Claude Opus 5.5 vs GPT-5.6 Sol
Full profiles: Claude Opus 5.5 (Anthropic) and GPT-5.6 Sol (OpenAI): every benchmark, price and rank.
Updated · Data from 12 sources
As of 9 Nov 2026, Claude Opus 5.5 and GPT-5.6 Sol are neck and neck on quality (68.9 vs 65.4 on the BenchLeader Index, inside the ±5.7 margin), and they cost about the same at $8.00 vs $8.00 per 1M tokens blended. Choose Claude Opus 5.5 for coding (80 vs 66), knowledge (74 vs 64), and reasoning (72 vs 65); choose GPT-5.6 Sol for fast first answers (79.2 s vs 380.6 s) and agents & tools (67 vs 63).
Key facts (as of 9 Nov 2026)
- Claude Opus 5.5 scores 68.9 on the BenchLeader Index and GPT-5.6 Sol scores 65.4 (BenchLeader, 9 Nov 2026).
- Claude Opus 5.5 costs $8.00 per 1M tokens blended (list price, 9 Nov 2026).
- GPT-5.6 Sol costs $8.00 per 1M tokens blended (list price, 9 Nov 2026).
- Of the 42 benchmarks both models have, Claude Opus 5.5 leads 32 (BenchLeader, 9 Nov 2026).
Claude Opus 5.5 benchmarks 
GPT-5.6 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 | Claude Opus 5.5 | 80 vs 66 category score; SciCode 66.9% vs 57.1% | High |
| Agents & tool use | GPT-5.6 Sol | 67 vs 63 category score; Terminal-Bench 38.5% vs 65.8% | Medium |
| Maths | Claude Opus 5.5 | 74 vs 71 category score; FrontierMath Tiers 1–3 91.2% vs 89.1% | Medium |
| Reasoning | Claude Opus 5.5 | 72 vs 65 category score; CritPt v2 hard subset (AA) 68.0% vs 60.0% | Medium |
| Knowledge | Claude Opus 5.5 | 74 vs 64 category score; SimpleQA Verified 72.2% vs 69.7% | High |
| Long documents | Either | 67 vs 67 category score; AA-LCR v1.1 84.7% vs 84.0% | Either |
| Images and other media | Claude Opus 5.5 | 70 vs 66 category score; MMMU-Pro 87.7% vs 83.4% | Medium |
| Lowest cost | Either | $8.00 vs $8.00 per 1M blended; agentic coding session $0.320 vs $0.320 | – |
| Fastest first answer | GPT-5.6 Sol | 79.2 s (GPT-5.6 Sol (max)) vs 380.6 s (Claude Opus 5.5 (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
- Claude Opus 5.5 (max) and GPT-5.6 Sol (max) are neck and neck on quality (68.9 vs 65.4, within the ±5.7 margin).
- Claude Opus 5.5 (max) is stronger in coding, knowledge, long context, maths, multimodal, reasoning.
- GPT-5.6 Sol (max) is stronger in agents & tools, instruction following.
- They cost about the same ($8.00 per 1M blended).
At a glance
| Spec | Claude Opus 5.5 | GPT-5.6 Sol |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Released | 22 Sep 2026 | 9 Jul 2026 |
| Context window (tokens) | 1,000,000 | 1,050,000 |
| Max output (tokens) | 128,000 (up to 300K on the Message Batches API (beta)) | – |
| Input types | text, image | – |
| Reasoning effort settings | low, medium, high, xhigh, max | – |
| Compared at | Claude Opus 5.5 (max) | GPT-5.6 Sol (max) |
| Open weights | No | No |
| Price in / out / cached input per 1M | $4.00 / $20.00 / $0.200 | $4.00 / $20.00 / $0.400 |
| API model ID | claude-opus-5-5 | – |
| Where to use it | Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry | – |
Limits, availability and announced prices from the vendors' own pages: platform.claude.com (checked 20 Oct 2026). Everything else is from the day's data.
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 Opus 5.5 (max) | GPT-5.6 Sol (max) | Leader |
|---|---|---|---|
| Reasoning | 72.3 | 64.9 | Claude Opus 5.5 |
| Coding | 80.4 | 65.9 | Claude Opus 5.5 |
| Agents & tools | 62.7 | 66.8 | GPT-5.6 Sol |
| Maths | 74.0 | 70.7 | Claude Opus 5.5 |
| Knowledge | 73.7 | 64.3 | Claude Opus 5.5 |
| Instruction following | – | 71.2 | not comparable |
| Multimodal | 70.3 | 65.9 | Claude Opus 5.5 |
| Long context | 67.1 | 66.8 | level |
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, Human preference.
ModelIndex
Where sources disagree
Across the 42 benchmarks both have, Claude Opus 5.5 leads 32, GPT-5.6 Sol leads 9 and 1 is a tie. Every overall index that rates both puts Claude Opus 5.5 ahead.
| Overall index | Claude Opus 5.5 (max) | GPT-5.6 Sol (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 68.9 | 65.4 | neck and neck |
| Arena Text | 1506† | 1485 | Claude Opus 5.5 |
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 Opus 5.5 (max) | GPT-5.6 Sol (max) |
|---|---|---|
| Blended price per 1M tokens | $8.00 | $8.00 |
| Input / output per 1M | $4.00 / $20.00 | $4.00 / $20.00 |
| Cached input per 1M | $0.200 | $0.400 |
| Price per task: chat reply | $0.0076 | $0.0076 |
| Price per task: summarize a 30-page report | $0.060 | $0.060 |
| Price per task: code edit | $0.054 | $0.054 |
| Price per task: agentic coding session | $0.320 | $0.320 |
| Price per task: structured extraction | $0.012 | $0.012 |
| Agentic coding session, input mostly cached | $0.149 | $0.158 |
| Cost per benchmark task (AA Index) | $10.04 | – |
| Output tokens per task (AA Index) | 173k | – |
| Of which reasoning | 120k | – |
| Time per task (AA Index) | 25 min | – |
| Output speed | 115 tok/s | 99 tok/s |
| Time to first answer | 380.6 s | 79.2 s |
| Time to a complete answer | 771.1 s | 162.8 s |
Each model is compared at its best-ranked reasoning-effort setting. Claude Opus 5.5 (max) scores 68.9 and takes 381 s to its first answer token; Claude Opus 5.5 (xhigh) scores 65.5 in 84 s, Claude Opus 5.5 (high) scores 65.4 in 28 s. GPT-5.6 Sol (max) scores 65.4 and takes 79 s to its first answer token; GPT-5.6 Sol (xhigh) scores 64.2 in 31 s, GPT-5.6 Sol (high) scores 63.5 in 13 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
172 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 Opus 5.5 (max) | GPT-5.6 Sol (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 68.9 | 65.4 |
| Agents & tools score | BenchLeader | 62.7 | 66.8 |
| Coding score | BenchLeader | 80.4 | 65.9 |
| Knowledge score | BenchLeader | 73.7 | 64.3 |
| Long context score | BenchLeader | 67.1 | 66.8 |
| Maths score | BenchLeader | 74.0 | 70.7 |
| Multimodal score | BenchLeader | 70.3 | 65.9 |
| Reasoning score | BenchLeader | 72.3 | 64.9 |
| Instruction following score | BenchLeader | – | 71.2 |
| Blended price $/M | BenchLeader | $8.00 | $8.00 |
| Output speed | BenchLeader | 115 tok/s | 99 tok/s |
| Time to first answer | BenchLeader | 380.6 s | 79.2 s |
| Context window | BenchLeader | 1,000,000 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 90.6% | 93.5% |
| SimpleBench | SimpleBench | – | 64.8%† |
| Arena Hard Prompts | Arena (LMArena) | 1530† | 1511† |
| LiveBench 2.0 Reasoning | LiveBench | 92.2%† | – |
| LiveBench 1.0 Reasoning | LiveBench | 92.2% | 91.7% |
| GPQA Diamond (AA) | Artificial Analysis | – | 94.1% |
| Humanity's Last Exam (AA) | Artificial Analysis | 61.4% | 49.5% |
| HLE-Diamond (AA) | Artificial Analysis | 67.9% | – |
| GPQA Diamond (Vals) | Vals AI | – | 95.2% |
| EnigmaEval | Scale AI SEAL | – | 37.1%† |
| Kagi LLM Benchmark | Kagi LLM Benchmark | – | 67.0%† |
| ARC-AGI-1 | ARC Prize | 97.5% | 96.5% |
| ARC-AGI-2 | ARC Prize | 91.7% | 92.5% |
| ARC-AGI-3 | ARC Prize | – | 7.8% |
| CritPt | Artificial Analysis | 31.7% | 32.3% |
| CritPt v2 hard subset (AA) | Artificial Analysis | 68.0% | 60.0% |
| MysteryMechanism | Vals AI | 49.5%† | 33.3% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 67.0% | 59.0% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 71.0% | 58.0% |
| LMCA | Epoch AI Benchmarking Hub | 68.2% | 58.4% |
| DTBench | Epoch AI Benchmarking Hub | 98.9% | 95.5% |
| ForecastBench | ForecastBench | – | 65.5% |
| BTF-3 | Epoch AI Benchmarking Hub | – | 13.7% |
| Coding | |||
| SciCode | SciCode | 66.9% | 57.1% |
| WeirdML | WeirdML | – | 87.0% |
Show all 172 benchmarks
| Metric | Publisher | Claude Opus 5.5 (max) | GPT-5.6 Sol (max) |
|---|---|---|---|
| Coding | |||
| FrontierCode | Cognition | 54.6%† | 47.5%† |
| GSO-Bench | GSO-Bench | – | 76.5%† |
| Arena Coding | Arena (LMArena) | 1546† | 1533† |
| Arena WebDev | Arena (LMArena) | 1818 | 1615† |
| LiveBench 2.0 Coding | LiveBench | 87.1%† | – |
| LiveBench 1.0 Coding | LiveBench | 89.3% | 83.9% |
| LiveCodeBench | Vals AI | – | 82.6% |
| IOI | Vals AI | 95.1%† | 91.2% |
| SWE-bench (Vals) | Vals AI | – | 96.2% |
| Code Migration | Vals AI | 66.7%† | 52.9% |
| Code Migration v1.1 | Vals AI | 81.0%† | – |
| ProgramBench | Vals AI | 18.5%† | 1.5% |
| SWE-bench Multimodal | Vals AI | 70.2%† | – |
| Vibe Code Bench 1-100 | Vals AI | 30.4%† | 20.0% |
| Vibe Code Bench v1.1 | Vals AI | 90.3%† | 80.5% |
| SWE Atlas: Codebase QnA | Scale AI SEAL | – | 46.0%† |
| SWE Atlas: Test Writing | Scale AI SEAL | – | 45.9%† |
| MirrorCode | Epoch AI Benchmarking Hub | 78.9%† | 20.0%† |
| Surface Evolver Bench | Epoch AI Benchmarking Hub | – | 93.1%† |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | – | 72.7% |
| CursorBench | Cursor | 57.8% | 41.7% |
| ALE-Bench | Epoch AI Benchmarking Hub | 2147.4† | 2176.9 |
| FrontierSWE | Epoch AI Benchmarking Hub | 62.3% | 32.2% |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 59.6% | 39.9% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | – | 88.0% |
| Startrise Frontend | Startrise | 71.1%† | 66.2%† |
| GameBench 2 Overall | DuelLab GameBench 2 | 87.6† | 66.7† |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | 94.8† | 79.6† |
| Design Arena Website | Design Arena | 1325† | 1317† |
| FrontierCode 1.1 (AA) | Artificial Analysis | 52.2% | – |
| FrontierCode 2.0 (AA) | Artificial Analysis | 46.4% | – |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 65.8% | 38.5% |
| OSWorld-Verified 2.0 | OSWorld | – | 27.3% |
| Remote Labor Index | Scale AI / CAIS | 21.3%† | – |
| APEX-Agents | Mercor | 73.5% | 51.4%† |
| Arena Agent | Arena (LMArena) | 20 | 4.5† |
| LiveBench Long Agentic | LiveBench | 72.8%† | – |
| LiveBench Browser | LiveBench | 88.4%† | – |
| LiveBench Agentic Coding | LiveBench | 71.7% | 56.2% |
| Terminal-Bench Hard | Artificial Analysis | – | 65.9% |
| τ²-Bench Telecom (AA) | Artificial Analysis | – | 85.1% |
| Terminal-Bench 2.1 (Vals) | Vals AI | 87.6%† | 85.8% |
| MCP Atlas | Scale AI SEAL | 85.4%† | – |
| HiL-Bench | Scale AI SEAL | 59.0%† | 32.3%† |
| τ³-bench Banking | τ²-bench | – | 46.9%† |
| GDPval-AA v2.1, normalised score | Artificial Analysis | 68.3% | – |
| GDPval-AA v2.1 | Artificial Analysis | 1866 | – |
| τ³-Banking (AA) | Artificial Analysis | – | 44.3% |
| ITBench SRE (AA) | Artificial Analysis | 38.2% | 56.2% |
| Analyst Agent (AA) | Artificial Analysis | 56.3% | 47.5% |
| CUA-bench | Vals AI | 14.0% | 8.3% |
| CyberBench | Vals AI | 55.4%† | 76.3% |
| Finance Agent v2 | Vals AI | 58.6%† | 53.8% |
| Finance Agent v2.1 | Vals AI | 67.8%† | – |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 9.2%† | – |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 3.8%† | 2.5% |
| Legal Research Bench | Vals AI | 50.5%† | 48.1% |
| Robo-bench | Vals AI | 14.6% | – |
| SkillsBench | Vals AI | – | 54.1% |
| SREBench | Vals AI | 33.6%† | 30.5% |
| Tax Agent Bench | Vals AI | 44.5%† | 31.1% |
| Terminal-Bench 4.0 (Vals) | Vals AI | 65.2%† | 37.9% |
| Terminal-Bench Science | Vals AI | 47.1%† | 20.0% |
| Time Horizon Index: KSP | Vals AI | 91.3%† | 23.8% |
| Web Search Index | Vals AI | – | 43.6% |
| Arena Search | Arena (LMArena) | 1214† | 1256† |
| BALROG | BALROG | – | 60.0% |
| PostTrainBench | Epoch AI Benchmarking Hub | – | 36.2% |
| GBAEval | Epoch AI Benchmarking Hub | – | 52.6%† |
| Vending-Bench 2 | Andon Labs | 9235.3† | 9619.4† |
| AutomationBench | Artificial Analysis | 69.5% | 60.1% |
| GDP.pdf (AA) | Artificial Analysis | 26.2% | 27.2% |
| Harvey LAB-AA v1.1 | Harvey | 4.2% | – |
| EnterpriseOps-Gym | Artificial Analysis | – | 42.9% |
| AA-Briefcase v1.1 | Artificial Analysis | 1800 | 1479 |
| GDPx | GDPx by SemiAnalysis | 12.0% | – |
| Design Arena Agentic | Design Arena | 1334† | 1236† |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 59.0% | 22.4% |
| CWE-Bench (AA) | Artificial Analysis | 58.3% | – |
| DeepSec-Bench (AA) | Artificial Analysis | 26.9% | – |
| CyberGym end-to-end (AA) | Artificial Analysis | 0.8% | – |
| FACTS Search | 88.4%† | – | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 91.2% | 89.1% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 95.0% | 82.9% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 100.0% | 100.0% |
| ProofBench | Vals AI | 100.0% | 83.0% |
| LiveBench Mathematics | LiveBench | 97.1% | 96.2% |
| Arena Maths | Arena (LMArena) | 1518† | 1494† |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 72.2% | 69.7% |
| LiveBench 2.0 Data Analysis | LiveBench | 80.3%† | – |
| LiveBench Research | LiveBench | 88.9%† | – |
| LiveBench 1.0 Data Analysis | LiveBench | 80.3% | 79.8% |
| AA-Omniscience v1.1 | Artificial Analysis | 48.1 | – |
| MMLU-Pro | Vals AI | – | 89.1% |
| LegalBench | Vals AI | – | 87.0% |
| CorpFin | Vals AI | – | 64.4% |
| TaxEval | Vals AI | – | 74.8% |
| PRBench Finance | Scale AI SEAL | – | 50.5% |
| PRBench Legal | Scale AI SEAL | 47.6%† | 50.5% |
| BioMysteryBench | Vals AI | 79.3%† | 71.1% |
| ClaimBench | Vals AI | 100.0%† | – |
| Excel Modeling Benchmark | Vals AI | 75.9%† | 72.3% |
| Excel Modeling Benchmark v1.1 | Vals AI | 75.8%† | – |
| MedCode | Vals AI | 49.8%† | 44.0% |
| MedScribe | Vals AI | 91.4%† | 85.2% |
| Public Benefits Bench v1.1 | Vals AI | 70.6%† | 66.5% |
| DrugDiscoveryBench | Scale AI SEAL | – | 56.5% |
| EBR-bench | Epoch AI Benchmarking Hub | 71.4% | 44.8% |
| GDP.pdf | Epoch AI Benchmarking Hub | 30.6% | 30.7% |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | 65.5% | – |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | 49.7% | – |
| HealthBench Professional (AA) | OpenAI | 59.9% | – |
| Instruction following | |||
| LiveBench Language | LiveBench | 86.3% | 87.7% |
| LiveBench Instruction Following | LiveBench | 65.7% | 71.8% |
| IFBench | Artificial Analysis | – | 72.7% |
| Arena Instruction Following | Arena (LMArena) | 1519† | 1490† |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1506† | 1485† |
| EQ-Bench 4 | EQ-Bench | 1340† | 1236† |
| Arena Creative Writing | Arena (LMArena) | 1517† | 1471† |
| Arena Multi-turn | Arena (LMArena) | 1500† | 1491† |
| Arena Longer Queries | Arena (LMArena) | 1520† | 1498† |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1324† | 1282† |
| LiveBench Design | LiveBench | 83.3%† | – |
| MMMU-Pro | Artificial Analysis | 87.7% | 83.4% |
| MMMU-Pro (Vals) | Vals AI | – | 88.8% |
| MortgageTax | Vals AI | – | 67.3% |
| SAGE | Vals AI | 91.3%† | – |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | 51.2%† | 33.6%† |
| Code2Video-AA | Artificial Analysis | 1536 | – |
| AA-3D | Artificial Analysis | 1202 | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 84.7% | 84.0% |
| Arena Document | Arena (LMArena) | – | 1486† |
| MLCR | Artificial Analysis | 27.8% | 30.0% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 168.4† | 163.0† |
| LiveBench 2.0 | LiveBench | 83.4%† | – |
| LiveBench 1.0 | LiveBench | 83.2% | 81.0% |
| AA Intelligence Index v5.0 | Artificial Analysis | 59.1 | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 57.6 | 47.0 |
| AA Progress Index v1 | Artificial Analysis | 350 | 331.5 |
| Vals Index | Vals AI | 70.1† | – |
| Vals Finance Index | Vals AI | 65.3%† | – |
| Vals RSI Index | Vals AI | 37.3% | 23.9% |
| Vals Science Index | Vals AI | 58.6%† | 41.5% |
| Vals Multimodal Index | Vals AI | – | 72.6% |
| Vals Router Index | Vals AI | 64.2%† | – |
| AA Cyber Index | Artificial Analysis | 28.7 | – |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | 5.8% | 20.1%† |
Data as of 2026-11-09. Best configuration of each model; every score links to its source on the model pages.
DuelLab GameBench 2, release gb2-20261103-eaea31df94e6 (https://benchmarks.duellab.org/releases/gb2-20261103-eaea31df94e6/), CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Scores mapped to BenchLeader model names; not endorsed by DuelLab.
What changed
- Claude Opus 5.5 (max): 2 score revisions.
- GPT-5.6 Sol (max): 1 score revision.
- Claude Opus 5.5 (max): 1 score revision.
- Claude Opus 5.5 (max): 1 score revision.
- Claude Opus 5.5 (max): 2 score revisions.
- GPT-5.6 Sol (max): 1 score revision.
- Claude Opus 5.5 (max): 2 score revisions.
- Claude Opus 5.5 (max): 1 score revision.
- Claude Opus 5.5 (max): 5 new results (AA Intelligence Index v5.0, AA Progress Index v1, HLE-Diamond (AA)…); 3 score revisions; cost per task moved from $5.98/M to $10.04/M (68% up).
- GPT-5.6 Sol (max): 2 new results (AA Progress Index v1, CritPt v2 hard subset (AA)).
Related comparisons
- Claude Opus 5.5 vs Claude Fable 5.2
- Claude Opus 5.5 vs Gemini 4 Carbon
- Claude Opus 5.5 vs GPT-6 Astra
- GPT-5.6 Sol vs Claude Fable 5.2
- GPT-5.6 Sol vs Gemini 4 Carbon
- GPT-5.6 Sol vs GPT-6 Astra
- Claude Fable 5.1 vs GPT-5.6 Sol
- Claude Opus 5.5 vs GPT-5.5
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
Claude Opus 5.5 vs GPT-5.6 Sol: questions
- Is Claude Opus 5.5 better than GPT-5.6 Sol?
- Claude Opus 5.5 (max) and GPT-5.6 Sol (max) are neck and neck on quality (68.9 vs 65.4, within the ±5.7 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 9 Nov 2026, so check the category scores for your use.
- Claude Opus 5.5 vs GPT-5.6 Sol: which is cheaper, and will that change?
- They cost the same: $8.00 per million tokens blended. No price change has been announced for either.
- Is Claude Opus 5.5 or GPT-5.6 Sol better for coding?
- Claude Opus 5.5 scores higher in coding (80 vs 66 on the category index, where 50 is average). On SciCode, the top shared coding benchmark, it is 66.9% for Claude Opus 5.5 against 57.1% for GPT-5.6 Sol.
- Which is faster, Claude Opus 5.5 or GPT-5.6 Sol?
- Claude Opus 5.5 streams faster (115 vs 99 output tokens per second, Artificial Analysis 7-day median).
- Is Claude Opus 5.5 or GPT-5.6 Sol better for agents and tool use?
- GPT-5.6 Sol scores higher in agentic tasks (67 vs 63 on the category index, where 50 is average). On Terminal-Bench, the top shared agents & tools benchmark, it is 65.8% for Claude Opus 5.5 against 38.5% for GPT-5.6 Sol.
More questions (4)
- Which is better for long documents?
- They are level on long-context benchmarks (67 vs 67). Both take about 1.1M tokens: Claude Opus 5.5 1,000,000, GPT-5.6 Sol 1,050,000.
- Which has lower latency?
- GPT-5.6 Sol answers first: 79.2 s against 380.6 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 Opus 5.5 vs GPT-5.6 Sol?
- Across the 42 benchmarks both have, Claude Opus 5.5 leads 32, GPT-5.6 Sol leads 9 and 1 is a tie. Every overall index that rates both puts Claude Opus 5.5 ahead.
- When was Claude Opus 5.5 released?
- Claude Opus 5.5 was released on 22 Sep 2026.