[whatmodel.sh](../../README.md) / Claude Opus 4.6
# Claude Opus 4.6
Anthropic · released 2026-02 · closed weights
**score 73.3** ±0.5 · #48 of 732 · from 54 benchmark results across 28 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Claude Opus 4.6 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅●▄▂▁ #53 of 313 19% 14
coding ▁▁▁▃▅▇█▇▅●▃▁ #42 of 469 12% 5
chat & writing ▁▁▁▃▆▄▆█▆▇▇● #7 of 402* 2.9% 2
math ▁▁▃▅█▆▄█●▂▁▁ #42 of 431 33% 10
vision & documents ▁▁▂▂▁▃▄▂▆▇●▄ #18 of 153 5.2% 5
web search ▁▁▁▅▄●▃▄·▁·▁ #28 of 84* 1.7% 2
agent tasks · #53 of 313 · 19% of its score
Beats [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md) here, though they rank higher overall. Trails [Claude Sonnet 4.6](claude-sonnet-4-6.md), [Qwen3.8 27B](qwen3-8-27b.md), [GLM-5.2](glm-5-2.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 78.7% ▁··▁·▁▂▁▄█●▁ beats 27/29 hard · avg 19% 4.1% sharp
Agents' Last Exam overall full-pa… 14.1% ▄···█▄▄▄●▄·█ beats 6/10 very hard · avg 1.9% 2.0% typical
τ³-bench banking_knowledge (v1.0.… 27.3% ▃▃▂▃·▆●▂█▅▅▂ beats 11/25 very hard · avg 4.3% 1.7% at its level
ContractBench success rate (33 ob… 77.8% ▆··▆▄▂▄▂▆▆█● beats 23/23 very hard · avg 13% 1.6% sharp · at its level
E2E-SWE (build a working codebase… 22.6% ▃··●··▃█▃▅▃▅ beats 2/12 very hard · avg 3.7% 1.5% sharp · at its level
Toolathlon (Tool Decathlon) (make… 56.8% █···█··●·███ beats 2/5 hard · avg 16% 1.4% at its level
Vibe Code Bench (test split, 50 w… 53.5% ▃··▃·▃██▃▃▃● beats 12/14 very hard · avg 2.8% 1.2% sharp · at its level · noisy
Terminal-Bench 2.0 (maker) 65.4% ▂▂▅▅████●▂▃▃ beats 30/36 very hard · avg 14% 1.1% at its level · small run
SWE Atlas (Codebase Q&A, Test Wri… 31.8% ▄·▄··█···●·█ beats 4/6 very hard · avg 6.6% 1.1% at its level
Thinkingbox-bench pass@1 (507 sta… 32.1% ▅··▃·▃·●▃▅██ beats 4/15 very hard · avg 5.1% 1.1% sharp · at its level
RWSearch 81.3% ████··●····█ beats 4/5 medium · avg 45% 0.8% typical
EEGAgentBench overall (tool-based… 46.3% ▂·▁▁··▇●█▂·▁ beats 13/25 hard · avg 28% 0.8% blunt · at its level
τ²-bench Telecom (maker) 99.3% ▁·▅█▆▅▂▃▃▂▃● beats 87/89 hard · avg 30% 0.6% sharp · maxed out · noisy
T1-Bench multi-domain conversatio… 34.5% ▂▂··▂·▂·▂▂●▄ beats 8/11 hard · avg 25% 0.0% blunt · at its level · noisy
coding · #42 of 469 · 12% of its score
Beats [GPT-5.5](gpt-5-5.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [GPT-5.4](gpt-5-4.md) here, though they rank higher overall. Trails [Qwen3.8 27B](qwen3-8-27b.md), [GLM-5.2](glm-5-2.md), [GLM-5.3-Flash](glm-5-3-flash.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1546.2 ▁▁▃▂▅█▃●▅▅▁▂ beats 87/129 avg 981 4.5% sharp
SWE-bench Verified 78.7% ▁··▁·▁▂▁▄█●▁ beats 27/29 hard · avg 19% 4.1% sharp
E2E-SWE (build a working codebase… 22.6% ▃··●··▃█▃▅▃▅ beats 2/12 very hard · avg 3.7% 1.5% sharp · at its level
Vibe Code Bench (test split, 50 w… 53.5% ▃··▃·▃██▃▃▃● beats 12/14 very hard · avg 2.8% 1.2% sharp · at its level · noisy
SWE Atlas (Codebase Q&A, Test Wri… 31.8% ▄·▄··█···●·█ beats 4/6 very hard · avg 6.6% 1.1% at its level
chat & writing · #7 of 402 · 2.9% of its score
Beats [GPT-6 Astra](gpt-6-astra.md), [GPT-6.1 Sol](gpt-6-1-sol.md), [Claude Sonnet 5.5](claude-sonnet-5-5.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1503.2 ▁▁▁▂▃▃▃▆▆▇█● beats 379/385 avg 1260 2.5% blunt
IFEval (aggregation unspecified) 92.2% █·██·●···█·█ beats 3/5 easy · avg 72% 0.3% typical
math · #42 of 431 · 33% of its score
Trails [GPT-5.2](gpt-5-2.md), [Qwen3.7 Max](qwen3-7-max.md), [Muse Spark 1.1](muse-spark-1-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
FrontierMath tiers 1–3 v2 66.0% ▂··▁▁▃▄▄●▆▄▃ beats 53/86 very hard · avg 1.2% 12% sharp · at its level
FrontierMath tier 4 v2 26.8% ▁▂▁▂●▅▂▃▂▁▁▁ beats 30/67 very hard · avg 0.1% 10% sharp · at its level
MathArena ArXivMath February 2026… 40.6% ▁▁▁▁█●▄▃▁▃·▃ beats 13/24 very hard · avg 9.4% 3.0% at its level
MathArena ArXivMath March 2026 (3… 62.5% █▃▅·█▃●·▅·▃▃ beats 10/14 hard · avg 16% 2.3% at its level
MathArena ArXivMath January 2026 … 72.8% ▂··▄▂▄█▆▆▄▄● beats 20/23 hard · avg 23% 1.9% at its level
MathArena ArXivMath December 2025… 57.4% ▃·▃▃█▁·▄·●▁▁ beats 16/18 hard · avg 19% 1.5% at its level
MathArena BrokenArXiv March 2026 … 5.8% ●·▆█▂·▂▂···▂ beats 1/14 very hard · avg 1.0% 1.0% sharp · noisy
MathArena BrokenArXiv February 20… 3.2% ●▂▂▃█▃▂·▃··▂ beats 1/16 very hard · avg 0.9% 1.0% sharp · noisy
MathNet-Solve-Test micro-average … 45.7% ▃▃···█●·▃·▃▃ beats 5/8 very hard · avg 15% 0.8% at its level
OTIS mock AIME 2024–2025 91.1% ▃▃▄▃▃▃▇▇●▃▂▅ beats 146/197 very hard · avg 7.2% 0.4% sharp · maxed out · noisy
vision & documents · #18 of 153 · 5.2% of its score
Beats [Claude Sonnet 5.5](claude-sonnet-5-5.md), [GPT-6 Sol](gpt-6-sol.md), [GPT-5.5](gpt-5-5.md) here, though they rank higher overall. Trails [Muse Spark 1.2](muse-spark-1-2.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1299.1 ▁▁▂▂▂▂▃▄▅▅█● beats 150/152 avg 1061 1.7% blunt
CharXiv (reasoning questions) (ma… 69.1% █●█··█·██··█ beats 1/6 hard · avg 28% 1.6% typical
Arena document (style controlled) 1495.3 ▁▁▃▃▄▃▇█▅▇▄● beats 36/37 avg 1331 0.9% blunt
BEAR-Bench (bilingual RU/EN enter… 61.5% ▃▃·▅·▃·█·●▅▅ beats 9/14 hard · avg 24% 0.8% at its level
Diagram-MMU diagram question answ… 68.8% ●·····▄▄·█·█ beats 0/6 medium · avg 56% 0.3% blunt
web search · #28 of 84 · 1.7% of its score
Beats [Claude Opus 4.8](claude-opus-4-8.md), [Claude Haiku 5.5](claude-haiku-5-5.md), [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md) here, though they rank higher overall. Trails [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena search systems (style contr… 1222.0 ▁·▁▁▂▂▂█▆●▂▂ beats 29/36 avg 1069 0.9% blunt
RWSearch 81.3% ████··●····█ beats 4/5 medium · avg 45% 0.8% typical
other tests (20)
test result vs the others in its run difficulty counts why
EBR-bench (Epoch) 12.7% ▄▂·▆●▂█▆▄▆·▆ beats 7/25 very hard · avg 0.3% 4.1% sharp
Furniture Assembly (Epoch) 28.3% █●▆▆▆▂▆▂▂▂▂▅ beats 8/32 very hard · avg 1.2% 4.0% sharp · at its level
GPQA Diamond 88.4% ▁▁▁▁▂▄▆▄▅▇●▅ beats 163/211 hard · avg 39% 3.7% sharp · several runs
Mystery Game Puzzles (Epoch) 25.0% █··▂▃▇●█▂▄▂▁ beats 42/77 very hard · avg 9.4% 3.5% sharp · noisy
ARC-AGI-2 public evaluation set (… 79.0% █··▂▅▁▃▁▃·▁● beats 24/24 very hard · avg 0.3% 2.8% sharp · at its level · noisy
Chess Puzzles (Epoch) 14.0% █▁▁▁▁●▃▃▃▂▁▁ beats 66/137 very hard · avg 5.8% 2.2% sharp · at its level · noisy
KINA (Knowledge Index of Noah's A… 49.9% ▁▁▂▁▃▄█▆▄▅▂● beats 38/39 very hard · avg 21% 2.1% at its level
DSEffi-Bench Pass (1,000 data-sci… 61.3% ▅··▃▅·█·▃█●▃ beats 13/15 hard · avg 23% 1.7% at its level
EQ-Bench 3 (emotional intelligenc… 1383.4 ▁·▂▁▄▅▅▃█▄●▁ beats 60/62 avg 1034 1.5% typical
WildClawBench 51.6% ▃·▅▅█·▅█▅●▃▃ beats 15/17 very hard · avg 11% 1.3% at its level
AgentWorldBench (open-ended, over… 57.8% ▄▄··▄▄·▄█▄█● beats 10/11 hard · avg 26% 1.3% blunt · at its level
ARC-AGI-1 public evaluation set (… 96.8% ▁·▇▃▁·█▇▁·▇● beats 26/27 very hard · avg 4.0% 1.1% sharp · noisy
SimpleQA Verified 47.0% ▁▁▃▂▃▃▆▆●▄▂▅ beats 48/81 very hard · avg 8.0% 1.0% at its level
FORTE (AGI-Eval) 73.2% ▄···●·····▄█ beats 1/5 medium · avg 38% 0.9% at its level
MMMLU (maker) 91.1% ▂▁▁▂▃▂▄▄▅██● beats 45/46 medium · avg 61% 0.8% small run
HLE-Verified full set, text-only … 46.8% █·█·····█··● beats 3/3 very hard · avg 15% 0.7% at its level
SCHEDBench subset, solver-mediate… 56.7% ▄·▄·····▄··● beats 4/4 very hard · avg 5.4% 0.6% at its level · noisy
FinSheet-Bench accuracy (501 ques… 80.2% █···●█·····█ beats 1/3 medium · avg 53% 0.5% typical
DiagnosticIQ industrial maintenan… 73.6% ▁··▃▁▁▁▅█▅▁● beats 24/24 hard · avg 51% 0.5% blunt · at its level
Vectara hallucination leaderboard… 87.8% ▁▂▁▂●█▂▃▄▃▁▁ beats 21/98 very easy · avg 90% 0.0% blunt
source results share of the score
Epoch AI 10 42% ▇▇▇▇▇
MathArena ArXivMath / BrokenArXiv / ArX… 6 11% ▇
LMArena leaderboard dataset 5 10% ▇
Anthropic system cards and model announ… 6 7% ▇
ARC Prize Foundation model_baseline out… 2 4% ▇
meituan-longcat/LongCat-2.0 model card 3 2% ▇
Knowledge Index of Noah's Ark 1 2% ▇
Agents' Last Exam 1 2% ▇
DSEffi-Bench: Demystifying Large Langua… 1 2% ▇
τ²/τ³-bench leaderboard: Sierra's own e… 1 2% ▇
ContractBench: Can LLM Agents Preserve … 1 2% ▇
EQ-Bench 3 canonical leaderboard results 1 2% ▇
E2E-SWE: Benchmarking LLMs on Building … 1 1% ▇
WildClawBench: A Benchmark for Real-Wor… 1 1% ▇
Qwen/Qwen-AgentWorld-35B-A3B model card 1 1% ▇
Vibe Code Bench 1 1% ▇
SWE Atlas: Benchmarking Coding Agents B… 1 1% ▇
One Success Isn't Reliability: Thinking… 1 1% ▇
MathNet: a Global Multimodal Benchmark … 1 1% ▇
EEGAgentBench: Benchmarking LLM Agents … 1 1% ▇
BEAR-Bench: A Bilingual Enterprise and … 1 1% ▇
HLE-Verified: A Systematic Verification… 1 1% ▇
Improving Natural-Language Combinatoria… 1 1% ▇
FinSheet-Bench: From Simple Lookups to … 1 1% ▇
DiagnosticIQ: A Benchmark for LLM-Based… 1 1% ▇
Diagram-MMU: A Multi-Modal Benchmark fo… 1 0% ▇
T1-Bench: Benchmarking Multi-Scenario A… 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 73.3
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · MathArena ArXivMath / BrokenArXiv / ArXivLean model-output datasets ([CC BY-SA 4.0](../../licenses/matharena-sharealike-NOTICE.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · ARC Prize Foundation model\_baseline outputs on ARC-AGI-1/2 public evaluation sets ([MIT](../../licenses/arcprize-model-baseline-outputs-MIT.txt)) · meituan-longcat/LongCat-2.0 model card ([mit](../../licenses/meituan-longcat--LongCat-2.0.txt)) · Knowledge Index of Noah's Ark ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Agents' Last Exam ([CC BY 4.0](../../licenses/paper-2606.05405v2-CC-BY-4.0.txt)) · DSEffi-Bench: Demystifying Large Language Models' Capability in Efficient Data Science Code Generation ([CC BY 4.0](../../licenses/paper-2608.30248v1-CC-BY-4.0.txt)) · τ²/τ³-bench leaderboard: Sierra's own evaluation runs ([MIT](https://opensource.org/license/mit)) · ContractBench: Can LLM Agents Preserve Observation Contracts? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · WildClawBench: A Benchmark for Real-World ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Qwen/Qwen-AgentWorld-35B-A3B model card ([Apache-2.0](https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/blob/60d2b0434a53d2e62a7c00a489586815d94ebffb/README.md)) · Vibe Code Bench ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · SWE Atlas: Benchmarking Coding Agents Beyond Issue Resolution ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · One Success Isn't Reliability: Thinkingbox ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · MathNet: a Global Multimodal Benchmark for Mathematical Reasoning and Retrieval ([CC BY 4.0](../../licenses/paper-2604.18584v2-CC-BY-4.0.txt)) · EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · BEAR-Bench: A Bilingual Enterprise and Academic Reasoning Benchmark for Multimodal Models ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam ([CC BY 4.0](../../licenses/paper-2602.13964v4-CC-BY-4.0.txt)) · Improving Natural-Language Combinatorial-Optimization Accuracy in Resource-Constrained Language Models via Formal Abstractions ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · FinSheet-Bench: From Simple Lookups to Complex Reasoning ([CC BY 4.0](../../licenses/paper-2603.07316v1-CC-BY-4.0.txt)) · DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### Through an API
api (Anthropic) · $10.00/M blended · $5 in / $25 out per 1M tokens · [docs.anthropic.com](https://docs.anthropic.com/en/docs/about-claude/models)
[price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/anthropic/models/claude-opus-4-6.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/anthropic/claude-opus-4-6.toml)
[interactive version](https://whatmodel.sh/model/claude-opus-4-6) · [JSON](https://whatmodel.sh/api?format=json&all=1)