[whatmodel.sh](../../README.md) / Claude Haiku 5.5
# Claude Haiku 5.5
Anthropic · released 2026-10 · proprietary
**score 75.3** ±0.6 · #32 of 732 · from 17 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Claude Haiku 5.5 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄●▂▁ #22 of 313 33% 6
coding ▁▁▁▃▅▇█▇▅▄●▁ #34 of 469 21% 3
chat & writing ▁▁▁▃▆▄▆█▆●▇▃ #87 of 402* 3.8% 1
math ▁▁▃▅█▆▄█▄●▁▁ #22 of 431 32% 3
web search ▁▁▁▅▄●▃▄·▁·▁ #32 of 84* 2.5% 1
agent tasks · #22 of 313 · 33% of its score
Beats [GPT-5.5](gpt-5-5.md), [Hy4 preview (770B-A49B)](hy4-preview.md), [GPT-5.6 Terra](gpt-5-6-terra.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Arena Agent (October 2026 aggrega… 0.0143 IPS ▁▁▁▃▃▂●▁▂▂▁▁ beats 34/59 avg -0.379 IPS 10% sharp
SWE-bench Pro (split unspecified)… 64.8% ▄●·▄·▄██···▄ beats 2/9 very hard · avg 7.4% 10% sharp · at its level
SWE-bench Multilingual (maker) 83.7% ▃··▃·●▃▃█··▃ beats 2/9 hard · avg 31% 4.6% typical
Terminal-Bench 4.0 (maker) 39.2% ▄·····▄··●██ beats 2/7 very hard · avg 0.7% 4.3% sharp · at its level
Chartography with tools (maker) 86.2% ▃········▃●█ beats 4/7 very hard · avg 6.5% 2.8% sharp · at its level
Humanity's Last Exam with tools (… 57.4% ▁······▁·●·█ beats 3/10 very hard · avg 14% 2.5% at its level
coding · #34 of 469 · 21% of its score
Beats [GPT-5.5](gpt-5-5.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [Claude Opus 4.8](claude-opus-4-8.md) here, though they rank higher overall. Trails [GLM-5.2](glm-5-2.md), [GLM-5.3-Flash](glm-5-3-flash.md), [Grok 4.7](grok-4-7.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Pro (split unspecified)… 64.8% ▄●·▄·▄██···▄ beats 2/9 very hard · avg 7.4% 10% sharp · at its level
Arena WebDev (Code Arena) 1588.8 ▁▁▃▂▅█▃▆●▅▁▂ beats 97/129 avg 981 6.7% sharp
SWE-bench Multilingual (maker) 83.7% ▃··▃·●▃▃█··▃ beats 2/9 hard · avg 31% 4.6% typical
chat & writing · #87 of 402 · 3.8% of its score
Trails [Gemma 4 12B](gemma-4-12b.md), [Gemma 4 26B-A4B](gemma-4-26b-a4b.md), [Mistral Medium 3.5 (128B)](mistral-medium-3-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1424.8 ▁▁▁▂▃▃▃▆▆●█▃ beats 278/385 avg 1260 3.8% blunt
math · #22 of 431 · 32% of its score
Beats [DeepSeek V4.1 Flash 1017](deepseek-v4-1-flash-1017.md), [Muse Spark 1.3](muse-spark-1-3.md), [Gemini 3.8 Flash](gemini-3-8-flash.md) here, though they rank higher overall. Trails [GPT-5.6 Luna](gpt-5-6-luna.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
FrontierMath tier 4 v2 46.3% ▁▂▁▂█●▂▃▂▁▁▁ beats 46/67 very hard · avg 0.1% 17% sharp · at its level
FrontierMath tiers 1–3 v2 75.1% ▂··▁▁▃▄▄█●▄▃ beats 67/86 very hard · avg 1.2% 15% sharp · at its level
OTIS mock AIME 2024–2025 98.9% ▃▃▄▃▃▃▇▇█▃▂● beats 178/197 very hard · avg 7.2% 0.4% sharp · maxed out · noisy
web search · #32 of 84 · 2.5% 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 (… 57.4% ▁······▁·●·█ beats 3/10 very hard · avg 14% 2.5% at its level
other tests (6)
test result vs the others in its run difficulty counts why
Furniture Assembly (Epoch) 47.5% █▆▆▆▆●▆▂▂▂▂▅ beats 21/32 very hard · avg 1.2% 6.6% sharp · at its level
Mystery Game Puzzles (Epoch) 30.0% █··▂▃▇▄●▂▄▂▁ beats 48/77 very hard · avg 9.4% 6.6% sharp · at its level · noisy
Humanity's Last Exam (maker) 45.9% ▂·····▂·●▂█▄ beats 3/10 very hard · avg 5.9% 4.4% at its level
GPQA Diamond 89.6% ▁▁▁▁▂▄▆▄▅▇●▅ beats 170/211 hard · avg 39% 2.3% sharp
Global-MMLU (maker) 87.8% ●▄····▄▄··▄█ beats 0/6 medium · avg 64% 2.0% typical
SimpleQA Verified 23.8% ▁▁▃▂●▃▆▆█▄▂▅ beats 15/81 very hard · avg 8.0% 1.5% at its level
source results share of the score
Epoch AI 7 49% ▇▇▇▇▇▇
Anthropic system cards and model announ… 7 30% ▇▇▇▇
LMArena leaderboard dataset 3 20% ▇▇
prior: pull toward the middle 0% ▇
weighted average = 75.3
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/))
## How to run it
### Through an API
api (Anthropic) · $0.20/M blended · $0.1 in / $0.5 out per 1M tokens · [platform.claude.com](https://platform.claude.com/docs/en/models/haiku-5-5/overview)
[price source](https://platform.claude.com/docs/en/models/haiku-5-5/overview): Observed 2026-10-09 on Anthropic's own Claude Haiku 5.5 model page (Pricing table; same rows on platform.claude.com/docs/en/about-claude/pricing): USD per million tokens, standard uncached input/output 'for prompts up to 100,000 tokens'. Prompts over 100,000 tokens cost $0.50 input / $2.50 output; batch (50% off) and cache rates excluded. · [context source](https://platform.claude.com/docs/en/models/haiku-5-5/overview): Anthropic model page: 'Context window: 1M tokens · Max output: 128K tokens' (300K output on the Batch API with a beta header). Observed 2026-10-09.
[interactive version](https://whatmodel.sh/model/claude-haiku-5-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)