o1 (batch)
OpenAI · released Dec 17, 2024
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...
Specification
- Context window
- 200K
- Max output
- 100K
- Knowledge cutoff
- Oct 31, 2023
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 1
- Moderated
- Yes
Intelligence
23.9
66th percentile
Coding
39.7
Coding Index
Agentic
—
Agentic Index
Output speed
—
Median across providers
Latency
—
Time to first token
Cost per task
$2.77
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
- MMLU-Pro84.1%
- GPQA Diamond74.7%
- AIME 202572.3%
- IFBench70.3%
- LiveCodeBench67.9%
- AA-LCR (long context)63.3%
- τ²-bench (Telecom)62.6%
- SciCode35.8%
- Terminal-Bench Hard12.9%
- Humanity's Last Exam7.0%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 84.1% |
| GPQA Diamond | 74.7% |
| AIME 2025 | 72.3% |
| IFBench | 70.3% |
| LiveCodeBench | 67.9% |
| AA-LCR (long context) | 63.3% |
| τ²-bench (Telecom) | 62.6% |
| SciCode | 35.8% |
| Terminal-Bench Hard | 12.9% |
| Humanity's Last Exam | 7.0% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Ling 3.0 Tiny (free)InclusionAI24.3
- Claude Haiku 4.5Anthropic24.1
- gpt-oss-120bOpenAI24.1
- Kimi K2 0905Moonshot AI24.0
- o1 (batch)OpenAI23.9
- o1OpenAI23.9
- GLM 4.6Z.ai23.4
- GLM 4.7 FlashZ.ai23.3
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Ling 3.0 Tiny (free) | 24.3 |
| Claude Haiku 4.5 | 24.1 |
| gpt-oss-120b | 24.1 |
| Kimi K2 0905 | 24.0 |
| o1 (batch) | 23.9 |
| o1 | 23.9 |
| GLM 4.6 | 23.4 |
| GLM 4.7 Flash | 23.3 |
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
- $7.5
- Output / 1M tokens
- $30
- Cached input / 1M
- $3.75
- Blended 3:1
- $13.13
One task, estimated
$2.77
- 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.