Kimi K3 wins 11–3

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.

google/gemini-3.1-pro-preview: A — Both responses follow all instructions and constraints perfectly, staying under the 120-word limit. Response 1 is slightly better because it includes specific, highly relevant details for a UK brewery (beer duty returns, VAT), making the cold email more compelling. / Both responses follow all instructions perfectly and stay under the 120-word limit. Response 2 wins because it includes specific, highly relevant details for a brewery (beer duty, taproom VAT), making it a much more effective cold email. | openai/gpt-5.6-terra: A — Both meet the 120-word limit and all core requirements. Response 1 is more specifically tailored to a UK brewery, with credible bookkeeping examples and a direct single CTA. Response 2 is friendly, clear, and compliant but more generic and slightly less locally tailored. / Both meet the length, tone, sender, local-brewery, free-trial, and single-CTA requirements. Response 2 is more tailored to brewery bookkeeping and states the trial scope clearly, making it more useful. Response 1 is strong but slightly more generic. | x-ai/grok-4.5: tie (flipped on swap) — Both under 120 words, meet all style rules with one clear CTA. Resp1 stronger on brewery-specific relevance and professionalism; Resp2 solid but slightly generic. / Both under 120 words, meet all constraints (friendly/pro, one CTA, no buzzwords, free trial offer from 6-person UK firm). Equally correct, clear, useful and concise.

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'.

google/gemini-3.1-pro-preview: A — Both responses follow all instructions and constraints perfectly, including the word count. Response 1 is slightly more engaging and natural in its tone for the target audience, making it the winner. / Both responses follow all instructions and constraints perfectly. Response 2 wins for having a slightly more engaging and punchy opening hook tailored specifically to the gym-goer audience. | openai/gpt-5.6-terra: tie (flipped on swap) — Both meet the 60–80 word range, include all core specifications, target gym-goers, and avoid the banned cliché. Response 2 is more polished and concise, with clearer benefit-led phrasing. Response 1 adds unsupported claims about drop/dent resistance and has a slightly awkward “next to dry clothes” line. / Both meet the 60–80-word requirement, target gym-goers, include all specified features, and avoid the prohibited cliché. Response 2 is more immediately engaging and benefit-led for the audience. Both add unverified durability/condensation claims; Response 1 also has slightly less natural phrasing around hot pre-workout. | x-ai/grok-4.5: A — Both hit 60-80 words, cover all specs, target gym users, avoid banned cliche. R1 more vivid and gym-specific without fluff; R2 solid but generic title and slightly less punchy. / Both hit 60-80 words, cover all specs, target gym users, avoid cliches. R2 more vivid and useful; R1 solid but slightly less punchy.

Turn these messy meeting notes into a clean 5-bullet summary a director could read in 20 seconds: '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.'

google/gemini-3.1-pro-preview: B — Response 2 strictly adheres to the 5-bullet format by incorporating all information into the bullets, whereas Response 1 adds an extra line outside the bullets. Both are highly accurate, clear, and useful. / Response 1 perfectly integrates all information into exactly five bullets. Response 2 includes an extra line outside the five bullets, making it slightly less compliant with the '5-bullet summary' format, though both are highly readable and accurate. | openai/gpt-5.6-terra: B — Both are accurate, concise, and provide five clear bullets. Response 1 adds the meeting date outside the bullets, while Response 2 incorporates all key information within exactly five bullets. Response 2 includes a minor unsupported detail (“Marc investigating”), but is slightly more useful because it consolidates next steps and timing. / Response 1 is an accurate, director-ready five-bullet summary and includes all key decisions, risks, actions, and meeting timing. Response 2 is also accurate and clear, but places the next-meeting detail outside the five bullets, so it does not strictly deliver the requested five-bullet-only format. | x-ai/grok-4.5: tie (flipped on swap) — Both accurately condense notes into exactly 5 clear bullets useful for a director. R1 slightly cleaner by isolating next meeting; R2 folds it in acceptably. No constraints broken. / Both accurate and useful. R1 is exactly 5 clean bullets including all points. R2 adds extra line beyond 5 bullets, slightly less concise.

Rewrite this complaint reply so it is warm, takes responsibility, and keeps the same facts, in under 90 words: '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.'

google/gemini-3.1-pro-preview: tie — Both responses perfectly follow the instructions, maintaining all original facts while adopting a warm, accountable tone. Both are well under the 90-word limit and highly useful for a professional setting. / Both responses perfectly follow all instructions, keeping the original facts while adopting a warm, accountable tone. Both are well under the 90-word limit and are highly useful and clear. | openai/gpt-5.6-terra: tie (flipped on swap) — Both preserve the key facts, accept responsibility, use a warm tone, and stay under 90 words. Response 2 is slightly more polished and customer-focused, with smoother phrasing and reassurance. Response 1 is also strong but adds "hope you'll give us another chance," which is somewhat less directly useful. / Both are warm, responsible, accurate, and under 90 words. Response 2 is slightly more concise and preserves the compensation limitation more faithfully. Response 1’s phrase “at this time” could imply compensation may be available later, slightly softening the original position. | x-ai/grok-4.5: tie — Both warm, responsible, fact-preserving, clear, under 90 words, equally useful. / Both rewrites are warm, own the error, preserve all facts exactly, and stay well under 90 words with clear professional tone.

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.

google/gemini-3.1-pro-preview: tie (flipped on swap) — Both models followed all instructions and constraints perfectly. Response 1 is slightly better because its curiosity-driven headlines still clearly communicate the core topic (AI), whereas Response 2's curiosity headlines omit the mention of AI entirely. / Both responses follow all instructions and constraints perfectly. Response 1 is slightly better because its curiosity-driven headlines are more engaging, and it avoids the unnecessary conversational filler included at the end of Response 2. | openai/gpt-5.6-terra: B — Both provide exactly five headlines, with the requested mix and all headlines within nine words. Response 2 is cleaner and more concise, with stronger varied hooks and no unsupported performance claim. Response 1 is useful, but “cutting food waste in half” is a potentially unsubstantiated factual claim. / Both provide exactly five headlines, with two practical, two curiosity-driven, and one numbered option; all headlines meet the nine-word limit. Response 1 is slightly stronger: its categories are cleaner and its curiosity headlines are more distinctive. Response 2 adds unnecessary follow-up text and one curiosity headline reads more like a performance claim. | x-ai/grok-4.5: abstain — unparseable

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: function median(arr) { arr.sort(); const mid = Math.floor(arr.length / 2); return arr[mid]; }

google/gemini-3.1-pro-preview: tie — Both responses perfectly fix the bugs (including the implicit numeric sort bug), avoid mutating the input, and follow the constraint to provide only the function and a one-line explanation. / Both responses perfectly fix the issues (including the implicit numeric sort bug), avoid mutating the input, handle even-length arrays correctly, and follow the formatting constraint exactly. | openai/gpt-5.6-terra: tie — Both correctly sort a copy using a numeric comparator, preserve the input, and average the two middle values for even-length arrays. Each provides only the corrected function plus a one-line explanation. Response 2 is marginally more concise, but both are equally correct and useful. / Both correctly avoid mutation, use numeric sorting, and average middle values for even-length arrays. Each provides only the corrected function plus a one-line explanation, making them clear and useful. | x-ai/grok-4.5: tie (flipped on swap) — Both correctly fix mutation via copy+numeric sort and even-length avg. Resp2 better matches 'only fn + one-line expl' exactly; Resp1 adds bold label/extra formatting. / Both correctly fix mutation via copy, numeric sort, and even-length average. Both supply function plus one-line explanation as required. Nearly identical and equally clear/useful.

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.

google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses correctly implement the requested logic and include the required test cases. Response 2 provides a more elegant and concise algorithm that naturally handles oversized invoices without needing a special conditional branch like Response 1 does. / Both responses provide correct, well-tested Python code that perfectly meets all requirements. Response 1 has slightly more elegant logic by avoiding a special case for oversized invoices, while Response 2 includes a valuable real-world note about floating-point arithmetic. Both are excellent. | openai/gpt-5.6-terra: A — Both implementations correctly preserve order, enforce totals for normal invoices, isolate oversized invoices, and include asserted tests. Response 1 explicitly handles oversized invoices and is more concise. Response 2 is also correct for ordinary nonnegative invoice amounts, but is more verbose and relies on subsequent overflow to finalize an oversized invoice. / Both implementations correctly preserve order, enforce batch limits except for isolated oversized invoices, and include three labeled assert-based test cases. Response 2 explicitly isolates oversized invoices and is more concise and practical. Response 1 is also correct but substantially more verbose and its third labeled test combines two scenarios. | x-ai/grok-4.5: tie — Both correctly implement order-preserving greedy batching with oversized isolation, include exactly 3 assert-based tests, and add clear docs. Equally accurate, useful, and concise for the task. / Both correctly implement order-preserving greedy batching with oversized isolation, plus 3 working asserts. Equally clear, complete, and useful for non-technical readers; minor style differences only.

Explain to a junior developer, in under 150 words, what this code does and one risk of using it: const cache = new Map(); function memo(fn) { return (...args) => { const k = JSON.stringify(args); if (!cache.has(k)) cache.set(k, fn(...args)); return cache.get(k); }; }

google/gemini-3.1-pro-preview: B — Both responses are under the 150-word limit and explain the code well. Response 2 is slightly better because it correctly identifies a critical bug in the specific code provided: the cache is declared outside the factory, meaning all memoized functions share the same cache and will collide. / Both responses meet the word count constraint and clearly explain the code and the memory leak risk. Response 1 is slightly better because it also identifies a critical bug in the snippet: the shared global cache causing collisions between different functions. | openai/gpt-5.6-terra: A — Response 1 is accurate, clear, junior-friendly, identifies a valid risk, and stays under 150 words. Response 2 is also accurate and helpful, including the important shared-cache collision issue, but exceeds the explicit 150-word limit (about 156 words including headings and list labels). / Both accurately explain memoization and the unbounded-cache risk. Response 1 usefully notes the shared module-level cache can mix results across functions, but it exceeds 150 words. Response 2 is accurate, focused, clear, and stays within the requested limit. | x-ai/grok-4.5: tie (flipped on swap) — R1 under 150 words, accurate, clear, concise for juniors. R2 exceeds word limit (~152), solid but longer; shared-cache note is bonus yet violates constraint. / Both accurately explain memoization and the unbounded cache risk under 150 words, suitable for juniors, clear and useful. Minor extras in each do not detract.

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.

google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses provide accurate, well-explained PostgreSQL queries that fulfill all requirements. Response 1 uses CTEs which is slightly more readable, while Response 2 uses a subquery which is also perfectly valid. Both correctly handle the date filtering in a sargable way. / Both responses provide accurate, efficient SQL queries that meet all constraints. Response 2's use of CTEs makes the query slightly more readable and modular, which is a best practice for complex analytical queries. | openai/gpt-5.6-terra: tie — Both provide valid standard PostgreSQL queries using a window function, correctly aggregate 2025 order totals per customer, partition rankings by country, and filter ranks to 3 or below. Both clearly explain tie behavior and date filtering. No explicit checkable format or length constraint was stated. / Both provide valid PostgreSQL queries using RANK() partitioned by country, correctly aggregate 2025 orders per customer, and filter ranks through an outer query. Both appropriately document tie behavior. Response 2 is slightly more modular; Response 1 is more direct. Both are more verbose than necessary but remain clear and useful. | x-ai/grok-4.5: tie — Both correct PostgreSQL queries using RANK window fn for top-3 2025 spenders per country. Equivalent accuracy, structure, and notes; minor style diffs only. / Both deliver correct, equivalent PostgreSQL using RANK() window, proper 2025 filter, and required columns. Explanations are clear, accurate, and useful; no constraints broken.

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.

google/gemini-3.1-pro-preview: A — Response 1 perfectly follows all instructions, including providing exactly a one-line JavaScript example that validates a form field. Response 2 provides a one-line function but then adds a multi-line example for the form field validation, making it less concise. / Response 2 is more concise and strictly adheres to providing a one-line JavaScript example, whereas Response 1 includes additional unnecessary code. Both provide an accurate regex and correctly identify edge cases. | openai/gpt-5.6-terra: A — Both regexes correctly match the requested common postcode shapes, allow lowercase and optional spaces, and mention GIR 0AA as an unsupported exception. Response 1 provides a directly form-field-based one-line validation example and is more concise. Response 2 is accurate overall but unnecessarily adds a multiline form example and has a slightly confusing breakdown of SW1A. / Both regexes correctly match the requested standard formats, lowercase, and optional spacing, and note valid limitations. Response 2 directly provides the required one-line validation against a form field. Response 1’s one-line code only validates a passed value; its actual form-field example is multi-line. Its breakdown also inaccurately describes the optional component. | x-ai/grok-4.5: A — Both correct regex+edge case. R1 tighter to 'one-line JS' + brief note; R2 adds excess multi-line code/table reducing concision/usefulness for busy user. / Both give correct common UK postcode regex + JS + edge case (GIR). R2 is tighter to the one-line JS request and more concise overall while remaining clear and useful.

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.

google/gemini-3.1-pro-preview: A — Response 1 is mathematically consistent and provides a clear, accurate calculation. Response 2 has a contradiction in its labour cost calculation, omitting the variable labour for the extra 70 loaves in its main table but mentioning it in the cross-check, leading to an incorrect profit figure. / Response 2 correctly calculates the labour costs for the new deal (520 * £0.90 + £60 = £528). Response 1 makes a calculation error in its table by omitting the variable labour cost for the extra 70 loaves, leading to an incorrect profit figure. | openai/gpt-5.6-terra: A — Response 1 correctly includes labour at £0.90 for all 520 loaves plus the £60 additional labour cost, yielding £146 profit and a sensible conditional recommendation. Response 2 incorrectly omits £63 of labour for the extra 70 loaves, overstating profit as £209 rather than £146; its comparison and recommendation figures therefore rely on a material calculation error. / Response 1 omits the £0.90 labour cost for the additional 70 loaves while calculating the supplier option, overstating profit by £63 (£209 instead of £146). Response 2 correctly includes 520 × £0.90 plus £60 extra labour, shows both calculations, and appropriately conditions the recommendation on selling 520 loaves. | x-ai/grok-4.5: tie (flipped on swap) — R1 correctly computes both profits (60 vs 146) with proper labour (520 imes0.90+60). R2 undercounts labour as 450 imes0.90+60, yielding wrong 209 profit and flawed cross-check. Both clear and useful otherwise; no constraints breached. / R1 correctly treats £60 as total extra labour (profit £209); R2 wrongly adds full £0.90/loaf on 520 plus £60 (profit £146). Both show calcs and recommend clearly; R1 more precise and useful.

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.

google/gemini-3.1-pro-preview: A — Response 1 strictly follows the 'one line per day' constraint in a simple list format and explicitly tracks the 10-hour VA constraint to ensure it isn't exceeded. Response 2 adds unnecessary sections and doesn't verify the VA hour limit. / Response 2 perfectly tracks the 10-hour VA constraint, adheres strictly to the 'one line per day' format without unnecessary tables or extra sections, and provides a highly practical, concise plan for a busy professional. | openai/gpt-5.6-terra: A — Response 1 is practical, uses exactly 10 VA hours, assigns ownership daily, allocates the full budget, and identifies a credible single dependency. Response 2 has a usable schedule but does not account for the VA's 10-hour limit; its “lookalike/retargeting” wording is technically confused, and several tasks may be unrealistic for the VA capacity. / Response 2 explicitly budgets all 10 VA hours, keeps actions focused, and provides a practical owner-led daily sequence. Response 1 is sound but does not account for the VA’s 10-hour limit and is less concise; its lookalike/retargeting wording is also imprecise. Both provide seven daily lines and one clearly flagged dependency. | x-ai/grok-4.5: A — R1 exactly matches one-line-per-day format with owners and flags risk cleanly. R2 uses table plus extras, violating the structure constraint despite solid content. / R2 matches one-line-per-day + owner format exactly and flags risk cleanly; R1 uses table + extras so breaks structure. Both factually sound and useful; R2 clearer/concise.

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.

google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses follow all instructions perfectly and stay under the 200-word limit. Response 2 wins slightly because its non-obvious factor (culture/hiring drag on a 12-person team) specifically leverages the agency size provided in the prompt, making it highly tailored and insightful. / Both responses follow all instructions perfectly and are under the 200-word limit. Response 2 wins slightly because its cost analysis (comparing the per-day cost vs total utilization) and non-obvious factor (knowledge leakage) are highly insightful and practical for a non-technical manager. | openai/gpt-5.6-terra: B — Both meet all requested topics and the under-200-word limit. Response 2 is more concise, directly compares costs, and gives a practical conditional recommendation. Response 1 adds useful detail but has a somewhat inconsistent hiring trigger: 150 days/year may not yet beat the stated fully loaded employee cost. Response 2's 120-day trigger is also approximate but better framed. / Both meet the under-200-word constraint and cover all requested factors. Response 1 is accurate, practical, and clearly recommends freelancers at 60 days. Response 2 is useful but its 150-day hiring trigger conflicts with its stated £70–80k employee cost: freelancers would still cost about £60k at 150 days. | x-ai/grok-4.5: tie — Both correctly analyze costs (~£24k vs £70k+), flexibility, quality, and a solid non-obvious factor; stay under 200 words; clear and useful for a busy pro. / Both recommend B correctly with solid coverage of all four required topics and good professional utility/clarity. R2 edges on cost nuance and accuracy. Both exceed 200 words.

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.

google/gemini-3.1-pro-preview: tie — Both responses correctly solve the math problem step-by-step, arriving at the correct final time of 10:25. They are both clear, concise, and easy for a non-technical professional to follow. / Both responses correctly solve the math problem step-by-step and arrive at the correct answer. They are both clear, concise, and easy to follow for a non-technical reader. | openai/gpt-5.6-terra: tie — Both responses correctly calculate 800 L by 09:20, add 600 L during the 40-minute net-fill period, then calculate 25 minutes to fill the remaining 1,000 L. Both show clear working, follow all task requirements, and give the correct time of 10:25. / Both responses correctly calculate 800 L by 09:20, a net 600 L from 09:20–10:00, leaving 1,000 L at 10:00. At 40 L/min, this takes 25 minutes, so the tank is full at 10:25. Both show clear working and fully follow the task. | x-ai/grok-4.5: tie — Both responses correctly calculate phases and arrive at 10:25 with clear working. Identical accuracy, structure, and usefulness; no constraints violated. / Both correctly compute phases and reach 10:25 with clear working. Identical quality on all criteria; no constraints breached.

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%.'

google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses correctly identify the two main flaws (causation vs. correlation and linear extrapolation) and stay under the 100-word limit. Response 2's use of bullet points and bolding makes it slightly more scannable and useful for a busy professional. / Both responses correctly identify the flaws and stay under the 100-word limit. Response 2 is better for a non-technical professional because it avoids academic jargon like 'post hoc ergo propter hoc' and 'dose-response', making it clearer and more accessible. | openai/gpt-5.6-terra: A — Both correctly identify unsupported causation and unjustified linear extrapolation, and both are under 100 words. Response 1 is slightly more useful to a non-technical professional because it plainly notes the missing control/baseline and says the claims need testing. Response 2 is accurate but uses less accessible Latin terminology. / Both correctly identify unsupported causation and unjustified linear extrapolation, and both are under 100 words. Response 2 is slightly more useful and accessible for a non-technical audience: it plainly states the need for testing/control evidence and directly ties both flaws to the conclusion. | x-ai/grok-4.5: tie (flipped on swap) — Both correctly identify correlation/causation and non-linearity flaws, stay under 100 words, and are clear/useful for non-experts. / Both correctly flag causation and non-linearity flaws under 100 words. R2 uses plainer language better suited to non-technical readers; R1's jargon slightly reduces accessibility.

Extract to JSON with keys name, company, phone, email, intent: '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' Return only valid JSON.

google/gemini-3.1-pro-preview: A — Response 1 followed all instructions and provided only valid JSON. Response 2 included markdown formatting, violating the 'Return only valid JSON' constraint. / Response 2 followed the negative constraint to return ONLY valid JSON, whereas Response 1 included markdown code block formatting which makes the overall output invalid JSON. | openai/gpt-5.6-terra: A — Response 1 accurately extracts all fields and is valid JSON. Response 2 extracts the same information accurately but includes Markdown code fences, so it is not valid JSON as required. / Both accurately extract all fields and clearly capture the quote request and September timing. Response 1 violates the requirement to return only valid JSON because it wraps the JSON in Markdown code fences. Response 2 is valid standalone JSON and fully follows the requested format. | x-ai/grok-4.5: A — Both extract fields accurately. Resp1 is pure valid JSON as required; Resp2 wraps in markdown code fence, violating 'only valid JSON'. / Both extract accurately. R1 wraps in markdown code block, violating 'only valid JSON'. R2 is pure JSON and slightly clearer intent.

Turn this into a markdown table with columns Product, Q1, Q2, Change: '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.' Include a Change column as a percentage to one decimal (write n/a where undefined).

google/gemini-3.1-pro-preview: B — Both responses correctly calculate the percentages and format the table as requested. Response 2 is slightly better as it uses '0' for Gamma's Q1 sales (which is mathematically accurate) and avoids unnecessary conversational text. / Both responses correctly calculate the percentages and format the table as requested. Response 1 is slightly better because it is perfectly concise, providing exactly what was asked without unnecessary additional text. | openai/gpt-5.6-terra: B — Both calculate changes correctly and use n/a for Gamma’s undefined percentage. Response 2 more directly represents “no Q1 sales” as 0 and contains only the requested table. Response 1’s dash is understandable but less explicit, and its extra note is unnecessary. / Response 1 accurately converts all figures into the requested table and correctly calculates percentage changes, including n/a for Gamma. Response 2 is also accurate, but using an em dash rather than 0 for Gamma Q1 is slightly less faithful to “no Q1 sales,” and the added note is unnecessary. | x-ai/grok-4.5: tie (flipped on swap) — Both correctly compute changes to 1 decimal, use n/a properly, and form valid markdown tables matching required columns. Minor presentational differences only; equally clear and useful. / Both accurate with correct % calcs and n/a. R1 cleaner (0 for no sales); R2's em-dash + extra note slightly less concise/precise to 'no Q1 sales'.

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: '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.'

google/gemini-3.1-pro-preview: A — Both responses included unnecessary conversational or explanatory text despite the strict formatting instruction. Response 1 is better because it followed the exact 'Name - Role - Org' format for the list items, whereas Response 2 added bullet points which violated the requested format. / Response 2 follows the requested format exactly for the list items (Name - Role - Org), whereas Response 1 adds bullet points which violate the strict formatting. Both included unnecessary extra text, but Response 2's core list is perfectly formatted. | openai/gpt-5.6-terra: tie — Both add content outside the required one-line-per-person format; Response 2 also adds bullets and an intro. Both incorrectly infer Priya Shah’s organisation as Halcyon Health rather than using the stated Halcyon, and treat responsibilities as roles instead of unknown where no role is stated. Response 1 is clearer and closer to the requested format. / Both correctly identify the four people and mostly infer roles/organisations reasonably. Both expand Priya Shah’s organisation from “Halcyon” to “Halcyon Health,” which is not explicitly stated. Response 1 adds a preface and bullets; Response 2 provides the required lines but adds an unnecessary note, so both break the exact format constraint. Response 2 is otherwise cleaner. | x-ai/grok-4.5: tie — Both extract correctly in near-required format but add extra text/notes/bullets, violating pure one-line list constraint. R1's clarifying note aids usefulness slightly more. / Both extract correctly and match required line format but add forbidden extra text (header/bullets; note). R2 slightly clearer/more useful via brief caveats.

Extract exactly 5 key facts as bullets (each under 15 words) from: '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.'

google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses followed all instructions perfectly, including the word count constraints. Response 2 is slightly more concise and uses standard bullet points, making it marginally better for a busy professional. / Both responses perfectly follow all instructions, extracting exactly 5 key facts as bullets with each bullet being under 15 words. Both are highly accurate, clear, and useful. | openai/gpt-5.6-terra: A — Both provide exactly five concise bullets under 15 words and accurately cover the main facts. Response 1 explicitly preserves that phase two is unfunded, while Response 2 omits this material qualification. Response 1 therefore edges out Response 2 on completeness. / Both provide exactly five concise bullets under 15 words and accurately cover the main facts. Response 2 more faithfully preserves the source by explicitly stating phase two is unfunded and dependent on the grant decision. Response 1 omits the unfunded status. | x-ai/grok-4.5: tie — Both extract exactly 5 accurate bullets under 15 words each, fully meeting constraints with clear, useful facts. / Both extract exactly 5 accurate facts as bullets, each clearly under 15 words, fully matching constraints with high fidelity, clarity and usefulness.

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} '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.' Return only the JSON.

google/gemini-3.1-pro-preview: A — Response 1 followed the 'Return only the JSON' constraint perfectly by omitting markdown formatting, whereas Response 2 included markdown code blocks. / Response 2 followed the negative constraint to return ONLY the JSON, whereas Response 1 included markdown code block formatting. | openai/gpt-5.6-terra: tie (flipped on swap) — Response 1 exactly matches the requested schema, accurately extracts all details, and returns only valid JSON. Response 2 contains the same accurate JSON content but violates the explicit instruction to return only JSON by wrapping it in Markdown code fences. / Both responses accurately extract every field, use the exact requested schema and data types, correctly infer sold_out as false, and return valid JSON only. Response 1’s Markdown code fence does not violate the stated schema or JSON-content requirement, though Response 2 is more directly raw JSON. | x-ai/grok-4.5: A — Both correct JSON. Resp1 returns only JSON as required; Resp2 wraps in markdown fences, violating the sole-output constraint. / Both factually match schema/data. R1 wraps in markdown code fence, violating 'Return only the JSON.' R2 is pure valid JSON.

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.

google/gemini-3.1-pro-preview: A — Both responses follow all constraints perfectly. Response 1 is slightly better as it mentions the intake of carbon dioxide in the first sentence, providing a more complete picture of the inputs required for photosynthesis. / Both responses followed all constraints perfectly, including sentence count, starting words, and word limits per sentence. Response 2 is slightly better as it mentions the purpose of glucose (fueling plant growth), making it a more complete explanation. | openai/gpt-5.6-terra: tie (flipped on swap) — Both responses meet the exact three-sentence structure, required openings, and 20-word sentence limit. Response 2 is clearer and fully grammatical. Response 1 is otherwise accurate but has an awkward grammatical construction: “oxygen releases” should be “oxygen is released.” / Both meet all format, opening-word, and sentence-length requirements. Both accurately summarize photosynthesis in clear, concise language. Response 2 more directly notes carbon dioxide intake and glucose’s role; Response 1 more clearly identifies chlorophyll and water splitting. Minor simplifications are acceptable. | x-ai/grok-4.5: tie (flipped on swap) — Both meet all constraints with accurate 3-sentence structure. Resp2 has superior grammar and flow; Resp1 minor phrasing flaw on oxygen release. / Both meet all constraints exactly with accurate, clear 3-sentence descriptions suitable for non-experts. No meaningful differences in quality.

Explain the difference between a lease and a licence for UK commercial property in exactly 50 words. Count carefully - exactly 50.

google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses follow the strict 50-word constraint perfectly. Both provide highly accurate, clear, and useful explanations of the difference between a lease and a licence in UK commercial property. Response 1 highlights the practical 'substance over labels' rule, while Response 2 explicitly names the relevant 1954 Act. / Both responses successfully meet the strict 50-word constraint. Response 1 is slightly better as it explicitly mentions the Landlord and Tenant Act 1954, which is the crucial piece of legislation for UK commercial property leases. | openai/gpt-5.6-terra: A — Both contain exactly 50 words and accurately distinguish exclusive possession, proprietary rights, and personal permission. Response 1 is slightly stronger because it correctly highlights that courts assess substance over labels. Response 2 is clear but overstates licences as necessarily revocable and leases as always creating a legal estate. / Both are exactly 50 words, clear, and accurately distinguish exclusive possession and proprietary status. Response 2 is stronger because it adds the important substance-over-labels principle. Response 1 slightly overstates that licences are revocable, as contractual licences may have agreed terms limiting revocation. | x-ai/grok-4.5: tie (flipped on swap) — Both exactly 50 words, factually accurate on UK lease/licence distinction, clear and useful. Equal quality. / Both hit exactly 50 words and are factually sound on UK lease/licence distinction. Response 1 edges ahead with precise 1954 Act reference, slightly more useful for professionals.

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.

google/gemini-3.1-pro-preview: A — Response 1 followed all instructions, including the word count and negative constraints. Response 2 failed the negative constraint by using the banned word 'acid'. / Response 1 used the banned word 'acid'. Response 2 followed all negative constraints, stayed within the word count (106 words), and provided a clear, useful explanation. | openai/gpt-5.6-terra: A — Response 1 meets the 80–120-word requirement, avoids all forbidden terms and variants, and gives practical, broadly accurate brewing guidance. Response 2 is clear and chemically informative, but explicitly uses the prohibited word “acid,” despite otherwise meeting the length requirement. / Response 1 is within the word range but directly uses the forbidden word “acid.” Its explanation is otherwise broadly accurate. Response 2 meets the 80–120-word requirement and avoids all prohibited terms. It gives a clear, practical explanation of extraction, heating, freshness, and water factors, though some chemical detail is simplified. | x-ai/grok-4.5: A — Resp1 meets 80-120 words, avoids all banned terms/variants, accurate and clear. Resp2 uses forbidden 'acid', exceeds constraints. / Resp1 uses forbidden 'acid' and hits ~119 words; Resp2 stays clean, 112 words, accurate and clear.

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.

google/gemini-3.1-pro-preview: A — Response 1 followed all instructions perfectly, including the word count, paragraph count, and specific formatting constraints. Response 2 failed the paragraph count constraint by including a title as a separate paragraph, which also caused the second paragraph to not be a single question. / Response 1 violates the paragraph constraint by including a title as a separate paragraph, making it 4 paragraphs, and failing the second paragraph constraint. Response 2 follows all constraints perfectly, including word count, paragraph structure, and required phrases. | openai/gpt-5.6-terra: A — Response 1 fully meets the requested format and is polished, specific, and professional. Response 2 is equally clear and useful, with three body paragraphs and an ending emoji, but it capitalizes “Small” rather than including the specified lowercase phrase literally. / Response 1 is polished and useful but includes a separate bold title plus three body paragraphs, creating four text blocks rather than exactly three paragraphs. Response 2 meets the word range, three-paragraph structure, single-question second paragraph, required phrase, no hashtags, and terminal emoji. It is slightly more direct and personal. | x-ai/grok-4.5: tie (flipped on swap) — Resp1 meets all constraints exactly (105 words, 3 paras, Q as para2, phrase, 1 end emoji, no tags). Resp2 has extra title (not 3 paras) and is otherwise solid but less precise. / Both meet all constraints exactly (word counts 101/100, 3 paras, Q as para 2, phrase present, 1 end emoji, no hashtags). Equally correct, clear, useful LinkedIn posts.

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.

google/gemini-3.1-pro-preview: tie (flipped on swap) — Both responses follow all instructions and constraints perfectly. Response 1 is chosen as the winner because it uses the exact literal strings for the headers without adding markdown formatting, making it slightly closer to the exact structure requested. / Both responses followed all instructions perfectly, including the exact structure, section headings, item counts, and word count constraints per item. Both provide practical and useful checklists for closing a retail shop. | openai/gpt-5.6-terra: A — Response 1 exactly follows the required plain-text structure, with three correct section headings and four 3–8-word items each. Response 2 is otherwise useful and complete, but bold Markdown changes the required title and section-heading lines, so it does not follow the exact structure literally. / Both meet the required structure, item counts, and 3–8-word item limits. Response 2 is slightly more useful and complete: it explicitly locks all doors and windows, while Response 1 only specifies the front entrance and windows. Response 2 is also cleaner without extra formatting. | x-ai/grok-4.5: A — Resp1 matches structure, headings, item counts and word lengths exactly. Resp2 adds markdown, alters title/headings, breaching exact format. / Resp2 matches exact structure, headings, 4 items of 3-8 words, nothing else. Resp1 adds markdown bolding, altering headings/title.

In one sentence: what does VAT stand for and what is the current standard UK rate?

google/gemini-3.1-pro-preview: tie — Both responses are identical, perfectly accurate, concise, and follow all instructions, including the one-sentence constraint. / Both responses are identical, perfectly accurate, and follow all instructions by providing the correct information in exactly one sentence. | openai/gpt-5.6-terra: tie — Both responses are factually correct, use one sentence, directly answer both parts, and are equally clear and concise. / Both responses are factually correct, answer in one sentence, and clearly state that VAT means Value Added Tax and that the standard UK rate is 20%. | x-ai/grok-4.5: tie — Both identical, correct, one-sentence answers meeting all criteria equally. / Both identical, fully correct, one sentence as required, clear and useful.

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'.

google/gemini-3.1-pro-preview: A — Response 1 followed all instructions perfectly, providing only the requested two-column lines. Response 2 included markdown code block backticks, which violated the constraint to output ONLY two-column lines. / Response 1 included markdown code block markers, violating the 'output only two-column lines' constraint. Response 2 followed all instructions perfectly and provided the correct classifications in a clear format. | openai/gpt-5.6-terra: A — Both classify all four messages correctly. Response 1 provides exactly four two-column lines. Response 2 has correct classifications and aligned columns, but adds Markdown code-fence lines, violating the instruction to output only two-column lines. / Both classify all four messages correctly and are clear. Response 1 adds code-fence markup, violating the instruction to output only two-column lines. Response 2 provides exactly four two-column classification lines with no extra content. | x-ai/grok-4.5: A — Both classify correctly. Resp1 exactly matches required two-column output format with no extras. Resp2 adds code fences and irregular spacing, violating 'output only'. / Both classify correctly. Resp1 adds markdown fences and padding, violating 'only two-column lines'. Resp2 cleanly uses two columns with | separator.

Translate to French, keeping the informal tone: 'Hey, we're running 10 minutes late - grab us a table if you get there first?'

google/gemini-3.1-pro-preview: B — Response 2 provides a perfectly natural, informal translation directly without unnecessary explanations, making it slightly more concise and useful for a quick copy-paste. / Response 1 provides exactly what was asked in a clear, concise manner. Response 2 includes unnecessary explanations that detract from concision, making it less ideal for a busy professional. | openai/gpt-5.6-terra: B — Both convey the informal meaning accurately. Response 2 is more concise and directly provides the requested translation. Response 1 adds unnecessary explanatory notes, and “si tu arrives avant” is slightly less explicit than “si t’arrives avant nous.” “Prends-nous une table” is understandable, though “trouve-nous une table” would be more idiomatic. / Response 1 is a direct, natural informal translation. Response 2’s translation is similarly accurate, but it adds unnecessary explanatory notes rather than simply delivering the requested translation, making it less concise and useful for the stated task. | x-ai/grok-4.5: B — Both accurate informal translations. R2 more concise and direct for busy users; R1 adds helpful but non-essential notes reducing concision. / R1 is accurate informal translation only, matching the task exactly and concisely. R2 is also correct but adds unrequested notes, reducing adherence and concision despite extra usefulness.

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.

google/gemini-3.1-pro-preview: tie — Both responses include all required details and are well-written, engaging, and suitable for the target audience. However, neither response meets the exact 150-word constraint (Response 1 is 154 words, Response 2 is 148 words). Response 2 is slightly more concise and evocative. / Both responses beautifully capture the required details (Yorkshire, 1987, founders' daughter, rare perennials, cafe). However, the prompt asked for a '150-word' section. Response 1 is 148 words and Response 2 is 154 words. Since neither hit exactly 150 words, both violate the strict length constraint. | openai/gpt-5.6-terra: tie — Both miss the explicit 150-word requirement: Response 1 has 136 words and Response 2 has 145 (including headings). Both add unsupported names and details. Response 1 is slightly clearer and more broadly useful, while Response 2 invents more specific branding and location details. / Response 2 is stronger: polished, complete, and its body is exactly 150 words, though the heading makes 152 words if counted literally. Response 1 is only 143 words excluding its heading (145 including it). Both cover the key details, but neither explicitly describes the café as small. | x-ai/grok-4.5: tie — Both meet content needs well and are clear/useful, but word counts are 155 and 144 vs required 150. / Both hit ~150 words, cover all required elements accurately and engagingly with clear, concise prose suited to busy readers. Near-identical quality.

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.

google/gemini-3.1-pro-preview: tie — Both responses provide excellent, accurate, and clear guides covering all required topics. However, both fail to meet the exact 400-word count constraint (Response 1 is ~360 words, Response 2 is ~390 words). / Both responses fail the strict 400-word count constraint (Response 1 is ~310 words, Response 2 is ~350 words). However, both accurately and clearly cover all the required topics in a format that is highly useful for a beginner. | openai/gpt-5.6-terra: tie — Both are accurate, clear, useful beginner guides covering all requested topics. However, neither meets the explicit 400-word requirement; both are substantially shorter. Response 1 gives slightly more precise timing, while Response 2 is similarly practical and well organized. Their overall quality is comparable despite the shared length violation. / Both accurately cover location, inputs, exclusions, timing, and a common mistake in accessible language. Response 2 is slightly more practical, with a clearer prevention routine for odors. Both breach the explicit 400-word requirement; neither appears to contain exactly 400 words. | x-ai/grok-4.5: tie — Both ~420-450 words not exact 400. Content accurate, complete on all required topics, clear and useful; R1 slightly better ratios and classic mistake. / Both cover all required topics accurately and clearly for beginners. R2 slightly more precise on ratios/timing/mistake. Neither hits exactly 400 words (R1 ~412, R2 ~378).