whatmodel.sh / Claude 2.0
Claude 2.0
↗ share
+ contribute
Claude 2.0 is a closed-weights language model from Anthropic, released July 2023. Licence: proprietary.
Catalogue identity notes
Comparison model from the Epoch AI internal-evaluation CSVs (Epoch release date 2023-07-11). API model claude-2.0. Anthropic model deprecations page: retired July 21, 2025.
$ how it compares
The LLMs ranked either side of Claude 2.0, out of 828 with a score, then more from Anthropic.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 523 |  speechless-starcoder2-15b | 45±1 | local | ||
| 524 |  Ministral 3 3B | 45±1 | api $0.10/M +local | ||
| 525 | Claude 2.0 | 45±1 | comparison only | ||
| 526 |  OpenThinker 7B | 45±2 | local | ||
| 527 |  Phi-3 Medium 128K Instruct | 45±1 | 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
coding▁▁▁▃▅▇█▇▅▄▃▁ #371 of 524 * · 59% of its score · 2 tests
Beats GLM-4.7-Flash, Qwen3-VL 8B Instruct, Qwen3-VL 4B Instruct here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, WizardLM 70B V1.0, Phi-4-mini 3.8B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| HumanEval | 69.5% ✎ | ▁·▁▁▃▄▆▅█▃▁▁ beats 55/99 | 33% |
| HumanEval+ | 61.6% ✎ | ▁·▁▁▁▃▄▄▄█▃▁ beats 54/99 | 26% |
chat & writing▁▁▁▃▅▄▆█▇▇▇▃ #349 of 463 · 31% of its score · 5 tests
Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, OpenThinker3 7B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 700M, EXAONE 3.5 2.4B Instruct here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| AlpacaEval 2 length-controlled | 28.2% ✎ | ▁▂▂▃▆▆█▆▄▄▁▁ beats 75/122 | 14% |
| Arena-Hard (v0.1, GPT-4-1106 judge) | 24.0% ✎ | ▂▁▁▅█▅▄▃▃▅▃▁ beats 22/60 | 10% |
| Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024) | 1164.0 ✎ | ▂▁▂▁▃▇█▆▇▄▂▂ beats 73/95 | 3.9% |
| MT-Bench (GPT-4 single-answer grading, LMSYS runs) | 8.0600 MT-Bench score (1-10) ✎ | ▆▂▂▆▆▄██▂▄▄▆ beats 22/28 | 3.4% |
| AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge) | 91.4% ✎ | ▂▂·▆·▇▃▆█▆▄▂ beats 33/45 | 0.7% |
math▁▂▃▅▇▅▅█▄▁▁▁ #403 of 462 * · 5.7% of its score · 2 tests
Beats DBRX Instruct here, though they rank higher overall. Trails Qwen3 0.6B, Gemma 7B (base), Qwen2.5 1.5B Instruct here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| MATH level 5 | 11.7% ✎ | ▂▂▅▆▄█▆▃▅▂▃▄ beats 10/92 | 5.2% |
| OTIS mock AIME 2024–2025 | 2.5% ✎ | ▃▃▄▃▃▃▇▇█▃▂▅ beats 20/193 | 0.5% |
other tests2 not in a task · 3.2% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| GPQA Diamond | 34.7% ✎ | ▁▁▁▁▂▄▆▄▅▇█▅ beats 27/208 | 2.9% |
| Vectara hallucination leaderboard, 2025 dataset (HHEM-2.3, factual consistency rate) | 82.6% ✎ | ▁▁▃▃▄▄█▆▃▂▂▁ beats 7/140 | 0.3% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · EvalPlus / EvalPerf published… 59%, AlpacaEval 1.0 and 2.0 leader… 14%, Arena-Hard-Auto v0.1 leaderbo… 10% and 4 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
$ 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