o3 Pro (batch)
OpenAI · released Jun 10, 2025
The o-series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o3-pro model uses more compute to think harder and provide consistently...
Specification
- Context window
- 200K
- Max output
- 100K
- Knowledge cutoff
- Jun 30, 2024
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 1
- Moderated
- Yes
Intelligence
33.3
78th percentile
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
—
Median across providers
Latency
—
Time to first token
Cost per task
$3.70
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- GPQA Diamond84.5%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| GPQA Diamond | 84.5% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- 34.3
- LongCat 2.0Meituan33.9
- KAT-Coder-Pro V2KwaiPilot33.9
- Kimi K2 ThinkingMoonshot AI33.5
- o3 Pro (batch)OpenAI33.3
- o3 ProOpenAI33.3
- 32.8
- 32.5
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Qwen3.5 397B A17B | 34.3 |
| LongCat 2.0 | 33.9 |
| KAT-Coder-Pro V2 | 33.9 |
| Kimi K2 Thinking | 33.5 |
| o3 Pro (batch) | 33.3 |
| o3 Pro | 33.3 |
| Qwen3.5-122B-A10B | 32.8 |
| Qwen3 Max Thinking | 32.5 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $10
- Output / 1M tokens
- $40
- Cached input / 1M
- Not offered
- Blended 3:1
- $17.5
One task, estimated
$3.70
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.