GPT-4o-mini
OpenAI · released Jul 18, 2024
GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...
Specification
- Context window
- 128K
- Max output
- 16K
- Knowledge cutoff
- Oct 31, 2023
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 2
- Moderated
- Yes
- Uptime
- 100.0%
Intelligence
6.7
22th percentile
Coding
11.4
Coding Index
Agentic
1.0
Agentic Index
Output speed
36 t/s
Median across providers
Latency
539ms
Time to first token
Cost per task
$0.02
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-Pro64.8%
- GPQA Diamond42.6%
- IFBench31.0%
- LiveCodeBench23.4%
- SciCode22.9%
- AIME 202514.7%
- Humanity's Last Exam4.2%
- τ²-bench (Telecom)2.9%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 64.8% |
| GPQA Diamond | 42.6% |
| IFBench | 31.0% |
| LiveCodeBench | 23.4% |
| SciCode | 22.9% |
| AIME 2025 | 14.7% |
| Humanity's Last Exam | 4.2% |
| τ²-bench (Telecom) | 2.9% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Mistral Large 2407Mistral AI7.0
- 6.9
- GLM 4.5VZ.ai6.8
- GPT-4OpenAI6.8
- GPT-4o-miniOpenAI6.7
- Gemini 2.5 Flash LiteGoogle6.7
- GPT-4o-mini (2024-07-18)OpenAI6.7
- Granite 4.1 8BIBM Granite6.4
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Mistral Large 2407 | 7.0 |
| Qwen2.5 Coder 32B Instruct | 6.9 |
| GLM 4.5V | 6.8 |
| GPT-4 | 6.8 |
| GPT-4o-mini | 6.7 |
| Gemini 2.5 Flash Lite | 6.7 |
| GPT-4o-mini (2024-07-18) | 6.7 |
| Granite 4.1 8B | 6.4 |
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
- $0.15
- Output / 1M tokens
- $0.6
- Cached input / 1M
- $0.075
- Blended 3:1
- $0.262
One task, estimated
$0.02
- Input tokens
- 50,000
- Output tokens
- 25,000
- Profile
- Standard
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).