Claude Opus 5.5 vs GPT-5 Pro
Full profiles: Claude Opus 5.5 (Anthropic) and GPT-5 Pro (OpenAI): every benchmark, price and rank.
Updated · Data from 14 sources
As of 9 Nov 2026, Claude Opus 5.5 is ahead of GPT-5 Pro on quality (68.9 vs 60.0 on the BenchLeader Index), and Claude Opus 5.5 is 5.2× cheaper at $8.00 vs $41.25 per 1M tokens blended. Claude Opus 5.5 leads in every category we measure both in.
Key facts (as of 9 Nov 2026)
- Claude Opus 5.5 scores 68.9 on the BenchLeader Index and GPT-5 Pro scores 60.0 (BenchLeader, 9 Nov 2026).
- Claude Opus 5.5 costs $8.00 per 1M tokens blended (list price, 9 Nov 2026).
- GPT-5 Pro costs $41.25 per 1M tokens blended (list price, 9 Nov 2026).
- Of the 3 benchmarks both models have, Claude Opus 5.5 leads 2 (BenchLeader, 9 Nov 2026).
Claude Opus 5.5 benchmarks 
GPT-5 Pro 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 |
|---|---|---|---|
| Reasoning | Claude Opus 5.5 | 72 vs 56 category score; ARC-AGI-2 91.7% vs 18.3% | High |
| Knowledge | Claude Opus 5.5 | 74 vs 66 category score | Medium |
| Images and other media | Claude Opus 5.5 | 70 vs 67 category score | Medium |
| Lowest cost | Claude Opus 5.5 | $8.00 vs $41.25 per 1M blended; agentic coding session $0.320 vs $1.38 | – |
| 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) leads on quality: 68.9 vs 60.0.
- Claude Opus 5.5 (max) is stronger in agents & tools, coding, knowledge, long context, maths, multimodal, reasoning.
- Claude Opus 5.5 (max) is 5.2× cheaper ($8.00 vs $41.25 per 1M blended).
At a glance
| Spec | Claude Opus 5.5 | GPT-5 Pro |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Released | 22 Sep 2026 | 7 Aug 2025 |
| Context window (tokens) | 1,000,000 | 400,000 |
| 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 Pro |
| Open weights | No | No |
| Price in / out / cached input per 1M | $4.00 / $20.00 / $0.200 | $15.00 / $120.00 / – |
| 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 Pro | Leader |
|---|---|---|---|
| Reasoning | 72.3 | 56.1 | Claude Opus 5.5 |
| Coding | 80.4 | – | not comparable |
| Agents & tools | 62.7 | – | not comparable |
| Maths | 74.0 | – | not comparable |
| Knowledge | 73.7 | 66.3 | Claude Opus 5.5 |
| Multimodal | 70.3 | 67.4 | Claude Opus 5.5 |
| Long context | 67.1 | – | 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: Coding, Agents & tools, Maths, Instruction following, Long context, Human preference.
Where sources disagree
Across the 3 benchmarks both have, Claude Opus 5.5 leads 2, GPT-5 Pro leads 1. Every overall index that rates both puts Claude Opus 5.5 ahead.
| Overall index | Claude Opus 5.5 (max) | GPT-5 Pro | Leader |
|---|---|---|---|
| BenchLeader Index | 68.9 | 60.0 | 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 Pro |
|---|---|---|
| Blended price per 1M tokens | $8.00 | $41.25 |
| Input / output per 1M | $4.00 / $20.00 | $15.00 / $120.00 |
| Cached input per 1M | $0.200 | – |
| Price per task: chat reply | $0.0076 | $0.042 |
| Price per task: summarize a 30-page report | $0.060 | $0.252 |
| Price per task: code edit | $0.054 | $0.270 |
| Price per task: agentic coding session | $0.320 | $1.38 |
| Price per task: structured extraction | $0.012 | $0.054 |
| Agentic coding session, input mostly cached | $0.149 | – |
| 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 | – |
| Time to first answer | 380.6 s | – |
| Time to a complete answer | 771.1 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 Pro scores 60.0. 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
143 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 Pro |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 68.9 | 60.0 |
| Agents & tools score | BenchLeader | 62.7 | – |
| Coding score | BenchLeader | 80.4 | – |
| Knowledge score | BenchLeader | 73.7 | 66.3 |
| Long context score | BenchLeader | 67.1 | – |
| Maths score | BenchLeader | 74.0 | – |
| Multimodal score | BenchLeader | 70.3 | 67.4 |
| Reasoning score | BenchLeader | 72.3 | 56.1 |
| Blended price $/M | BenchLeader | $8.00 | $41.25 |
| Output speed | BenchLeader | 115 tok/s | – |
| Time to first answer | BenchLeader | 380.6 s | – |
| Context window | BenchLeader | 1,000,000 tokens | 400,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 90.6% | – |
| Humanity's Last Exam | Scale AI / CAIS | – | 31.6% |
| SimpleBench | SimpleBench | – | 61.6% |
| Arena Hard Prompts | Arena (LMArena) | 1530† | – |
| LiveBench 2.0 Reasoning | LiveBench | 92.2%† | – |
| LiveBench 1.0 Reasoning | LiveBench | 92.2% | – |
| Humanity's Last Exam (AA) | Artificial Analysis | 61.4% | – |
| HLE-Diamond (AA) | Artificial Analysis | 67.9% | – |
| Kagi LLM Benchmark | Kagi LLM Benchmark | – | 76.8% |
| ARC-AGI-1 | ARC Prize | 97.5% | 70.2% |
| ARC-AGI-2 | ARC Prize | 91.7% | 18.3% |
| CritPt | Artificial Analysis | 31.7% | – |
| CritPt v2 hard subset (AA) | Artificial Analysis | 68.0% | – |
| MysteryMechanism | Vals AI | 49.5%† | – |
| Chess Puzzles | Epoch AI Benchmarking Hub | 67.0% | 48.0%† |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 71.0% | – |
| LMCA | Epoch AI Benchmarking Hub | 68.2% | – |
| DTBench | Epoch AI Benchmarking Hub | 98.9% | – |
| Coding | |||
| SciCode | SciCode | 66.9% | – |
| WeirdML | WeirdML | – | 60.4%† |
| FrontierCode | Cognition | 54.6%† | – |
| Arena Coding | Arena (LMArena) | 1546† | – |
| Arena WebDev | Arena (LMArena) | 1818 | – |
| LiveBench 2.0 Coding | LiveBench | 87.1%† | – |
| LiveBench 1.0 Coding | LiveBench | 89.3% | – |
Show all 143 benchmarks
| Metric | Publisher | Claude Opus 5.5 (max) | GPT-5 Pro |
|---|---|---|---|
| Coding | |||
| IOI | Vals AI | 95.1%† | – |
| Code Migration | Vals AI | 66.7%† | – |
| Code Migration v1.1 | Vals AI | 81.0%† | – |
| ProgramBench | Vals AI | 18.5%† | – |
| SWE-bench Multimodal | Vals AI | 70.2%† | – |
| Vibe Code Bench 1-100 | Vals AI | 30.4%† | – |
| Vibe Code Bench v1.1 | Vals AI | 90.3%† | – |
| MirrorCode | Epoch AI Benchmarking Hub | 78.9%† | – |
| CursorBench | Cursor | 57.8% | – |
| ALE-Bench | Epoch AI Benchmarking Hub | 2147.4† | – |
| AlgoTune | AlgoTune | – | 1.3† |
| FrontierSWE | Epoch AI Benchmarking Hub | 62.3% | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 59.6% | – |
| Startrise Frontend | Startrise | 71.1%† | – |
| GameBench 2 Overall | DuelLab GameBench 2 | 87.6† | – |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | 94.8† | – |
| Design Arena Website | Design Arena | 1325† | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | 52.2% | – |
| FrontierCode 2.0 (AA) | Artificial Analysis | 46.4% | – |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 65.8% | – |
| Remote Labor Index | Scale AI / CAIS | 21.3%† | – |
| APEX-Agents | Mercor | 73.5% | – |
| Arena Agent | Arena (LMArena) | 20 | – |
| LiveBench Long Agentic | LiveBench | 72.8%† | – |
| LiveBench Browser | LiveBench | 88.4%† | – |
| LiveBench Agentic Coding | LiveBench | 71.7% | – |
| Terminal-Bench 2.1 (Vals) | Vals AI | 87.6%† | – |
| MCP Atlas | Scale AI SEAL | 85.4%† | – |
| HiL-Bench | Scale AI SEAL | 59.0%† | – |
| GDPval-AA v2.1, normalised score | Artificial Analysis | 68.3% | – |
| GDPval-AA v2.1 | Artificial Analysis | 1866 | – |
| ITBench SRE (AA) | Artificial Analysis | 38.2% | – |
| Analyst Agent (AA) | Artificial Analysis | 56.3% | – |
| CUA-bench | Vals AI | 14.0% | – |
| CyberBench | Vals AI | 55.4%† | – |
| Finance Agent v2 | Vals AI | 58.6%† | – |
| 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%† | – |
| Legal Research Bench | Vals AI | 50.5%† | – |
| Robo-bench | Vals AI | 14.6% | – |
| SREBench | Vals AI | 33.6%† | – |
| Tax Agent Bench | Vals AI | 44.5%† | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | 65.2%† | – |
| Terminal-Bench Science | Vals AI | 47.1%† | – |
| Time Horizon Index: KSP | Vals AI | 91.3%† | – |
| Arena Search | Arena (LMArena) | 1214† | – |
| Vending-Bench 2 | Andon Labs | 9235.3† | – |
| AutomationBench | Artificial Analysis | 69.5% | – |
| GDP.pdf (AA) | Artificial Analysis | 26.2% | – |
| Harvey LAB-AA v1.1 | Harvey | 4.2% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1800 | – |
| GDPx | GDPx by SemiAnalysis | 12.0% | – |
| Design Arena Agentic | Design Arena | 1334† | – |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 59.0% | – |
| CWE-Bench (AA) | Artificial Analysis | 58.3% | – |
| DeepSec-Bench (AA) | Artificial Analysis | 26.9% | – |
| CyberGym end-to-end (AA) | Artificial Analysis | 0.8% | – |
| DeepSearchQA | – | 65.2% | |
| FACTS Search | 88.4%† | – | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 91.2% | 55.8%† |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 95.0% | 19.5%† |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 100.0% | – |
| ProofBench | Vals AI | 100.0% | – |
| LiveBench Mathematics | LiveBench | 97.1% | – |
| Arena Maths | Arena (LMArena) | 1518† | – |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 72.2% | – |
| LiveBench 2.0 Data Analysis | LiveBench | 80.3%† | – |
| LiveBench Research | LiveBench | 88.9%† | – |
| LiveBench 1.0 Data Analysis | LiveBench | 80.3% | – |
| AA-Omniscience v1.1 | Artificial Analysis | 48.1 | – |
| PRBench Finance | Scale AI SEAL | – | 51.1% |
| PRBench Legal | Scale AI SEAL | 47.6%† | 49.9% |
| MultiNRC | Scale AI SEAL | – | 65.2% |
| BioMysteryBench | Vals AI | 79.3%† | – |
| ClaimBench | Vals AI | 100.0%† | – |
| Excel Modeling Benchmark | Vals AI | 75.9%† | – |
| Excel Modeling Benchmark v1.1 | Vals AI | 75.8%† | – |
| MedCode | Vals AI | 49.8%† | – |
| MedScribe | Vals AI | 91.4%† | – |
| Public Benefits Bench v1.1 | Vals AI | 70.6%† | – |
| EBR-bench | Epoch AI Benchmarking Hub | 71.4% | – |
| GDP.pdf | Epoch AI Benchmarking Hub | 30.6% | – |
| 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% | – |
| LiveBench Instruction Following | LiveBench | 65.7% | – |
| Arena Instruction Following | Arena (LMArena) | 1519† | – |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1506† | – |
| EQ-Bench 4 | EQ-Bench | 1340† | – |
| Arena Creative Writing | Arena (LMArena) | 1517† | – |
| Arena Multi-turn | Arena (LMArena) | 1500† | – |
| Arena Longer Queries | Arena (LMArena) | 1520† | – |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1324† | – |
| LiveBench Design | LiveBench | 83.3%† | – |
| MMMU-Pro | Artificial Analysis | 87.7% | – |
| VISTA | Scale AI SEAL | – | 52.4% |
| SAGE | Vals AI | 91.3%† | – |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | 51.2%† | – |
| Code2Video-AA | Artificial Analysis | 1536 | – |
| AA-3D | Artificial Analysis | 1202 | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 84.7% | – |
| MLCR | Artificial Analysis | 27.8% | – |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 168.4† | 151.2 |
| LiveBench 2.0 | LiveBench | 83.4%† | – |
| LiveBench 1.0 | LiveBench | 83.2% | – |
| AA Intelligence Index v5.0 | Artificial Analysis | 59.1 | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 57.6 | – |
| AA Progress Index v1 | Artificial Analysis | 350 | – |
| Vals Index | Vals AI | 70.1† | – |
| Vals Finance Index | Vals AI | 65.3%† | – |
| Vals RSI Index | Vals AI | 37.3% | – |
| Vals Science Index | Vals AI | 58.6%† | – |
| Vals Router Index | Vals AI | 64.2%† | – |
| AA Cyber Index | Artificial Analysis | 28.7 | – |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | 5.8% | 15.2% |
| MASK | Scale AI SEAL | – | 86.0% |
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.
- Claude Opus 5.5 (max): 1 score revision.
- Claude Opus 5.5 (max): 1 score revision.
- Claude Opus 5.5 (max): 2 score revisions.
- 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).
- Claude Opus 5.5 (max): 1 score revision.
- Claude Opus 5.5 (max): 2 new results (Robo-bench, Vals RSI Index).
- Claude Opus 5.5 (max): 2 score revisions.
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 Pro vs Claude Fable 5.2
- GPT-5 Pro vs Gemini 4 Carbon
- GPT-5 Pro vs GPT-6 Astra
- Claude Fable 5.1 vs GPT-5 Pro
- Claude Opus 5.5 vs o3
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
Claude Opus 5.5 vs GPT-5 Pro: questions
- Is Claude Opus 5.5 better than GPT-5 Pro?
- Claude Opus 5.5 (max) leads on quality: 68.9 vs 60.0. The BenchLeader Index combines every independent quality benchmark; Claude Opus 5.5 (max) is ahead overall as of 9 Nov 2026, but check the category scores for your use.
- Claude Opus 5.5 vs GPT-5 Pro: which is cheaper, and will that change?
- Claude Opus 5.5 is cheaper today: $8.00 against $41.25 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Which is better for long documents?
- Claude Opus 5.5 accepts more context: 1,000,000 against 400,000 tokens for GPT-5 Pro.
- Do independent benchmarks agree on Claude Opus 5.5 vs GPT-5 Pro?
- Across the 3 benchmarks both have, Claude Opus 5.5 leads 2, GPT-5 Pro leads 1. 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.