[whatmodel.sh](../../README.md) / Claude Opus 4.5
# Claude Opus 4.5
Anthropic · released 2025-11 · closed weights
**score 70.8** ±0.6 · #71 of 732 · from 40 benchmark results across 20 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 4.5 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅●▄▂▁ #60 of 313 25% 12
coding ▁▁▁▃▅▇█▇▅●▃▁ #63 of 469 15% 4
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #36 of 402* 3.3% 1
math ▁▁▃▅█▆▄●▄▂▁▁ #96 of 431 27% 5
vision & documents ▁▁▂▂▁▃▄▂▆●█▄ #62 of 153 4.6% 3
web search ▁▁▁▅▄●▃▄·▁·▁ #44 of 84* 1.2% 1
agent tasks · #60 of 313 · 25% of its score
Beats [Muse Spark 1.1](muse-spark-1-1.md), [Muse Spark 1.2](muse-spark-1-2.md), [GPT-5.2](gpt-5-2.md) here, though they rank higher overall. Trails [Beam 1.0 501B A23B](beam-1-0-501b-a23b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 76.7% ▁··▁·▁▂▁▄●▄▁ beats 21/29 hard · avg 19% 6.3% sharp · at its level
MCP-Atlas (public set) 65.2% ▄······▄▄●▄█ beats 3/6 hard · avg 26% 3.9% at its level · several runs
Terminal-Bench 2.0 with Terminus … 57.8% ▃··▃·▃▃██▅▃● beats 14/14 very hard · avg 6.1% 2.9% sharp · at its level
τ³-bench banking_knowledge (v1.0.… 24.7% ▃▃▂▃·●▃▂█▅▅▂ beats 7/25 very hard · avg 4.3% 2.0% at its level
τ²-bench aggregate 91.6% ▄····█▄▄···● beats 5/5 medium · avg 45% 1.7% typical
SWE-bench Pro public set, Qwen-re… 57.1% ▄······▄█·▄● beats 5/5 hard · avg 19% 1.5% at its level
BFCL V4 (December 2025 archive) 77.5% ▁▁▁▂██▅▅▆▂▂● beats 77/77 hard · avg 31% 1.5% at its level
τ³-bench airline (tau2-bench ≥ v1… 84.0% ▃······▃·▃█● beats 6/6 medium · avg 51% 1.3% typical
τ³-bench retail (tau2-bench ≥ v1.… 79.6% ▄▄·█··●·▄··▄ beats 4/6 medium · avg 48% 1.2% typical
τ²-bench Telecom (maker) 98.2% ▁·▅█▆▅▂▃▃▂●▁ beats 84/89 hard · avg 30% 1.0% sharp · noisy
τ³-bench telecom (tau2-bench ≥ v1… 92.3% █·▄█●······▄ beats 5/6 medium · avg 64% 1.0% typical
τ²-Bench-Verified (GPT-5.1 user) 82.0% ▂··▄···▂▄▄█● beats 12/13 medium · avg 45% 0.5% typical
coding · #63 of 469 · 15% of its score
Beats [GPT-5.4](gpt-5-4.md), [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md) here, though they rank higher overall. Trails [MiMo-V2.5-Pro (1T-A42B)](mimo-v2-5-pro.md), [MiniMax-M3](minimax-m3.md), [GLM-5.1](glm-5-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 76.7% ▁··▁·▁▂▁▄●▄▁ beats 21/29 hard · avg 19% 6.3% sharp · at its level
Arena WebDev (Code Arena) 1469.2 ▁▁▃▂▅█●▆▅▅▁▂ beats 66/129 avg 981 5.8% sharp
SWE-bench Pro public set, Qwen-re… 57.1% ▄······▄█·▄● beats 5/5 hard · avg 19% 1.5% at its level
WebApp1K-Duo-React pass@1 (onekq-… 76.3% ▁··▁·▁▁▁▆█▇● beats 41/45 hard · avg 29% 1.3% typical
chat & writing · #36 of 402 · 3.3% of its score
Beats [GPT-6 Sol](gpt-6-sol.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md) here, though they rank higher overall. Trails [Grok 4.20 (0309 reasoning)](grok-4-20.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1469.7 ▁▁▁▂▃▃▃▆▆▇●▃ beats 345/385 avg 1260 3.3% blunt
math · #96 of 431 · 27% of its score
Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md) here, though they rank higher overall. Trails [Gemini 2.5 Flash](gemini-2-5-flash.md), [Granite 4.2 30B](granite-4-2-30b.md), [Qwen3-Next 80B-A3B Instruct](qwen3-next-80b-a3b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
FrontierMath tiers 1–3 v2 34.4% ▂··▁▁▃●▄█▆▄▃ beats 23/86 very hard · avg 1.2% 16% sharp · at its level
FrontierMath tier 4 v2 4.9% ▁▂●▂█▅▂▃▂▁▁▁ beats 8/67 very hard · avg 0.1% 8.6% sharp
MATH-Vision 74.3% ●·····▄▄·▄·▄ beats 0/5 medium · avg 47% 1.1% typical
OTIS mock AIME 2024–2025 86.1% ▃▃▄▃▃▃▇▇●▃▂▅ beats 127/197 very hard · avg 7.2% 0.9% sharp · noisy
FormalProofBench (200 graduate-le… 33.5% ▃▅·▃··▅▃█··● beats 10/10 very hard · avg 3.1% 0.6% noisy
vision & documents · #62 of 153 · 4.6% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-5](gpt-5.md) here, though they rank higher overall. Trails [Gemma 4 26B-A4B](gemma-4-26b-a4b.md), [Qwen3.6 Flash](qwen3-6-flash.md), [Qwen3.5 35B-A3B](qwen3-5-35b-a3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CharXiv (reasoning questions) (ma… 65.7% ●██··█·██··█ beats 0/6 hard · avg 28% 2.3% at its level
MATH-Vision 74.3% ●·····▄▄·▄·▄ beats 0/5 medium · avg 47% 1.1% typical
Arena document (style controlled) 1464.6 ▁▁▃▃▄▃▇●▅▇▄▅ beats 21/37 avg 1331 1.1% blunt
web search · #44 of 84 · 1.2% of its score
Beats [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [Gemini 3.5 Flash](gemini-3-5-flash.md), [Qwen3.7 Max](qwen3-7-max.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), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena search systems (style contr… 1186.7 ▁·▁▁▂▂▂●▆▃▂▂ beats 11/36 avg 1069 1.2% blunt
other tests (16)
test result vs the others in its run difficulty counts why
Furniture Assembly (Epoch) 28.3% █●▆▆▆▂▆▂▂▂▂▅ beats 7/32 very hard · avg 1.2% 4.4% sharp
EBR-bench (Epoch) 14.3% ▄▂·▆●▂█▆▄▆·▆ beats 9/25 very hard · avg 0.3% 3.9% sharp
GPQA Diamond 86.0% ▁▁▁▁▂▄▆▄▅▇●▅ beats 151/211 hard · avg 39% 3.2% sharp
Mystery Game Puzzles (Epoch) 22.0% █··▂▃▇●█▂▄▂▁ beats 38/77 very hard · avg 9.4% 3.2% sharp · noisy
WebCompass (overall) 67.4% ▅·▃▃▃···▃·▃● beats 9/9 hard · avg 18% 3.2% at its level
ARC-AGI-2 public evaluation set (… 40.3% █··▂▅▁▃▁●·▁▃ beats 20/24 very hard · avg 0.3% 3.0% sharp · at its level · noisy
Chess Puzzles (Epoch) 12.0% █▁▁▁▁●▃▃▃▂▁▁ beats 59/137 very hard · avg 5.8% 2.3% sharp · noisy
EQ-Bench 3 (emotional intelligenc… 1308.4 ▁·▂▁▄▅▅▃●▄▁▁ beats 50/62 avg 1034 2.0% typical
ARC-AGI-1 public evaluation set (… 86.6% ▁·▇▃▁·█▇●·▇▃ beats 20/27 very hard · avg 4.0% 1.9% sharp · noisy
NL2Repo 43.2% ▄·····█·▄▄·● beats 5/5 very hard · avg 8.9% 1.5% at its level
AraGen-v3 3C3H score (Inception, … 80.3% ▁▁·▁▂▂▄█▄▅▄● beats 62/66 hard · avg 29% 1.3% typical
SimpleQA Verified 45.7% ▁▁▃▂▃▃▆●█▄▂▅ beats 43/81 very hard · avg 8.0% 1.2% at its level
HLE-Verified full set, text-only … 38.8% █·●·····█··█ beats 1/3 very hard · avg 15% 1.0% at its level
SOOHAK Challenge (340 problems, A… 10.4% █·██●██··█·█ beats 3/7 very hard · avg 4.3% 0.6% typical
SOOHAK-Mini (702 problems, Avg@3) 51.4% ▄··●·▄·▄▄█·▄ beats 1/7 medium · avg 36% 0.4% blunt · at its level
Vectara hallucination leaderboard… 89.1% ▁▂▁▂▅●▂▃▄▃▁▁ beats 33/98 very easy · avg 90% 0.0% blunt
source results share of the score
Epoch AI 10 50% ▇▇▇▇▇▇
LMArena leaderboard dataset 4 11% ▇
τ²/τ³-bench leaderboard: Sierra's own e… 4 5% ▇
ARC Prize Foundation model_baseline out… 2 5% ▇
Anthropic system cards and model announ… 2 3% ▇
WebCompass: Towards Multimodal Web Codi… 1 3% ▇
Qwen/Qwen3.6-27B model card 2 3% ▇
Terminal-Bench: Benchmarking Agents on … 1 3% ▇
Qwen/Qwen3.5-397B-A17B model card 2 3% ▇
EQ-Bench 3 canonical leaderboard results 1 2% ▇
zai-org/GLM-5 model card 1 2% ▇
GLM-5 Team 1 2% ▇
Berkeley Function Calling Leaderboard r… 1 1% ▇
Inception Arabic/Hindi leaderboards 1 1% ▇
WebApp1K models leaderboard 1 1% ▇
Soohak: A Mathematician-Curated Benchma… 2 1% ▇
HLE-Verified: A Systematic Verification… 1 1% ▇
FormalProofBench 1 1% ▇
Amazon AGI 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 70.8
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/)) · τ²/τ³-bench leaderboard: Sierra's own evaluation runs ([MIT](https://opensource.org/license/mit)) · ARC Prize Foundation model\_baseline outputs on ARC-AGI-1/2 public evaluation sets ([MIT](../../licenses/arcprize-model-baseline-outputs-MIT.txt)) · WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)) · Qwen/Qwen3.6-27B model card ([apache-2.0](../../licenses/Qwen--Qwen3.6-27B.txt)) · Terminal-Bench: Benchmarking Agents on Hard ([CC BY 4.0](../../licenses/paper-2601.11868v1-CC-BY-4.0.txt)) · Qwen/Qwen3.5-397B-A17B model card ([apache-2.0](../../licenses/Qwen--Qwen3.5-397B-A17B.txt)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · zai-org/GLM-5 model card ([MIT](../../licenses/zai-org--GLM-5.txt)) · GLM-5 Team ([CC BY 4.0](../../licenses/report-2602.15763v2.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.txt)) · Soohak: A Mathematician-Curated Benchmark for Evaluating Research-level Math Capabilities of LLMs ([CC BY-SA 4.0](../../licenses/paper-2605.09063v3-CC-BY-SA-4.0.txt)) · 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)) · FormalProofBench ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Amazon AGI ([MIT](../../licenses/tau2-verified-MIT.txt)) · 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-4-5.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-4-5.toml)
[interactive version](https://whatmodel.sh/model/claude-opus-4-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)