Claude Sonnet 4.6 vs GPT-6.1 Sol
Full profiles: Claude Sonnet 4.6 (Anthropic) and GPT-6.1 Sol (OpenAI): every benchmark, price and rank.
Updated · Data from 14 sources
As of 14 Nov 2026, GPT-6.1 Sol is ahead of Claude Sonnet 4.6 on quality (66.6 vs 58.3 on the BenchLeader Index), and GPT-6.1 Sol is 1.5× cheaper at $4.00 vs $6.00 per 1M tokens blended. GPT-6.1 Sol leads in every category we measure both in.
Key facts (as of 14 Nov 2026)
- Claude Sonnet 4.6 scores 58.3 on the BenchLeader Index and GPT-6.1 Sol scores 66.6 (BenchLeader, 14 Nov 2026).
- Claude Sonnet 4.6 costs $6.00 per 1M tokens blended (list price, 14 Nov 2026).
- GPT-6.1 Sol costs $4.00 per 1M tokens blended (list price, 14 Nov 2026).
- Of the 24 benchmarks both models have, GPT-6.1 Sol leads 19 (BenchLeader, 14 Nov 2026).
- On Arena WebDev, Claude Sonnet 4.6 is rated 1522 and GPT-6.1 Sol 1743 (LMArena, as published by 14 Nov 2026).
Claude Sonnet 4.6 benchmarks 
GPT-6.1 Sol benchmarks Which to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | GPT-6.1 Sol | 71 vs 55 category score; Arena WebDev 1743 vs 1522 | High |
| Agents & tool use | GPT-6.1 Sol | 64 vs 51 category score; Terminal-Bench 60.9% vs 53.4% | High |
| Maths | GPT-6.1 Sol | 72 vs 63 category score; Arena Maths 1499 vs 1461 | High |
| Reasoning | GPT-6.1 Sol | 71 vs 64 category score; GPQA Diamond 95.4% vs 87.4% | High |
| Knowledge | GPT-6.1 Sol | 72 vs 60 category score; Excel Modeling Benchmark 70.8% vs 60.1% | High |
| Images and other media | GPT-6.1 Sol | 66 vs 62 category score; Arena Vision 1286 vs 1283 | Medium |
| Chat quality (human preference) | GPT-6.1 Sol | 67 vs 61 on Arena votes | Medium |
| Lowest cost | GPT-6.1 Sol | $4.00 vs $6.00 per 1M blended; agentic coding session $0.160 vs $0.240 | – |
| Fastest first answer | Claude Sonnet 4.6 | 0.7 s (Claude Sonnet 4.6) vs 201.4 s (GPT-6.1 Sol (max)) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- GPT-6.1 Sol (max) leads on quality: 66.6 vs 58.3.
- GPT-6.1 Sol (max) is stronger in agents & tools, coding, human preference, knowledge, maths, multimodal, reasoning.
- GPT-6.1 Sol (max) is 1.5× cheaper ($4.00 vs $6.00 per 1M blended).
At a glance
| Spec | Claude Sonnet 4.6 | GPT-6.1 Sol |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Released | 17 Feb 2026 | 29 Sep 2026 |
| Context window (tokens) | 1,000,000 | 1,050,000 |
| Compared at | Claude Sonnet 4.6 | GPT-6.1 Sol (max) |
| Open weights | No | No |
| Price in / out / cached input per 1M | $3.00 / $15.00 / $0.300 | $2.00 / $10.00 / $0.100 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | Claude Sonnet 4.6 | GPT-6.1 Sol (max) | Leader |
|---|---|---|---|
| Reasoning | 63.9 | 71.4 | GPT-6.1 Sol |
| Coding | 55.2 | 71.1 | GPT-6.1 Sol |
| Agents & tools | 51.3 | 64.4 | GPT-6.1 Sol |
| Maths | 63.1 | 71.6 | GPT-6.1 Sol |
| Knowledge | 59.8 | 71.5 | GPT-6.1 Sol |
| Human preference | 60.5 | 67.1 | GPT-6.1 Sol |
| Multimodal | 62.4 | 65.6 | GPT-6.1 Sol |
| Long context | – | 66.2 | not comparable |
On one chart
Each domain on the index's own scale, where 50 is an average model. Click a domain to see the benchmarks behind it. Pick up to 5 models from the list.
Not drawn, measured for fewer than two of these models: Instruction following, Long context.
Model, click to add or removeIndex
Where sources disagree
Across the 24 benchmarks both have, Claude Sonnet 4.6 leads 4, GPT-6.1 Sol leads 19 and 1 is a tie. Every overall index that rates both puts GPT-6.1 Sol ahead.
| Overall index | Claude Sonnet 4.6 | GPT-6.1 Sol (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 58.3 | 66.6 | GPT-6.1 Sol |
| Arena Text | 1472 | 1481 | GPT-6.1 Sol |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page. Cost, tokens and time per benchmark task are measured: Artificial Analysis’ averages over its whole Intelligence Index run.
| Measure | Claude Sonnet 4.6 | GPT-6.1 Sol (max) |
|---|---|---|
| Blended price per 1M tokens | $6.00 | $4.00 |
| Input / output per 1M | $3.00 / $15.00 | $2.00 / $10.00 |
| Cached input per 1M | $0.300 | $0.100 |
| Price per task: chat reply | $0.0057 | $0.0038 |
| Price per task: summarize a 30-page report | $0.045 | $0.030 |
| Price per task: code edit | $0.041 | $0.027 |
| Price per task: agentic coding session | $0.240 | $0.160 |
| Price per task: structured extraction | $0.0090 | $0.0060 |
| Agentic coding session, input mostly cached | $0.118 | $0.074 |
| Cost per benchmark task (AA Index) | – | $1.27 |
| Output tokens per task (AA Index) | – | 68k |
| Of which reasoning | – | 49k |
| Time per task (AA Index) | – | 24 min |
| Output speed | 44 tok/s | 48 tok/s |
| Time to first answer | 0.7 s | 201.4 s |
| Time to a complete answer | – | 478.5 s |
Each model is compared at its best-ranked reasoning-effort setting. Claude Sonnet 4.6 scores 58.3 and takes 1 s to its first answer token; Claude Sonnet 4.6 (max) scores 54.0 in 123 s, Claude Sonnet 4.6 (high) scores 50.3 in 1 s. GPT-6.1 Sol (max) scores 66.6 and takes 201 s to its first answer token; GPT-6.1 Sol (xhigh) scores 63.6 in 86 s, GPT-6.1 Sol (high) scores 63.4 in 43 s. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
165 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | Claude Sonnet 4.6 | GPT-6.1 Sol (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 58.3 | 66.6 |
| Agents & tools score | BenchLeader | 51.3 | 64.4 |
| Coding score | BenchLeader | 55.2 | 71.1 |
| Human preference score | BenchLeader | 60.5 | 67.1 |
| Knowledge score | BenchLeader | 59.8 | 71.5 |
| Maths score | BenchLeader | 63.1 | 71.6 |
| Multimodal score | BenchLeader | 62.4 | 65.6 |
| Reasoning score | BenchLeader | 63.9 | 71.4 |
| Long context score | BenchLeader | – | 66.2 |
| Blended price $/M | BenchLeader | $6.00 | $4.00 |
| Output speed | BenchLeader | 44 tok/s | 48 tok/s |
| Time to first answer | BenchLeader | 0.7 s | 201.4 s |
| Context window | BenchLeader | 1,000,000 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 87.4% | 95.4% |
| SimpleBench | SimpleBench | – | 82.9%† |
| Arena Hard Prompts | Arena (LMArena) | 1503 | 1505 |
| LiveBench 2.0 Reasoning | LiveBench | – | 92.6%† |
| LiveBench 1.0 Reasoning | LiveBench | 84.8%† | 92.6% |
| GPQA Diamond (AA) | Artificial Analysis | 87.5%† | – |
| Humanity's Last Exam (AA) | Artificial Analysis | 33.6%† | 52.9% |
| HLE-Diamond (AA) | Artificial Analysis | – | 58.8% |
| GPQA Diamond (Vals) | Vals AI | 85.6% | – |
| ARC-AGI-1 | ARC Prize | 86.0%† | 96.5% |
| ARC-AGI-2 | ARC Prize | 58.3%† | 94.2% |
| ARC-AGI-3 | ARC Prize | – | 96.2% |
| CritPt | Artificial Analysis | 3.1%† | 31.7% |
| CritPt v2 hard subset (AA) | Artificial Analysis | – | 69.5% |
| MysteryMechanism | Vals AI | – | 46.4% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 23.0%† | 68.0% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 14.0% | 80.0% |
| LMCA | Epoch AI Benchmarking Hub | 46.5%† | 62.8% |
| DTBench | Epoch AI Benchmarking Hub | 89.9%† | 96.5% |
| ForecastBench | ForecastBench | 64.0% | – |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | 75.2% | – |
| SciCode | SciCode | 46.8%† | 54.2% |
| WeirdML | WeirdML | 66.1%† | – |
| FrontierCode | Cognition | 24.3% | 50.2%† |
| Arena Coding | Arena (LMArena) | 1528 | 1540 |
Show all 165 benchmarks
| Metric | Publisher | Claude Sonnet 4.6 | GPT-6.1 Sol (max) |
|---|---|---|---|
| Coding | |||
| Arena WebDev | Arena (LMArena) | 1522 | 1743 |
| LiveBench 2.0 Coding | LiveBench | – | 82.1%† |
| LiveBench 1.0 Coding | LiveBench | 79.3%† | 80.4% |
| SciCode (AA) | Artificial Analysis | 50.1%† | 54.2% |
| LiveCodeBench | Vals AI | 82.1% | – |
| IOI | Vals AI | – | 96.9% |
| SWE-bench (Vals) | Vals AI | 77.4% | – |
| Code Migration | Vals AI | 39.9% | 65.1% |
| Code Migration v1.1 | Vals AI | – | 77.3% |
| ProgramBench | Vals AI | 0.5% | 3.0% |
| SWE-bench Multimodal | Vals AI | – | 43.5% |
| Vibe Code Bench v1.1 | Vals AI | 51.5% | 88.9% |
| SWE Atlas: Codebase QnA | Scale AI SEAL | 31.2% | – |
| SWE Atlas: Refactoring | Scale AI SEAL | 32.2% | – |
| SWE Atlas: Test Writing | Scale AI SEAL | 31.8% | – |
| MirrorCode | Epoch AI Benchmarking Hub | – | 61.1% |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | 29.9%† | – |
| ALE-Bench | Epoch AI Benchmarking Hub | 1327.3† | 2743.8 |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 3.0%† | 56.1% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 71.2%† | – |
| Startrise Frontend | Startrise | – | 78.6%† |
| GameBench 2 Overall | DuelLab GameBench 2 | – | 84.8† |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | – | 92.0† |
| Design Arena Website | Design Arena | 1277 | 1316† |
| FrontierCode 1.1 (AA) | Artificial Analysis | – | 49.1% |
| FrontierCode 2.0 (AA) | Artificial Analysis | – | 40.6% |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 53.4% | 60.9% |
| OSWorld-Verified 2.0 | OSWorld | 8.3%† | – |
| APEX-Agents | Mercor | 43.0%† | 60.0% |
| Arena Agent | Arena (LMArena) | – | 12.5 |
| LiveBench Long Agentic | LiveBench | – | 65.6%† |
| LiveBench Browser | LiveBench | – | 93.3%† |
| LiveBench Agentic Coding | LiveBench | 42.6%† | 54.5% |
| Terminal-Bench Hard | Artificial Analysis | 53.0%† | – |
| τ²-Bench Telecom (AA) | Artificial Analysis | 75.7%† | – |
| Terminal-Bench 2.1 (Vals) | Vals AI | 57.3% | – |
| MCP Atlas | Scale AI SEAL | 69.5% | – |
| HiL-Bench | Scale AI SEAL | – | 62.3%† |
| GDPval-AA v2.1, normalised score | Artificial Analysis | – | 54.8% |
| GDPval-AA v2.1 | Artificial Analysis | – | 1607 |
| τ³-Banking (AA) | Artificial Analysis | 34.4%† | – |
| ITBench SRE (AA) | Artificial Analysis | 39.8%† | – |
| Analyst Agent (AA) | Artificial Analysis | 20.0%† | 50.0% |
| APEX-Agents (AA) | Artificial Analysis | 28.0%† | – |
| CyberBench | Vals AI | – | 39.3% |
| Finance Agent v2 | Vals AI | 51.0% | 52.0% |
| Finance Agent v2.1 | Vals AI | – | 61.8% |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | – | 6.3% |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 5.0% | 5.4% |
| Legal Research Bench | Vals AI | 38.5% | 38.5% |
| Robo-bench | Vals AI | – | 28.3% |
| SkillsBench | Vals AI | 49.0% | – |
| SREBench | Vals AI | – | 50.8% |
| Tax Agent Bench | Vals AI | 25.8% | 21.8% |
| Terminal-Bench 2.0 (Vals) | Vals AI | 59.5% | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | – | 55.0% |
| Terminal-Bench Science | Vals AI | – | 52.9% |
| Arena Search | Arena (LMArena) | 1201 | 1249 |
| DeepResearch Bench | DeepResearch Bench | 54.9%† | – |
| PostTrainBench | Epoch AI Benchmarking Hub | – | 38.8% |
| ExploitBench | Epoch AI Benchmarking Hub | 23.6% | – |
| GBAEval | Epoch AI Benchmarking Hub | 48.8% | – |
| Vending-Bench 2 | Andon Labs | 7204.1 | 12860.6† |
| AutomationBench | Artificial Analysis | 20.1%† | 64.9% |
| GDP.pdf (AA) | Artificial Analysis | 15.8%† | 31.0% |
| Harvey LAB-AA v1.1 | Harvey | – | 6.9% |
| AA-Briefcase v1.1 | Artificial Analysis | 1062† | 1555 |
| GDPx | GDPx by SemiAnalysis | – | 8.4% |
| Design Arena Agentic | Design Arena | 1181 | 1381 |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | – | 58.1% |
| FACTS Search | – | 92.8%† | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | – | 93.7% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | – | 100.0% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 85.8% | 100.0% |
| ProofBench | Vals AI | 45.0%† | 99.0%† |
| LiveBench Mathematics | LiveBench | 87.0%† | 96.8% |
| AIME (Vals) | Vals AI | 92.3% | – |
| Arena Maths | Arena (LMArena) | 1461 | 1499 |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 32.8%† | 73.9% |
| LiveBench 2.0 Data Analysis | LiveBench | – | 82.7%† |
| LiveBench Research | LiveBench | – | 90.0%† |
| LiveBench 1.0 Data Analysis | LiveBench | 78.0%† | 82.7% |
| AA-Omniscience v1.1 | Artificial Analysis | – | 42.0 |
| MMLU-Pro | Vals AI | 87.3% | – |
| LegalBench | Vals AI | 82.1% | – |
| CorpFin | Vals AI | 65.3% | – |
| TaxEval | Vals AI | 77.1% | – |
| MedQA | Vals AI | 92.1% | – |
| PRBench Finance | Scale AI SEAL | – | 46.2%† |
| PRBench Legal | Scale AI SEAL | – | 47.9%† |
| BioMysteryBench | Vals AI | – | 79.6% |
| CaseLaw v2 | Vals AI | 64.0% | – |
| ClaimBench | Vals AI | – | 100.0% |
| Excel Modeling Benchmark | Vals AI | 60.1% | 70.8% |
| Excel Modeling Benchmark v1.1 | Vals AI | – | 71.1% |
| MedCode | Vals AI | – | 48.8% |
| MedScribe | Vals AI | – | 86.5% |
| Public Benefits Bench v1.1 | Vals AI | 62.5% | 59.3% |
| DrugDiscoveryBench | Scale AI SEAL | 31.3%† | – |
| EBR-bench | Epoch AI Benchmarking Hub | – | 54.3% |
| GDP.pdf | Epoch AI Benchmarking Hub | 18.0%† | 35.0% |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | – | 61.8% |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | – | 48.1% |
| Global-MMLU-Lite (AA) | Artificial Analysis | 90.5%† | – |
| HealthBench Professional (AA) | OpenAI | – | 65.1% |
| Instruction following | |||
| LiveBench Language | LiveBench | 76.1%† | 90.1% |
| LiveBench Instruction Following | LiveBench | 63.2%† | 74.2% |
| IFBench | Artificial Analysis | 56.6%† | – |
| MultiChallenge | Scale AI SEAL | – | 82.0%† |
| Arena Instruction Following | Arena (LMArena) | 1475 | 1482 |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1472 | 1481 |
| EQ-Bench 4 | EQ-Bench | 1197 | 1272† |
| Arena Creative Writing | Arena (LMArena) | 1449 | 1449 |
| Arena Multi-turn | Arena (LMArena) | 1480 | 1486 |
| Arena Longer Queries | Arena (LMArena) | 1492 | 1488 |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1283 | 1286 |
| LiveBench Design | LiveBench | – | 79.6%† |
| MMMU-Pro | Artificial Analysis | 73.3%† | 86.0% |
| MMMU-Pro (Vals) | Vals AI | 83.6% | – |
| MortgageTax | Vals AI | 67.7% | – |
| SAGE | Vals AI | – | 90.5% |
| GeoBench | GeoBench | 73.0% | – |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | 6.7% | 43.2%† |
| Code2Video-AA | Artificial Analysis | – | 1369 |
| AA-3D | Artificial Analysis | – | 1121 |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 80.0%† | 83.0% |
| Arena Document | Arena (LMArena) | 1478 | – |
| MLCR | Artificial Analysis | 7.8%† | 38.3% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 152.9 | 168.5† |
| LiveBench 2.0 | LiveBench | – | 81.4%† |
| LiveBench 1.0 | LiveBench | 73.0%† | 81.6% |
| AA Intelligence Index v5.0 | Artificial Analysis | – | 54.6 |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 30.1† | 51.8 |
| AA Progress Index v1 | Artificial Analysis | 293.6† | 348.5 |
| Vals Index | Vals AI | – | 63.9 |
| Vals Finance Index | Vals AI | – | 55.8% |
| Vals RSI Index | Vals AI | – | 26.6% |
| Vals Science Index | Vals AI | – | 59.6% |
| Vals Multimodal Index | Vals AI | 60.6% | – |
| Vals Router Index | Vals AI | – | 63.9%† |
Data as of 2026-11-14. Best configuration of each model; every score links to its source on the model pages.
DuelLab GameBench 2, release gb2-20261110-2acac640a228 (https://benchmarks.duellab.org/releases/gb2-20261110-2acac640a228/), CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Scores mapped to BenchLeader model names; not endorsed by DuelLab.
What changed
- GPT-6.1 Sol (max): 1 score revision.
- GPT-6.1 Sol (max): 1 score revision.
- GPT-6.1 Sol (max): 1 new result (PostTrainBench).
- Claude Sonnet 4.6: 1 new result (GeoBench).
- GPT-6.1 Sol (max): 1 new result (MirrorCode); 1 score revision.
- GPT-6.1 Sol (max): BenchLeader Index 67.4 → 66.9.
- GPT-6.1 Sol (max): 2 score revisions.
- Claude Sonnet 4.6: 1 new result (Arena Search).
- GPT-6.1 Sol (max): 3 score revisions.
- GPT-6.1 Sol (max): 1 score revision.
Related comparisons
- Claude Sonnet 4.6 vs Gemini 4 Carbon
- GPT-6.1 Sol vs Claude Fable 5.2
- GPT-6.1 Sol vs Gemini 4 Carbon
- GPT-6.1 Sol vs Claude Opus 5.5
- GPT-6.1 Sol vs GPT-6 Astra
- Claude Opus 4.6 vs GPT-6.1 Sol
- Claude Sonnet 4.6 vs Claude Sonnet 5.5
- Claude Sonnet 4.6 vs Muse Spark 1.3
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
Claude Sonnet 4.6 vs GPT-6.1 Sol: questions
- Is GPT-6.1 Sol better than Claude Sonnet 4.6?
- GPT-6.1 Sol (max) leads on quality: 66.6 vs 58.3. The BenchLeader Index combines every independent quality benchmark; GPT-6.1 Sol (max) is ahead overall as of 14 Nov 2026, but check the category scores for your use.
- Claude Sonnet 4.6 vs GPT-6.1 Sol: which is cheaper, and will that change?
- GPT-6.1 Sol is cheaper today: $4.00 against $6.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Claude Sonnet 4.6 or GPT-6.1 Sol better for coding?
- GPT-6.1 Sol scores higher in coding (71 vs 55 on the category index, where 50 is average). On Arena WebDev, the top shared coding benchmark, it is 1522 for Claude Sonnet 4.6 against 1743 for GPT-6.1 Sol.
- Which is faster, Claude Sonnet 4.6 or GPT-6.1 Sol?
- GPT-6.1 Sol streams faster (48 vs 44 output tokens per second, Artificial Analysis 7-day median).
- Is Claude Sonnet 4.6 or GPT-6.1 Sol better for agents and tool use?
- GPT-6.1 Sol scores higher in agentic tasks (64 vs 51 on the category index, where 50 is average). On Terminal-Bench, the top shared agents & tools benchmark, it is 53.4% for Claude Sonnet 4.6 against 60.9% for GPT-6.1 Sol.
More questions (4)
- Which is better for long documents?
- Both take about 1.1M tokens: Claude Sonnet 4.6 1,000,000, GPT-6.1 Sol 1,050,000.
- Which has lower latency?
- Claude Sonnet 4.6 answers first: 0.7 s against 201.4 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- Do independent benchmarks agree on Claude Sonnet 4.6 vs GPT-6.1 Sol?
- Across the 24 benchmarks both have, Claude Sonnet 4.6 leads 4, GPT-6.1 Sol leads 19 and 1 is a tie. Every overall index that rates both puts GPT-6.1 Sol ahead.
- When was GPT-6.1 Sol released?
- GPT-6.1 Sol was released on 29 Sep 2026.