[whatmodel.sh](../../README.md) / Claude Sonnet 5.5
# Claude Sonnet 5.5
Anthropic · released 2026-09 · proprietary
**score 81.7** ±0.6 · #8 of 732 · from 26 benchmark results across 6 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Claude Sonnet 5.5 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄▄▂● #3 of 313 38% 8
coding ▁▁▁▃▅▇█▇▅▄▃● #4 of 469 21% 5
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #26 of 402* 4.0% 1
math ▁▁▃▅█▆▄█▄▂●▁ #9 of 431 19% 4
vision & documents ▁▁▂▂▁▃▄▂▆▇●▄ #37 of 153* 2.7% 1
web search ▁▁▁▅▄█●▄·▁·▁ #19 of 84* 2.4% 1
agent tasks · #3 of 313 · 38% of its score
Beats [GPT-6 Astra](gpt-6-astra.md), [GPT-6.1 Terra](gpt-6-1-terra.md), [GPT-6.1 Sol](gpt-6-1-sol.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Terminal-Bench 4.0 61.8% █▅▅▅▅·▅▃▃▅▅● beats 19/20 very hard · avg 0.7% 11% sharp · at its level · several runs
Arena Agent (October 2026 aggrega… 0.1384 IPS ▁▁▁▃▃▂█▁▂●▁▁ beats 57/59 avg -0.379 IPS 10% sharp
SWE-bench Pro (split unspecified)… 81.3% ▄█·▄·▄█●···▄ beats 8/9 very hard · avg 7.4% 7.0% sharp
SWE-bench Multilingual (maker) 90.3% ▃··▃·▅▃▃●··▃ beats 8/9 hard · avg 31% 3.3% typical
Humanity's Last Exam with tools (… 64.5% ▁······▁·▄·● beats 7/10 very hard · avg 14% 2.4% at its level
DeepSWE v1.1 (resolved) (maker) 71.0% █·····███●·█ beats 4/5 hard · avg 19% 1.9% at its level
Chartography with tools (maker) 90.2% ▃········▃█● beats 7/7 very hard · avg 6.5% 1.9% sharp
Toolathlon-Verified (maker) 77.8% ▃·····●···▃▃ beats 1/5 medium · avg 37% 1.0% blunt
coding · #4 of 469 · 21% of its score
Beats [GPT-6.1 Terra](gpt-6-1-terra.md), [GPT-6.1 Sol](gpt-6-1-sol.md), [Claude Fable 5.1](claude-fable-5-1.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1769.7 ▁▁▃▂▅█▃▆▅▅▁● beats 127/129 avg 981 7.2% sharp
SWE-bench Pro (split unspecified)… 81.3% ▄█·▄·▄█●···▄ beats 8/9 very hard · avg 7.4% 7.0% sharp
SWE-bench Multilingual (maker) 90.3% ▃··▃·▅▃▃●··▃ beats 8/9 hard · avg 31% 3.3% typical
DeepSWE v1.1 (resolved) (maker) 71.0% █·····███●·█ beats 4/5 hard · avg 19% 1.9% at its level
Luau Leetcode fully solved (Torpe… 80.8% ▁▁▁▁·▁▂▃▅▃●▇ beats 35/49 hard · avg 17% 1.7% sharp
chat & writing · #26 of 402 · 4.0% of its score
Beats [GPT-6 Astra](gpt-6-astra.md) here, though they rank higher overall. Trails [Gemini 3.6 Flash](gemini-3-6-flash.md), [Gemini 3.5 Flash](gemini-3-5-flash.md), [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
LMArena (text) 1477.1 ▁▁▁▂▃▃▃▆▆▇●▃ beats 358/385 avg 1260 4.0% blunt
math · #9 of 431 · 19% of its score
Trails [Claude Fable 5](claude-fable-5.md), [GPT-5.6 Sol](gpt-5-6-sol.md), [GPT-6 Sol](gpt-6-sol.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
FrontierMath tier 4 v2 80.5% ▁▂▁▂█▅▂●▂▁▁▁ beats 59/67 very hard · avg 0.1% 11% sharp
FrontierMath tiers 1–3 v2 88.8% ▂··▁▁▃▄▄█▆▄● beats 79/86 very hard · avg 1.2% 7.0% sharp
FrontierMath: Erdős problems 2.9% █··········● beats 4/6 very hard · avg 0.0% 0.6% sharp · too hard for it · noisy
OTIS mock AIME 2024–2025 100.0% ▃▃▄▃▃▃▇▇█▃▂● beats 190/197 very hard · avg 7.2% 0.1% sharp · maxed out · noisy
vision & documents · #37 of 153 · 2.7% of its score
Trails [Qwen3.6 Flash](qwen3-6-flash.md), [Qwen3.6 Plus](qwen3-6-plus.md), [Qwen3.7 Plus](qwen3-7-plus.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1264.9 ▁▁▂▂▂▂▃▄▅▅●▇ beats 120/152 avg 1061 2.7% blunt
web search · #19 of 84 · 2.4% 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 (… 64.5% ▁······▁·▄·● beats 7/10 very hard · avg 14% 2.4% at its level
other tests (9)
test result vs the others in its run difficulty counts why
Mystery Game Puzzles (Epoch) 65.0% █··▂▃▇▄█▂▄●▁ beats 73/77 very hard · avg 9.4% 8.5% sharp · at its level · noisy
Furniture Assembly (Epoch) 75.0% █▆▆▆▆▂▆▂▂●▂▅ beats 28/32 very hard · avg 1.2% 6.9% sharp · at its level
Humanity's Last Exam (maker) 56.9% ▂·····▂·▄▂●▄ beats 7/10 very hard · avg 5.9% 4.6% at its level
Global-MMLU (maker) 92.1% ▄▄····●▄··▄█ beats 2/6 medium · avg 64% 1.6% typical
SimpleQA Verified 46.5% ▁▁▃▂▃▃▆▆●▄▂▅ beats 46/81 very hard · avg 8.0% 1.4% at its level
Icelandic LLM Leaderboard average… 62.1% ▁▁▃▂▂▄▁▃▄▄●█ beats 26/38 very hard · avg 14% 1.4% at its level
GPQA Diamond 95.6% ▁▁▁▁▂▄▆▄▅▇█● beats 210/211 hard · avg 39% 1.1% sharp · maxed out
HealthBench (maker) 69.4% █·▄·····▄▄▄● beats 6/6 hard · avg 30% 1.0% blunt · at its level
RobloxQA v2.0 multiple choice (To… 96.6% ▁▁▃▁█▄▃▃●▂▂▂ beats 37/49 easy · avg 77% 0.6% maxed out
source results share of the score
Epoch AI 8 37% ▇▇▇▇
Anthropic system cards and model announ… 10 30% ▇▇▇▇
LMArena leaderboard dataset 4 24% ▇▇▇
Terminal-Bench 4.0 official leaderboard… 1 5% ▇
Roblox LLM Leaderboard 2 2% ▇
Miðeind Icelandic LLM Leaderboard 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 81.7
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)) · 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) · $4.00/M blended · $2 in / $10 out per 1M tokens · [platform.claude.com](https://platform.claude.com/docs/en/models/sonnet-5-5/overview)
[price source](https://platform.claude.com/docs/en/models/sonnet-5-5/overview): Observed 2026-10-06. USD per million tokens, standard uncached input/output; batch and cache discounts excluded. · [context source](https://platform.claude.com/docs/en/models/sonnet-5-5/overview): Official 1M-token context window, 128K maximum output; observed 2026-10-06.
[interactive version](https://whatmodel.sh/model/claude-sonnet-5-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)