { "a": 0.000363012, "b": 0.000542 }, "winner": "B", "judge_scores": { "a": 8, "b": 9.3 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both correctly classify all four items with clear two-column output, differing only in minor formatting (spacing vs pipe delimiter). Both are accurate, follow instructions, useful, and concise. / Both correctly classify all items. Response 1 uses clear two-column format with delimiter as requested; Response 2 lacks a clear column separator and retains quotes, less clean formatting. | google/gemini-3.1-pro-preview: B — Both responses correctly classify the messages and follow the constraint to output only two-column lines. Response 2 is slightly better formatted with a pipe separator, making the columns clearer. / Both responses correctly classify the messages and follow the constraint to output only two-column lines. Response 1 is slightly better formatted with the pipe separator, making the columns clearer. | x-ai/grok-4.5: B — Both correctly classify all four. Both output only the lines as required. Resp2 clearer two-column format with | separator; Resp1 uses spaces and keeps quotes. / Both classify all 4 correctly as SPAM/NOT SPAM. Resp1 uses clear two-column | format matching instructions better; Resp2 uses quotes+space. Both concise and useful.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 9, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "B", "flipped_on_swap": false, "score_a": 7, "score_b": 9 } ] }, { "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": "Hé, on a 10 minutes de retard - prends-nous une table si t'arrives en premier ?", "output_b": "Salut, on a 10 minutes de retard — tu peux nous prendre une table si tu arrives avant nous ?", "latency_ms": { "a": 11781, "b": 1093 }, "tokens_out": { "a": 823, "b": 26 }, "cost_usd": { "a": 0.0010071936, "b": 0.000191 }, "winner": "A", "judge_scores": { "a": 9, "b": 8.5 }, "judge_reasoning": "anthropic/claude-sonnet-5: B — Both accurate, informal translations. Response 2 is slightly more natural French phrasing ('avant nous' vs 'en premier') and polite question form, marginally better fluency and tone. / Both are accurate informal translations. Response 1 flows slightly more naturally in French ('tu arrives avant nous' vs 't'arrives en premier'). Both maintain informal tone, no constraints violated. | google/gemini-3.1-pro-preview: A — Response 1 captures the informal tone slightly better by using the contraction 't'arrives' and the direct imperative 'prends-nous', which closely matches the casual feel of the original English text. / Both translations are accurate and informal, but Response 2 captures the casual, conversational tone slightly better with the use of 't'arrives' and the direct imperative 'prends-nous', which perfectly matches the English 'grab us'. | x-ai/grok-4.5: A — Resp1 better matches casual/informal tone and imperative phrasing of original. Both accurate French; Resp2 slightly politer/less contracted. / Both accurate informal French translations. Resp2 better matches casual tone with 'Hé', imperative 'prends-nous', and 't'arrives'. No constraints violated.", "judge_pass_winners": { "pass1_a_first": "B", "pass2_b_first_mapped": "B" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "B", "flipped_on_swap": false, "score_a": 8, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "A", "flipped_on_swap": false, "score_a": 10, "score_b": 9 }, { "judge": "x-ai/grok-4.5", "verdict": "A", "flipped_on_swap": false, "score_a": 9, "score_b": 7.5 } ] }, { "suite": "speed_cost", "id": "s4", "name": "Medium generation", "prompt": "Write a 150-word 'About us' section for a family-run garden centre in Yorkshire founded in 1987, now run by the founders' daughter, known for rare perennials and a small cafe.", "output_a": "Nestled in the heart of Yorkshire, our garden centre first put down roots in 1987, when Mum and Dad turned their passion for plants into a family business. I grew up surrounded by seedlings and soil, learning that a garden isn’t just about what you grow—it’s about the care you sow. Today, I’m proud to carry on their legacy, nurturing the same warm, personal welcome they were known for.\n\nWe’ve become a bit of a destination for rare and unusual perennials, lovingly propagated and tended by our small, dedicated team. Every plant on our benches has been chosen for character and resilience, just right for Yorkshire gardens. When you’re ready for a breather, our tiny café serves proper brews, homemade cake, and a good natter. Whether you’re hunting for something special or simply enjoying a wander, you’ll always find a friendly face and a helping hand here. Generations grow with us—and we’d love to welcome yours.", "output_b": "Founded in 1987, our family-run garden centre has been helping Yorkshire gardens flourish for nearly four decades. What began as a modest nursery created by passionate growers has grown into a welcoming destination for gardeners, plant lovers and anyone seeking a peaceful pause.\n\nToday, the founders’ daughter leads the business with the same practical knowledge, warmth and love of growing that shaped it from the start. We are especially proud of our carefully chosen collection of rare perennials, unusual varieties and reliable old favourites, all selected to thrive in Yorkshire conditions.\n\nBeyond the plants, our small café offers homemade cakes, hearty lunches and good coffee in relaxed surroundings. Whether you’re planning a border, searching for something special or simply enjoying a leisurely visit, you’ll find friendly advice and a warm family welcome at every turn. We look forward to sharing garden experience and seasonal inspiration with you throughout the year.", "latency_ms": { "a": 7897, "b": 18317 }, "tokens_out": { "a": 336, "b": 1050 }, "cost_usd": { "a": 0.0002912208, "b": 0.006348 }, "winner": "tie", "judge_scores": { "a": 6.2, "b": 8.2 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie (flipped on swap) — Both responses are well-written, accurate, and cover all required elements (1987 founding, daughter, rare perennials, cafe). Neither strictly hits 150 words but both are close and neither violates a checkable explicit constraint. Response 1 has more personal voice; Response 2 is slightly more polished/professional. / Both meet the brief; Response 2 uses a warmer, personal first-person voice fitting a family-run business, more engaging and vivid. Both are close to 150 words. Response 2 edges out on usefulness and charm for a non-technical audience. | google/gemini-3.1-pro-preview: B — Response 2 followed the exact 150-word constraint perfectly, while Response 1 exceeded it. Both responses accurately included all the required details and adopted an appropriate, welcoming tone. / Response 1 followed all instructions perfectly, including the exact 150-word constraint, and incorporated all required details in a professional, welcoming tone. Response 2 is well-written and engaging but failed the strict 150-word count constraint. | x-ai/grok-4.5: tie (flipped on swap) — Both accurately cover all required elements with clear, professional prose suited to non-technical readers. Response 1 is more engaging and personal; Response 2 is solid but slightly flatter. Word counts: 156 vs exactly 150. / Both exceed 150 words (154 and 159) and cover all required elements accurately. R2 is more personal, engaging and vivid while remaining clear and useful.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "A" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": true, "score_a": 8.5, "score_b": 8 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "B", "flipped_on_swap": false, "score_a": 5, "score_b": 10 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": true, "score_a": 5, "score_b": 6.5 } ] }, { "suite": "speed_cost", "id": "s5", "name": "Long generation", "prompt": "Write a detailed 400-word beginner's guide to setting up a home compost bin: location, what to add, what to avoid, how long it takes, and one common mistake.", "output_a": "Starting a compost bin is one of the easiest ways to reduce kitchen waste and create nutrient-rich food for your garden. If you’re new to composting, this simple guide will walk you through the essentials—from picking the perfect spot to avoiding the most common beginner blunder.\n\n**Choosing the Right Location**\nPlace your bin on bare soil in a level, well-drained area. This allows worms and beneficial microbes to enter from the ground and helps excess moisture drain away. A partially shaded spot is ideal—too much direct sun can dry out the pile, while deep shade may keep it too cool and slow decomposition. Most importantly, pick a location that’s convenient. If the bin is a long, muddy trek from the kitchen, you’re far less likely to use it daily. Keep it within easy reach of the back door, but not directly against wooden structures, as constant moisture can cause rot.\n\n**What to Add: The Green-Brown Balance**\nThink of your compost pile as a living recipe that needs two main ingredients: “greens” and “browns.” Greens are nitrogen-rich materials that fuel the microbes breaking everything down. These include fruit and vegetable scraps, coffee grounds, tea bags, fresh grass clippings, and plant trimmings. Browns are carbon-rich materials that provide structure and prevent the pile from turning into a slimy, smelly mess. Add dry autumn leaves, shredded newspaper, plain cardboard (torn into small pieces), straw, sawdust from untreated wood, and crushed eggshells. A good rule of thumb is to add roughly two to three parts browns for every one part greens by volume. Layering them as you go—like a lasagna—helps maintain airflow and speeds up the process.\n\n**What to Avoid**\nNot everything from the kitchen belongs in the bin. Avoid meat, fish, bones, dairy products, and greasy or oily foods. These attract rats, create foul odors, and break down very slowly. Also leave out pet waste (which can contain harmful pathogens), diseased plants, and weeds that have gone to seed—home compost piles rarely get hot enough to kill those seeds reliably. Treated wood products, glossy paper, and synthetic materials should never be added.\n\n**How Long Does It Take?**\nPatience is key. In a well-maintained bin, you can expect dark, crumbly compost in anywhere from three to six months. The timeline depends on factors like temperature, how finely you chop materials, and how often you turn the pile. Turning every week or two with a garden fork introduces oxygen, which speeds things up dramatically. A neglected pile will still decompose, but it might take a year or more. You’ll know it’s ready when the original ingredients are no longer recognizable and the compost smells earthy, like a forest floor.\n\n**One Common Mistake: The Soggy, Smelly Pile**\nThe number-one mistake beginners make is adding too many kitchen scraps (greens) without enough dry browns. Without carbon-rich materials to absorb moisture and create air pockets, the pile becomes compacted and anaerobic. The result? A slimy, foul-smelling mess that attracts flies. If your bin starts to stink, don’t give up. Simply mix in a generous amount of shredded leaves, cardboard, or straw, and give it a good turn. The smell will vanish as the balance is restored, and your compost will be back on track.", "output_b": "## Beginner’s Guide to Setting Up a Home Compost Bin\n\nComposting turns kitchen and garden waste into a dark, crumbly soil improver that helps plants retain water and nutrients. A simple compost bin is easy to manage once you understand the balance of materials, moisture, and airflow.\n\n### Choose the Right Location\n\nPlace your compost bin outdoors on bare soil if possible. Contact with the ground allows worms and helpful microorganisms to enter the pile and speeds decomposition. Choose a level, well-drained spot that is convenient enough to reach regularly, especially from the kitchen.\n\nA partly shaded location is ideal. Full sun can dry the pile out too quickly, while deep shade may keep it too wet and slow the process. Keep the bin away from doors, windows, and neighboring fences in case it develops odors temporarily. A covered plastic bin, wooden pallet enclosure, or purchased compost tumbler can all work well.\n\n### What to Add\n\nSuccessful compost needs a mix of “greens” and “browns.”\n\n**Greens** are moist, nitrogen-rich materials that help feed the microorganisms:\n- Fruit and vegetable scraps \n- Coffee grounds and paper filters \n- Tea leaves and tea bags without plastic \n- Fresh grass clippings \n- Plant trimmings and weeds that have not gone to seed \n- Crushed eggshells \n\n**Browns** are dry, carbon-rich materials that create air pockets:\n- Dry leaves \n- Shredded cardboard \n- Torn-up uncoated paper \n- Small twigs \n- Straw \n- Wood chips or sawdust in small amounts \n\nAim for roughly two to three parts browns for every one part greens. Add a layer of browns after adding wet kitchen scraps. Chop larger materials into smaller pieces to help them break down faster.\n\n### What to Avoid\n\nDo not add meat, fish, dairy products, oils, greasy foods, or cooked leftovers. These can smell bad and attract rats, flies, or other pests. Avoid pet waste, diseased plants, coal ash, and plants treated with herbicides. Also avoid glossy magazines, plastic-coated packaging, and “compostable” items unless your local system specifically accepts them; many do not break down well in a home bin.\n\n### Moisture and Turning\n\nYour compost should feel like a wrung-out sponge: damp, but not dripping. If it is dry, add water or moist greens. If it is soggy or smelly, mix in dry leaves, shredded cardboard, or other browns.\n\nTurn or mix the pile every few weeks using a garden fork or shovel. This introduces oxygen, helping prevent odor and speeding decomposition.\n\n### How Long It Takes\n\nFinished compost may take two to six months in warm weather if you turn it regularly and maintain a good balance. A neglected pile can take six months to a year or longer. Compost is ready when it is dark brown, crumbly, earthy smelling, and no longer recognizable as food scraps or leaves.\n\n### One Common Mistake\n\nThe most common beginner mistake is adding too many wet kitchen scraps without enough browns. This creates a dense, soggy, smelly pile. Prevent it by keeping a bag of dry leaves or shredded cardboard nearby and adding a generous handful whenever you add food waste.", "latency_ms": { "a": 87639, "b": 33108 }, "tokens_out": { "a": 1010, "b": 730 }, "cost_usd": { "a": 0.000852784092, "b": 0.004423 }, "winner": "tie", "judge_scores": { "a": 6.3, "b": 6.3 }, "judge_reasoning": "anthropic/claude-sonnet-5: tie — Both responses are accurate, well-structured, and cover all required elements clearly. Response 2 uses bullet lists and headers for slightly better scannability for a non-technical reader; Response 1 is slightly more narrative. Both roughly meet length; quality is comparable overall. / Both responses accurately and thoroughly cover all required elements (location, additions, avoidances, timeline, common mistake) with clear structure. Both are similarly informative and well-organized; word counts likely close to 400. Quality is nearly identical, with response 2 slightly more engaging tone. | google/gemini-3.1-pro-preview: tie — Both responses provide excellent, clear, and useful guides covering all required topics. However, both fail the exact 400-word count constraint. / Both responses failed the strict 400-word count constraint. However, both provide excellent, accurate, and well-structured advice covering all the requested topics. Response 1's use of bullet points makes it slightly easier to skim, but both are highly useful and clear. | x-ai/grok-4.5: tie — Both cover all required topics accurately and clearly for beginners; R1 more polished/detailed. Neither hits exactly 400 words (R1 ~500, R2 ~420). / Both cover all required topics accurately, clearly, and usefully for beginners with strong structure. Neither hits exactly 400 words.", "judge_pass_winners": { "pass1_a_first": "tie", "pass2_b_first_mapped": "tie" }, "panel": [ { "judge": "anthropic/claude-sonnet-5", "verdict": "tie", "flipped_on_swap": false, "score_a": 9, "score_b": 9 }, { "judge": "google/gemini-3.1-pro-preview", "verdict": "tie", "flipped_on_swap": false, "score_a": 5, "score_b": 5 }, { "judge": "x-ai/grok-4.5", "verdict": "tie", "flipped_on_swap": false, "score_a": 5, "score_b": 5 } ] } ] }