[whatmodel.sh](../../README.md) / Claude Opus 5.5
# Claude Opus 5.5
Anthropic · released 2026-09 · closed weights
**score 84.7** ±0.6 · #2 of 732 · from 31 benchmark results across 7 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 5.5 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄▄▂● #1 of 313 38% 10
coding ▁▁▁▃▅▇█▇▅▄▃● #1 of 469 18% 5
chat & writing ▁▁▁▃▆▄▆█▆▇▇● #3 of 402* 4.1% 1
math ▁▁▃▅█▆▄█▄▂▁● #3 of 431 11% 4
vision & documents ▁▁▂▂▁▃▄▂▆▇█● #9 of 153* 2.7% 1
web search ▁▁▁▅▄█▃●·▁·▁ #13 of 84* 2.3% 1
agent tasks · #1 of 313 · 38% of its score
Beats [GPT-6 Astra](gpt-6-astra.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Arena Agent (October 2026 aggrega… 0.2094 IPS ▁▁▁▃▃▂█▁▂▂▁● beats 59/59 avg -0.379 IPS 10% sharp
Terminal-Bench 4.0 64.8% █▅▅▅▅·▅▃▃▅▅● beats 20/20 very hard · avg 0.5% 10% sharp · at its level · several runs
SWE-bench Pro (split unspecified)… 89.9% ▄█·▄·▄██···● beats 9/9 very hard · avg 7.4% 5.5% sharp
SWE-bench Multilingual (maker) 93.9% ▃··▃·▅▃▃█··● beats 9/9 hard · avg 31% 2.7% maxed out
Humanity's Last Exam with tools (… 67.7% ▁······▁·▄·● beats 10/10 very hard · avg 14% 2.3% at its level
KlinikeBench strict pass@1 (333 i… 28.8% ▁▁·▁▁▁▁▅▃█▅● beats 26/27 very hard · avg 2.4% 1.8% at its level
DeepSWE v1.1 (resolved) (maker) 74.2% █·····████·● beats 5/5 hard · avg 19% 1.8% at its level
Chartography with tools (maker) 89.0% ▃········▃█● beats 6/7 very hard · avg 6.5% 1.4% sharp
OSWorld 2.1 (partial score) (make… 81.8% █·········█● beats 2/2 hard · avg 29% 1.0% small run
Toolathlon-Verified (maker) 77.8% ▃·····●···▃▃ beats 3/5 medium · avg 37% 0.9% blunt
coding · #1 of 469 · 18% of its score
Beats [GPT-6 Astra](gpt-6-astra.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1809.8 ▁▁▃▂▅█▃▆▅▅▁● beats 129/129 avg 981 7.3% sharp
SWE-bench Pro (split unspecified)… 89.9% ▄█·▄·▄██···● beats 9/9 very hard · avg 7.4% 5.5% sharp
SWE-bench Multilingual (maker) 93.9% ▃··▃·▅▃▃█··● beats 9/9 hard · avg 31% 2.7% maxed out
DeepSWE v1.1 (resolved) (maker) 74.2% █·····████·● beats 5/5 hard · avg 19% 1.8% at its level
Luau Leetcode fully solved (Torpe… 87.0% ▁▁▁▁·▁▂▃▅▃█● beats 49/49 hard · avg 17% 1.3% sharp
chat & writing · #3 of 402 · 4.1% of its score
Beats [GPT-6 Astra](gpt-6-astra.md) here, though they rank higher overall. Trails [Gemini 4 Argon](gemini-4-argon.md), [Gemini 4 Carbon](gemini-4-carbon.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1508.7 ▁▁▁▂▃▃▃▆▆▇█● beats 383/385 avg 1260 4.1% blunt
math · #3 of 431 · 11% of its score
Trails [GPT-6.1 Sol](gpt-6-1-sol.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
FrontierMath tier 4 v2 95.0% ▁▂▁▂█▅▂▃▂●▁▁ beats 65/67 very hard · avg 0.1% 6.2% sharp
FrontierMath tiers 1–3 v2 91.2% ▂··▁▁▃▄▄█▆▄● beats 84/86 very hard · avg 1.2% 4.1% sharp · maxed out
FrontierMath: Erdős problems 2.9% █··········● beats 5/6 very hard · avg 0.0% 0.8% sharp · too hard for it · noisy
OTIS mock AIME 2024–2025 100.0% ▃▃▄▃▃▃▇▇█▃▂● beats 188/197 very hard · avg 7.2% 0.1% sharp · maxed out · noisy
vision & documents · #9 of 153 · 2.7% of its score
Beats [GPT-6 Astra](gpt-6-astra.md) here, though they rank higher overall. Trails [Claude Opus 4.7](claude-opus-4-7.md), [Gemini 3.7 Flash](gemini-3-7-flash.md), [Gemini 3.8 Flash](gemini-3-8-flash.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1293.0 ▁▁▂▂▂▂▃▄▅▅█● beats 145/152 avg 1061 2.7% blunt
web search · #13 of 84 · 2.3% of its score
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
Humanity's Last Exam with tools (… 67.7% ▁······▁·▄·● beats 10/10 very hard · avg 14% 2.3% at its level
other tests (12)
test result vs the others in its run difficulty counts why
EBR-bench (Epoch) 71.4% ▄▂·▆█▂█▆▄▆·● beats 23/25 very hard · avg 0.3% 10% sharp · at its level
Mystery Game Puzzles (Epoch) 71.0% █··▂▃▇▄█▂▄●▁ beats 74/77 very hard · avg 9.4% 7.1% sharp · at its level · noisy
Furniture Assembly (Epoch) 83.3% █▆▆▆▆▂▆▂▂▂▂● beats 32/32 very hard · avg 1.2% 5.9% sharp · at its level
Humanity's Last Exam (maker) 64.4% ▂·····▂·▄▂█● beats 10/10 very hard · avg 5.9% 4.4% at its level
Global-MMLU (maker) 94.3% ▄▄····▄▄··▄● beats 6/6 medium · avg 64% 1.4% maxed out
MirrorCode (Epoch) 77.4% ▄·█▄▄▄██·▄▄● beats 13/13 very hard · avg 1.6% 1.3% sharp · at its level · noisy
SimpleQA Verified 72.2% ▁▁▃▂▃▃▆▆█▄▂● beats 78/81 very hard · avg 8.0% 1.3% at its level
Icelandic LLM Leaderboard average… 70.9% ▁▁▃▂▂▄▁▃▄▄▄● beats 38/38 very hard · avg 14% 1.3% at its level
HealthBench (maker) 68.1% █·▄·····▄▄●▄ beats 5/6 hard · avg 30% 0.9% blunt · at its level
HealthBench Professional (maker) 65.6% ▂·▂··▂▂▂█▄▂● beats 13/13 hard · avg 20% 0.8% blunt · at its level · small run
GPQA Diamond 90.6% ▁▁▁▁▂▄▆▄▅▇●▅ beats 177/211 hard · avg 39% 0.7% sharp · maxed out
RobloxQA v2.0 multiple choice (To… 98.6% ▁▁▃▁█▄▃▃▆▂▂● beats 49/49 easy · avg 77% 0.5% maxed out
source results share of the score
Epoch AI 10 37% ▇▇▇▇
Anthropic system cards and model announ… 12 28% ▇▇▇
LMArena leaderboard dataset 4 25% ▇▇▇
Terminal-Bench 4.0 official leaderboard… 1 5% ▇
KlinikeBench: Evaluating Language Model… 1 2% ▇
Roblox LLM Leaderboard 2 2% ▇
Miðeind Icelandic LLM Leaderboard 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 84.7
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Terminal-Bench 4.0 official leaderboard submissions ([Apache-2.0](../../licenses/terminal-bench-leaderboard-submissions-Apache-2.0.txt)) · KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · Miðeind Icelandic LLM Leaderboard ([CC BY 4.0](../../licenses/mideind-icelandic-llm-leaderboard-CC-BY-4.0.txt))
## How to run it
### Through an API
api (Anthropic) · $8.00/M blended · $4 in / $20 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-5-5.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-5-5.toml)
[interactive version](https://whatmodel.sh/model/claude-opus-5-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)