[whatmodel.sh](../../README.md) / Claude Haiku 4.5
# Claude Haiku 4.5
Anthropic · released 2025-10 · closed weights
**score 63.6** ±0.7 · #159 of 732 · from 28 benchmark results across 15 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 4.5 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄●▅▄▄▂▁ #128 of 313 31% 10
coding ▁▁▁▃▅▇█▇●▄▃▁ #129 of 469 28% 4
chat & writing ▁▁▁▃▆▄▆█▆●▇▃ #97 of 402* 5.9% 1
math ▁▁▃▅█▆▄●▄▂▁▁ #106 of 431* 10% 2
vision & documents ▁▁▂▂▁▃▄▂▆●█▄ #68 of 153* 2.0% 1
web search ●▁▁▅▄█▃▄·▁·▁ #83 of 84* 3.4% 1
agent tasks · #128 of 313 · 31% of its score
Beats [o3](o3.md), [GPT-5 mini](gpt-5-mini.md), [Gemini 2.5 Pro](gemini-2-5-pro.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Large](jamba-1-5-large.md), [Devstral 2 (2512)](devstral-2.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Multilingual (maker) 67.4% ●··▃·▅▃▃█··▃ beats 0/9 hard · avg 31% 11% at its level
Chartography with tools (maker) 8.8% ●········▃██ beats 0/7 very hard · avg 6.5% 4.5% sharp · at its level
Terminal-Bench 2.0 with Terminus … 28.3% ▃··▃·▃▃█●▅▃▅ beats 7/14 very hard · avg 6.1% 4.0% sharp
Humanity's Last Exam with tools (… 18.7% ●······▁·▄·█ beats 0/10 very hard · avg 14% 3.4% at its level
BFCL V4 (December 2025 archive) 68.7% ▁▁▁▂██▅▅▆▂●▂ beats 73/77 hard · avg 31% 2.8% at its level
Terminal-Bench 4.0 (maker) 0.0% ●·····▄··███ beats 0/7 very hard · avg 0.7% 2.0% sharp · too hard for it
Vibe Code Bench (test split, 50 w… 11.4% ▃··▃·●██▃▃▃█ beats 2/14 very hard · avg 2.8% 1.5% sharp · noisy
τ²-Bench-Verified (GPT-5.1 user) 53.4% ▂··●···▂▄▄█▄ beats 1/13 medium · avg 45% 1.1% at its level
KlinikeBench strict pass@1 (333 i… 2.7% ▁▁·▁●▁▁▅▃█▅█ beats 3/27 very hard · avg 2.4% 0.8% too hard for it
T1-Bench multi-domain conversatio… 34.3% ▂▂··▂·▂·▂▂●▄ beats 7/11 hard · avg 25% 0.0% blunt · at its level · noisy
coding · #129 of 469 · 28% of its score
Beats [Qwen3.5 Plus](qwen3-5-plus.md), [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Solar Pro 4](solar-pro-4.md), [Gemma 4 26B-A4B](gemma-4-26b-a4b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Multilingual (maker) 67.4% ●··▃·▅▃▃█··▃ beats 0/9 hard · avg 31% 11% at its level
Arena WebDev (Code Arena) 1330.4 ▁▁▃▂●█▃▆▅▅▁▂ beats 23/129 avg 981 10% sharp
Luau Leetcode fully solved (Torpe… 59.6% ▁▁▁▁·▁▂●▅▃█▇ beats 15/49 hard · avg 17% 5.4% sharp · at its level
Vibe Code Bench (test split, 50 w… 11.4% ▃··▃·●██▃▃▃█ beats 2/14 very hard · avg 2.8% 1.5% sharp · noisy
chat & writing · #97 of 402 · 5.9% of its score
Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [Inkling Small (266B-A12B)](inkling-small.md), [GPT-5.4 nano](gpt-5-4-nano.md) here, though they rank higher overall. Trails [Gemma 4 12B](gemma-4-12b.md), [Kimi K2 Instruct (0905)](kimi-k2-0905.md), [Gemma 4 26B-A4B](gemma-4-26b-a4b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1414.5 ▁▁▁▂▃▃▃▆▆●█▃ beats 264/385 avg 1260 5.9% blunt
math · #106 of 431 · 10% of its score
Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [MiniMax-M3](minimax-m3.md), [Nemotron 3 Ultra 550B-A55B](nemotron-3-ultra-550b-a55b.md) here, though they rank higher overall. Trails [Gemini 2.5 Flash](gemini-2-5-flash.md), [GLM-4.5-Air](glm-4-5-air.md), [Granite 4.2 30B](granite-4-2-30b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 96.4% ▂▂▅▆▄█▆▃▅▂●▄ beats 84/92 hard · avg 28% 5.8% sharp
OTIS mock AIME 2024–2025 66.7% ▃▃▄▃▃▃●▇█▃▂▅ beats 95/197 very hard · avg 7.2% 4.4% sharp · at its level · noisy
vision & documents · #68 of 153 · 2.0% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-5](gpt-5.md), [o3](o3.md) here, though they rank higher overall. Trails [Gemma 4 26B-A4B](gemma-4-26b-a4b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena document (style controlled) 1418.7 ▁▁●▃▄▃▇█▅▇▄▅ beats 2/37 avg 1331 2.0% blunt
web search · #83 of 84 · 3.4% of its score
Trails [GPT-4.1 nano](gpt-4-1-nano-2025-04-14.md), [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Humanity's Last Exam with tools (… 18.7% ●······▁·▄·█ beats 0/10 very hard · avg 14% 3.4% at its level
other tests (12)
test result vs the others in its run difficulty counts why
GPQA Diamond 71.2% ▁▁▁▁▂▄▆▄●▇█▅ beats 107/211 hard · avg 39% 9.4% sharp · at its level
Humanity's Last Exam (maker) 10.2% ●·····▂·▄▂█▄ beats 0/10 very hard · avg 5.9% 4.6% typical
ARC-AGI-1 public evaluation set (… 62.9% ▁·▇▃▁·●▇▁·▇▃ beats 14/27 very hard · avg 4.0% 4.4% sharp · at its level · noisy
AraGen-v3 3C3H score (Inception, … 65.8% ▁▁·▁▂▂▄█▄▅●▄ beats 52/66 hard · avg 29% 3.3% at its level
The Token Games Elo (10-model puz… 1000.0 ●··▄█··▄·▄·▄ beats 0/7 avg 822 3.1% typical
RobloxQA v2.0 multiple choice (To… 89.5% ▁▁▃▁●▄▃▃▆▂▂▂ beats 12/49 easy · avg 77% 2.6% typical
Icelandic LLM Leaderboard average… 15.7% ▁▁●▂▂▄▁▃▄▄▄█ beats 4/38 very hard · avg 14% 1.9% at its level
ARC-AGI-2 public evaluation set (… 5.1% █··▂●▁▃▁▃·▁▃ beats 10/24 very hard · avg 0.3% 1.7% sharp · too hard for it · noisy
Chess Puzzles (Epoch) 8.0% █▁▁●▁▂▃▃▃▂▁▁ beats 53/137 very hard · avg 5.8% 1.7% sharp · too hard for it · noisy
SimpleQA Verified 12.6% ▁▁●▂▃▃▆▆█▄▂▅ beats 6/81 very hard · avg 8.0% 1.7% typical
Vectara hallucination leaderboard… 95.5% ▁▁▂▃▄▄●▆▃▂▂▁ beats 77/140 very easy · avg 94% 0.2% blunt · maxed out · noisy
Vectara hallucination leaderboard… 90.2% ▁▂▁▂▅●▂▃▄▃▁▁ beats 45/98 very easy · avg 90% 0.0% blunt
source results share of the score
Anthropic system cards and model announ… 5 25% ▇▇▇
Epoch AI 5 23% ▇▇▇
LMArena leaderboard dataset 3 18% ▇▇
Roblox LLM Leaderboard 2 8% ▇
ARC Prize Foundation model_baseline out… 2 6% ▇
Terminal-Bench: Benchmarking Agents on … 1 4% ▇
Inception Arabic/Hindi leaderboards 1 3% ▇
The Token Games: Evaluating Language Mo… 1 3% ▇
Berkeley Function Calling Leaderboard r… 1 3% ▇
Miðeind Icelandic LLM Leaderboard 1 2% ▇
Vibe Code Bench 1 1% ▇
Amazon AGI 1 1% ▇
KlinikeBench: Evaluating Language Model… 1 1% ▇
Vectara hallucination leaderboard 2 0% ▇
T1-Bench: Benchmarking Multi-Scenario A… 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 63.6
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/)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · ARC Prize Foundation model\_baseline outputs on ARC-AGI-1/2 public evaluation sets ([MIT](../../licenses/arcprize-model-baseline-outputs-MIT.txt)) · Terminal-Bench: Benchmarking Agents on Hard ([CC BY 4.0](../../licenses/paper-2601.11868v1-CC-BY-4.0.txt)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · The Token Games: Evaluating Language Model Reasoning with Puzzle Duels ([CC BY-SA 4.0](../../licenses/paper-2602.17831v2-CC-BY-SA-4.0.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · Miðeind Icelandic LLM Leaderboard ([CC BY 4.0](../../licenses/mideind-icelandic-llm-leaderboard-CC-BY-4.0.txt)) · Vibe Code Bench ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Amazon AGI ([MIT](../../licenses/tau2-verified-MIT.txt)) · KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy ([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)) · T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### Through an API
api (Anthropic) · $2.00/M blended · $1 in / $5 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-haiku-4-5.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-haiku-4-5.toml)
[interactive version](https://whatmodel.sh/model/claude-haiku-4-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)