[whatmodel.sh](../../README.md) / o3-pro
# o3-pro
OpenAI · released 2025-06 · proprietary
**score 69.5** ±1.5 · #83 of 732 · from 5 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks o3-pro among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄●▅▄▄▂▁ #127 of 313* 9.4% 1
coding ▁▁▁▃▅▇█▇▅●▃▁ #70 of 469* 41% 1
math ▁▁▃▅█▆▄█●▂▁▁ #70 of 431* 13% 1
agent tasks · #127 of 313 · 9.4% of its score
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
Thinkingbox-bench pass@1 (507 sta… 19.3% ▅··▃·●·█▃▅██ beats 3/15 very hard · avg 5.1% 9.4% sharp · at its level
coding · #70 of 469 · 41% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-5.2](gpt-5-2.md), [GPT-5](gpt-5.md) here, though they rank higher overall. Trails [MiMo-V2.5-Pro (1T-A42B)](mimo-v2-5-pro.md), [Qwen3.6 Plus](qwen3-6-plus.md), [MiniMax-M3](minimax-m3.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Aider polyglot (225 exercises, tw… 84.9% ▂·▂▃▅▂▃█▆▄▂● beats 48/49 very hard · avg 7.6% 41% sharp
math · #70 of 431 · 13% of its score
Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [Claude Opus 4.5](claude-opus-4-5.md), [Qwen3.7 Plus](qwen3-7-plus.md) here, though they rank higher overall. Trails [INTELLECT-3](intellect-3.md), [GPT-5 mini](gpt-5-mini.md), [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AIME 2024 (maker) 93.0% ▁▁▁▂▁▃▁▅███● beats 81/85 medium · avg 50% 13% sharp · maxed out · small run
other tests (2)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 84.0% ▁▁▁▁▂▄▅▅█●█▄ beats 228/322 hard · avg 38% 36% sharp
Vectara hallucination leaderboard… 76.7% ●▂▁▂▅█▂▃▄▃▁▁ beats 2/98 very easy · avg 90% 0.0% blunt
source results share of the score
OpenAI vendor-reported benchmark scores… 2 49% ▇▇▇▇▇▇
Aider polyglot benchmark runs 1 41% ▇▇▇▇▇
One Success Isn't Reliability: Thinking… 1 9% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 1% ▇
weighted average = 69.5
Licences: Aider polyglot benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · One Success Isn't Reliability: Thinkingbox ([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))
## How to run it
### Through an API
api (OpenAI) · $35.00/M blended · $20 in / $80 out per 1M tokens · [developers.openai.com](https://developers.openai.com/api/docs/models/o3-pro)
[price source](https://github.com/BerriAI/litellm/blob/c19ce71bcdad234ca87a7af10cee45375ce811a8/model_prices_and_context_window.json): USD per 1M text tokens, standard uncached input and output. LiteLLM record 'o3-pro-2025-06-10' (litellm\_provider openai, mode responses): input\_cost\_per\_token 2e-05, output\_cost\_per\_token 8e-05; Batch and search-context charges excluded. Currency: USD. · [context source](https://github.com/BerriAI/litellm/blob/c19ce71bcdad234ca87a7af10cee45375ce811a8/model_prices_and_context_window.json): provider maximum input tokens (LiteLLM record o3-pro-2025-06-10); not an intrinsic model maximum
[interactive version](https://whatmodel.sh/model/o3-pro) · [JSON](https://whatmodel.sh/api?format=json&all=1)