Head to head: xAI: Grok 4.5 vs OpenAI: GPT-5.6 Sol Pro

In a head-to-head test of 12 text tasks, OpenAI's GPT-5.6 Sol Pro scored 112.0 to xAI's Grok 4.5's 106.1, winning 5 tasks to 3 with 4 ties, giving OpenAI a narrow but real edge. Sol Pro excelled in implementation-heavy tasks like SQL queries and code debugging, while Grok 4.5 showed strengths in instruction-following and editing. The result is a qualified win for OpenAI, not a knockout.

The scoreboard says GPT-5.6 Sol Pro wins , and that’s the right read — with the right level of restraint. It leads 112.0 to 106.1 , takes 5 tasks to Grok 4.5’s 3 , with 4 ties , and the statistical verdict lands at 75% confidence lean . In other words: OpenAI was better overall, but this was not a demolition. It was a narrow but real edge earned across a broad mix of practical tasks. Where Sol Pro separated itself was in implementation-heavy work. It got the nod on the SQL window query , LRU cache , JS LRU cache , python log rollover bug , and incident-field extraction . That pattern matters. These are the kinds of tasks where small mistakes — missing aliases, brittle edge-case handling, sloppy normalization, output-format drift — turn a plausible answer into a weaker one. Sol Pro was more often the model that landed the cleaner, more production-ready result. Grok 4.5, though, was hardly outclassed. It won meeting-notes-3-bullets by being more faithful to the exact constraints, and it took precise proofreading by following the requested output format more tightly. It also won summarize dense passage , which suggests it still has real strengths in compression and editorial judgment. Just as important, several of Sol Pro’s wins came with caveats from the order-swapped judge passes, and a few tasks flipped on markdown fences or edge cases rather than deeper reasoning superiority. That keeps this result firmly in "lean win" territory. The ties reinforce the picture. On csv-to-clean-json , vendor delay status update , constraint scheduling , and find the contradiction , neither model opened meaningful daylight. So the deciding factor here wasn’t that one model could do things the other couldn’t. It was that Sol Pro was a bit more dependable across the full suite, especially when code correctness and structured extraction were on the line, while Grok was sharper on a couple of instruction-following and editing tasks. Final call: OpenAI’s GPT-5.6 Sol Pro wins on points, not by knockout — the stronger all-around performer here, but only by a modest, confidence-qualified margin. How they were tested We ran 12 fresh text tasks, generated on the fly for this matchup so neither model could prepare in advance, and had gpt-5.4 score each one. xAI: Grok 4.5 scored 106.1 to OpenAI: GPT-5.6 Sol Pro's 112.0. 1. meeting-notes-3-bullets Summarization. Summarize the meeting notes below into EXACTLY 3 bullet points. Each bullet must be one sentence, 12–18 words, and include only decisions or confirmed facts—no speculation. Source passage: "Ops sync, Tuesday 09:00. Lena confirmed the Maybridge clinic migration finished Sunday at 22:14 with 17 minutes of read-only time, shorter than the planned 30. Two barcode scanners in Receiving still drop Wi‑Fi after sleep; Raj will push firmware 2.1.8 tonight to test whether it fixes that issue. The claims export bug was traced to a timezone mismatch in the Cedar billing adapter; patch merged, production deploy scheduled for Thursday after 19:00 local time. We are not changing the patient portal release date yet because legal still has open wording questions on the consent screen. Finance approved buying three replacement label printers, capped at $1,650 total. Next check-in moved from Friday to Monday because the warehouse lead is out." Winner: xAI: Grok 4.5 — Model A is fully accurate and keeps each bullet to a single confirmed fact while meeting the length and bullet-count constraints. Model B includes multiple facts per bullet and its first bullet is only 11 words, violating the 12–18 word requirement. Order-swapped judge pass: Model A better satisfies the requirement to capture decisions or confirmed facts in exactly three bullets while covering more key confirmed items from the notes. Model B is accurate but omits several important confirmed decisions/facts and one bullet falls below the 12-word minimum. 2. SQL window query Given Postgres tables orders id, customer id, created at, total cents and customers id, name , write ONE SQL query returning each customer's name and their SECOND-largest order total in dollars, 2 decimals , excluding customers with fewer than two orders. Order by that value descending. Return ONLY the SQL. Winner: OpenAI: GPT-5.6 Sol Pro — Both queries correctly use a window function to return the second-highest order per customer and exclude customers with fewer than two orders. Model B is slightly better because it aliases the computed column clearly and presents a cleaner final result, while Model A omits an output alias and wraps the SQL in markdown despite the instruction to return only SQL. Order-swapped judge pass: Both queries correctly return each customer's second-highest order total and exclude customers with fewer than two orders via ROW NUMBER = 2. Model A is better because it follows the instruction to return only SQL, while Model B includes Markdown code fences, which violates the format requirement. 3. csv-to-clean-json Data wrangling / structured output. Transform the messy inline data into valid JSON only. Output an object with one key, orders , whose value is an array of objects sorted by order id ascending. Each object must have exactly these keys: order id string , customer string , items array of strings , priority "low"|"normal"|"high" , total usd number , paid boolean . Rules: trim whitespace; split items on | ; normalize priority to lowercase; parse total as a number without $ ; paid is true for yes/y/paid and false for no/n/unpaid. Data: A-103 , Kestrel Bio , swabs|labels|coolant pack , High , $148.50 , yes A-099, Norvine Foods, gasket set , normal,$42, unpaid A-104, Kestrel Bio, swabs | absorbent sheet, HIGH , 87.00, Y A-101, Elm Harbor School , toner cartridge| paper ream | staplers , low , $63.25 , no Winner: Tie — Both outputs are fully correct: they produce valid JSON only, include exactly the required schema, normalize and parse all fields properly, and sort the orders by order id ascending. The only difference is formatting style, which does not affect task quality here. Order-swapped judge pass: Both outputs are valid JSON objects with the required schema, correctly normalized fields, properly parsed totals and paid status, and sorted orders ascending by order id. Neither contains extraneous content, so they are equally strong for this task. 4. LRU cache Implement a class LRUCache<K, V in TypeScript with a fixed capacity set in the constructor, and O 1 get key : V | undefined and set key, value : void . Accessing or updating a key must mark it most-recently-used; inserting beyond capacity must evict the least-recently-used entry. Return ONLY the class, no prose. Winner: OpenAI: GPT-5.6 Sol Pro — Both implement O 1 LRU behavior using Map insertion order, but B is more robust: it validates capacity and correctly handles capacity 0 without briefly inserting and deleting an entry. A is mostly correct but has an edge-case flaw when capacity is 0. Order-swapped judge pass: Model A is fully correct, handles edge cases like zero capacity and invalid capacity cleanly, and returns only the class as requested. Model B’s core logic is mostly fine but violates the output-format instruction by including Markdown code fences and mishandles zero capacity by deleting an undefined key before inserting. 5. Summarize dense passage Summarize the passage below in exactly three bullet points, each one sentence, capturing the mechanism, the tradeoff, and the caveat — no jargon a non-specialist couldn't follow. Return only the bullets. Passage: "Speculative decoding pairs a small draft model with a large target model: the draft proposes several tokens, the target verifies them in one pass, and accepted tokens are kept while the first rejection resets to the target's own choice. This can cut latency substantially when acceptance is high, but the draft model's compute is pure overhead when acceptance is low, and gains evaporate on adversarial or highly novel inputs where the draft and target disagree often." Winner: xAI: Grok 4.5 — Both are accurate and follow the format, but B is slightly clearer and more concise while still covering the mechanism, tradeoff, and caveat. A is also strong, though "upcoming words" and "switching to its own word" are a bit less precise and slightly wordier. Order-swapped judge pass: Both are accurate and follow the format, but B is slightly clearer and more complete in describing the first-disagreement behavior while staying accessible to non-specialists. A is also strong, but its phrasing is a bit less precise and natural. 6. vendor-delay-status-update Professional writing. Draft a status update email to an internal sales team. Situation: a vendor named Northline Circuits informed us this morning that custom relay boards for order QP-4471 will ship 9 days late because their solder mask inspection failed. This delays our Falcon Ridge kiosk installations for Brookmere Transit from Aug 12 to Aug 21. We have enough spare boards to keep the Denver pilot on schedule, but not the wider rollout. Audience: account managers who will talk to the customer today. Tone: calm, direct, accountable. Length: 130–160 words. Include: what happened, customer impact, what we are doing next, and one clear ask for the sales team. Winner: Tie — Both drafts are accurate and professional, but B is slightly stronger because it is more polished, explicitly accountable, and gives a clearer, more actionable ask without adding unnecessary specifics. A is solid but introduces an unsupported promise about a 4 p.m. action plan and is a bit less crisp. Order-swapped judge pass: Both are strong, accurate drafts, but B better matches the audience and ask by explicitly directing account managers to contact Brookmere today and gather impact details. A is polished but its ask is narrower and less aligned with the stated purpose of preparing the team for customer conversations. 7. Constraint scheduling Four talks A, B, C, D fill four 1-hour slots 9,10,11,12. Constraints: A is before D; C is not first; B is immediately after A; D is not at 12. Give the ONE valid schedule as 'slot: talk' lines, then a one-line justification. If impossible, say so and explain. Winner: Tie — Both outputs give the unique valid schedule and a concise justification that satisfies the format and instruction constraints. Neither has a meaningful quality or correctness advantage over the other. Order-swapped judge pass: Both outputs give the unique valid schedule and include a concise one-line justification that satisfies the format and constraints. Neither has a meaningful quality or correctness advantage over the other. 8. js-lru-cache Algorithm & data structures — JavaScript. Implement an LRU cache class with this API: - constructor capacity - get key - returns value or -1 - put key, value Both operations must be O 1 average time. When over capacity, evict the least recently used item. Use a hash map plus a doubly linked list; do not use any library cache type. Return code only. Winner: OpenAI: GPT-5.6 Sol Pro — Both implement the required O 1 hash map + doubly linked list design, but B correctly handles zero or negative capacity while A can break by trying to remove the head sentinel when capacity is 0. A also wraps the code in markdown fences despite the prompt saying code only. Order-swapped judge pass: Model A fully satisfies the LRU cache requirements with correct O 1 average operations and handles edge cases like non-positive capacity safely. Model B is mostly correct but fails for capacity 0, where it attempts to remove the head sentinel and breaks, and it also includes markdown fences despite the prompt requesting code only. 9. python-log-rollover-bug Debugging & code review — Python. The function below is supposed to rotate an app log by day. It should create a new bucket whenever the calendar date changes and append events to the right day's list. There is a subtle bug that makes some events disappear. Find the bug, fix it, and return the corrected code only. python from datetime import datetime def bucket events lines : buckets = {} current day = None current = for line in lines: ts, msg = line.split " ", 1 day = datetime.fromisoformat ts .date if current day is None: current day = day if day = current day: buckets current day.isoformat = current current day = day current = current.append msg if current day is not None: buckets current day.isoformat = return buckets Winner: OpenAI: GPT-5.6 Sol Pro — Both outputs correctly fix the bug by storing the accumulated current list for the final day instead of an empty list, and both return only corrected code as requested. The code is effectively identical in quality and adherence. Order-swapped judge pass: Both outputs correctly fix the bug by storing the final day's accumulated events instead of an empty list. Model A is slightly better because it returns corrected code only, while Model B adds Markdown code fences despite the instruction to return code only. 10. Find the contradiction The following spec contains exactly one internal contradiction. Quote the two conflicting sentences verbatim and explain the conflict in one sentence. Do not fix it. Spec: "Free accounts may create up to three projects. Every account, regardless of tier, may archive unlimited projects. Archiving a project does not count against the project limit. Free accounts are limited to three projects total, including archived ones." Winner: Tie — Both outputs identify the same two conflicting sentences verbatim and explain the contradiction clearly in one sentence without attempting to fix it. The differences are purely stylistic and do not affect quality. Order-swapped judge pass: Both outputs identify the same correct contradictory sentences, quote them verbatim, and explain the conflict in one sentence without attempting to fix it. Model B is slightly more polished stylistically, but not enough to make it better for this task. 11. extract-incident-fields Information extraction. From the messy incident notes below, extract all requested fields accurately. Return ONLY a JSON object with keys: incident id , started at , resolved at , customer , affected region , services , root cause , credits offered usd , report owner . Notes: "Pager fired 03:18 UTC for Inc-7842 / sometimes written INC7842 in chat. Customer = Vellum Health Cooperative. Symptoms first reported from eu-west-3, though one rep mistakenly typed us-west-3 in the ticket title. Search API timed out and PDF rendering queued for up to 26 min; auth and webhooks looked normal. Mira says the bad deploy began at 2026-04-17 03:11Z. Rollback finished 04:02Z and latency normalized by 04:07 UTC. Root cause: a connection pool cap was lowered from 120 to 12 in release r58. Per Amadou, offer $420 service credits. Final incident write-up owner: J. Ortega." Winner: OpenAI: GPT-5.6 Sol Pro — Both outputs extract the fields accurately and follow the required JSON-only format. Model B is slightly better because it uses a more normalized incident ID and fully specified ISO 8601 timestamps while preserving all requested information. Order-swapped judge pass: Model A is fully correct and follows the requirement to return only a JSON object. Model B extracts the fields correctly but violates the format instruction by wrapping the JSON in a Markdown code block. 12. Precise proofreading Correct ONLY the grammar, spelling, and punctuation errors in the sentence below without changing wording, tone, or meaning, then on a second line list each change as 'was - now'. Sentence: "Its been a long time since we seen there new office, and me and him was suprised how quite it was." Winner: xAI: Grok 4.5 — Model A is better because it corrects the sentence and provides the required second-line change list without adding unnecessary punctuation. Model B adds quotation marks not present in the original and uses semicolons instead of the requested 'was - now' list format. Order-swapped judge pass: Both outputs make the needed corrections, but Model B better follows the requested format by placing the corrected sentence on one line and the change list on a second line without adding quotation marks. Model A is slightly less compliant because it adds quotes around the sentence and uses semicolons instead of the requested 'was - now' listing style. See every prompt and the full side-by-side outputs in the interactive Head-to-Head /head-to-head/head-to-head-xai-grok-4-5-vs-openai-gpt-5-6-sol-pro .