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Claude Opus 4.1
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Claude Opus 4.1 is a closed-weights language model from Anthropic, released August 2025. Licence: proprietary.
Catalogue identity notes
Comparison model from the Epoch AI internal-evaluation CSVs (Epoch release date 2025-08-05). Dated API snapshot claude-opus-4-1-20250805. Thinking budgets recorded per result. Anthropic model deprecations page: retired August 5, 2026.
$ how it compares
The LLMs ranked either side of Claude Opus 4.1, out of 828 with a score, then more from Anthropic.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 126 | Claude Opus 4 | 67±1 | comparison only | ||
| 127 |  K-EXAONE 2.0 (750B-A37B) | 67±1 | local | ||
| 128 | Claude Opus 4.1 | 67±1 | comparison only | ||
| 129 |  Kimi K2 Thinking | 67±1 | api $1.07/M +local | ||
| 130 |  Laguna M.1 | 67±2 | api price unknown +local | ||
| more from Anthropic | |||||
| 2 | Claude Opus 5.5 | 87±1 | api $8.00/M | ||
| 4 | Claude Fable 5.1 | 85±1 | api $20.00/M | ||
| 5 | Claude Sonnet 5.5 | 84±1 | api $4.00/M | ||
$ what it's good at
agent tasks▁▁▂▆█▆▅▄▄▃▂▁ #126 of 340 · 24% of its score · 5 tests
Beats o3-pro, o3, GPT-5 mini here, though they rank higher overall. Trails SmolLM3 3B, Jamba 1.5 Large, Nova 2 Pro (Preview) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| SWE-bench Verified | 73.3% ✎ | ▁··▁·▁▂▁▄█▄▁ beats 12/29 | 14% |
| τ-bench Retail lab | 82.4% ✎ | ▁····▃·▂▇█▂▆ beats 28/28 | 3.5% |
| τ²-bench Telecom lab | 71.5% ✎ | ▁·▅█▆▅▂▃▃▂▃▁ beats 56/89 | 3.1% |
| τ-bench Airline lab | 56.0% ✎ | ▁▁▁·▅▄▁█▇▁▃▇ beats 23/28 | 2.4% |
| BFCL V4 (2025-11-03 archive, models absent from the December archive) | 71.2% ✎ | ▃··▅▃▃·▅·▃·█ beats 9/10 | 1.3% |
coding▁▁▁▃▅▇█▇▅▄▃▁ #100 of 524 · 26% of its score · 3 tests
Beats Grok 4.20 (0309 reasoning), Grok 4.3, Muse Glimmer 30B here, though they rank higher overall. Trails Llama 3.1 Nemotron Ultra 253B v1, Qwen3 235B-A22B Thinking 2507 here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| SWE-bench Verified | 73.3% ✎ | ▁··▁·▁▂▁▄█▄▁ beats 12/29 | 14% |
| Arena WebDev (Code Arena) | 1389.4 ✎ | ▁▂▄▃▅█▄▇▆▄▁▁ beats 40/119 | 10% |
| WebApp1K-Duo-React pass@1 (onekq-ai) | 76.9% ✎ | ▁··▁·▁▁▁▆█▇█ beats 43/45 | 2.5% |
chat & writing▁▁▁▃▅▄▆█▇▇▇▃ #61 of 463 * · 5.7% of its score · 1 test
Beats GPT-6 Luna, Grok 4.7, GPT-5.2 here, though they rank higher overall. Trails Gemma 4 31B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena (text) | 1449.8 ✎ | ▁▁▁▂▃▃▃▆▆▇█▃ beats 313/379 | 5.7% |
math▁▂▃▅▇▅▅█▄▁▁▁ #167 of 462 · 32% of its score · 3 tests
Beats MiMo-V2.5 (310B-A15B), MiniMax-M2.7 here, though they rank higher overall. Trails Qwen3-VL 4B Thinking, Granite 4.2 3B, DeepSeek R1 0528 Qwen3 8B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| FrontierMath tiers 1–3 v2 | 12.6% ✎ | ▂··▁▁▃▄▄█▅▃▃ beats 7/82 | 22% |
| FrontierMath tier 4 v2 | 2.4% ✎ | ▁▃▁▂█▅▂▂▁▁▁▁ beats 4/63 | 5.5% |
| OTIS mock AIME 2024–2025 | 68.9% ✎ | ▃▃▄▃▃▃▇▇█▃▂▅ beats 100/193 | 4.0% |
web search▂·▂▇▅█▃▄▁▂▁▁ #53 of 92 * · 2.3% of its score · 1 test
Beats Gemini 3.1 Pro (Preview), Qwen3.7 Max, GPT-5.2 here, though they rank higher overall. Trails GPT-4o mini (2024-07-18), Mistral Small 3.1 24B (2503), Llama 4 Scout (109B-A17B) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Arena search systems (style controlled) | 1167.1 ✎ | ▁·▁▁▂▃▂█▅▂▁▁ beats 8/30 | 2.3% |
other tests8 not in a task · 24% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| GPQA Diamond | 76.8% ✎ | ▁▁▁▁▂▄▆▄▅▇█▅ beats 120/208 | 8.5% |
| KCL-Essay vanilla | 45.5% ✎ | ▁···▁▃▂█▂▃▃▂ beats 18/23 | 4.3% |
| EBR-bench (Epoch) | 7.9% ✎ | ▄▂·▆█▂▆▄▆▆·▄ beats 3/23 | 3.9% |
| KCL-MCQA vanilla | 57.2% ✎ | ▂···█▂▆▆██▆▂ beats 22/23 | 2.3% |
| Mystery Game Puzzles (Epoch) | 21.0% ✎ | █··▂▃▇▄█▂▃▁▁ beats 36/74 | 2.3% |
| Chess Puzzles (Epoch) | 7.0% ✎ | █▁▃▁▁▂▁▄▂▂▁▁ beats 50/135 | 2.3% |
| Vectara hallucination leaderboard, 2025 dataset (HHEM-2.3, factual consistency rate) | 95.8% ✎ | ▁▁▂▃▄▄█▆▃▂▂▁ beats 83/140 | 0.2% |
| Vectara hallucination leaderboard (HHEM-2.3, factual consistency rate) | 88.2% ✎ | ▁▂▁▂▅█▂▃▄▃▁▁ beats 28/98 | 0.0% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · Epoch AI — internal evaluatio… 62%, LMArena leaderboard dataset 18%, Korean Canonical Legal Benchm… 7% and 5 more
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 Epoch AI — internal evaluations | 8 | 62% |
 LMArena leaderboard dataset | 3 | 18% |
 Korean Canonical Legal Benchmark: Toward Knowledge-Independent Evaluation of LLMs’ Legal Reasoning Capabilities | 2 | 7% |
 τ-bench leaderboard: Anthropic's own submissions · lab-reported | 2 | 6% |
 Anthropic system cards and model announcements (lab-reported scores) · lab-reported | 1 | 3% |
 WebApp1K models leaderboard (onekq-ai) | 1 | 3% |
| Berkeley Function Calling Leaderboard result archive | 1 | 1% |
| Vectara hallucination leaderboard (vectara/hallucination-leaderboard repository) | 2 | 0% |
| prior · pull toward the middle | 0% | |
| weighted average | = 67.1 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json