o3 Mini
OpenAI · released Jan 31, 2025
OpenAI o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and coding. This model supports the `reasoning_effort` parameter, which can be set to...
Specification
- Context window
- 200K
- Max output
- 100K
- Knowledge cutoff
- Oct 31, 2023
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 1
- Moderated
- Yes
- Uptime
- 100.0%
Intelligence
19.2
57th percentile
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
92 t/s
Median across providers
Latency
2.42s
Time to first token
Cost per task
$0.41
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-Pro79.1%
- AIME 202577.0%
- GPQA Diamond74.8%
- LiveCodeBench71.7%
- SciCode39.9%
- τ²-bench (Telecom)28.7%
- Humanity's Last Exam7.9%
- Terminal-Bench Hard6.8%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 79.1% |
| AIME 2025 | 77.0% |
| GPQA Diamond | 74.8% |
| LiveCodeBench | 71.7% |
| SciCode | 39.9% |
| τ²-bench (Telecom) | 28.7% |
| Humanity's Last Exam | 7.9% |
| Terminal-Bench Hard | 6.8% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- North Mini Code (free)Cohere20.2
- GPT-5 NanoOpenAI20.1
- GLM 4.5Z.ai19.7
- Kimi K2 0711Moonshot AI19.7
- GPT-4.1OpenAI19.6
- o3 MiniOpenAI19.2
- o1-proOpenAI19.1
- Trinity Large ThinkingArcee AI18.6
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| North Mini Code (free) | 20.2 |
| GPT-5 Nano | 20.1 |
| GLM 4.5 | 19.7 |
| Kimi K2 0711 | 19.7 |
| GPT-4.1 | 19.6 |
| o3 Mini | 19.2 |
| o1-pro | 19.1 |
| Trinity Large Thinking | 18.6 |
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
- $1.1
- Output / 1M tokens
- $4.4
- Cached input / 1M
- $0.55
- Blended 3:1
- $1.93
One task, estimated
$0.41
- 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.