GPT-4o
OpenAI · released May 13, 2024
GPT-4o ("o" for "omni") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as...
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
11.1
39th percentile
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
32 t/s
Median across providers
Latency
514ms
Time to first token
Cost per task
$0.38
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-Pro74.8%
- GPQA Diamond54.3%
- IFBench34.3%
- SciCode33.3%
- LiveCodeBench30.9%
- τ²-bench (Telecom)25.1%
- Terminal-Bench Hard8.3%
- AIME 20256.0%
- Humanity's Last Exam2.4%
- AA-LCR (long context)0.0%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 74.8% |
| GPQA Diamond | 54.3% |
| IFBench | 34.3% |
| SciCode | 33.3% |
| LiveCodeBench | 30.9% |
| τ²-bench (Telecom) | 25.1% |
| Terminal-Bench Hard | 8.3% |
| AIME 2025 | 6.0% |
| Humanity's Last Exam | 2.4% |
| AA-LCR (long context) | 0.0% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Mistral Medium 3Mistral AI12.5
- GPT-4oOpenAI11.1
- GPT-4o (2024-11-20)OpenAI11.1
- 11.0
- GLM 4.6VZ.ai10.9
- Llama 4 ScoutMeta10.3
- 9.9
- R1 Distill Llama 70BDeepSeek9.8
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Mistral Medium 3 | 12.5 |
| GPT-4o | 11.1 |
| GPT-4o (2024-11-20) | 11.1 |
| Qwen3 VL 32B Instruct | 11.0 |
| GLM 4.6V | 10.9 |
| Llama 4 Scout | 10.3 |
| Qwen3 VL 30B A3B Instruct | 9.9 |
| R1 Distill Llama 70B | 9.8 |
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
- $2.5
- Output / 1M tokens
- $10
- Cached input / 1M
- $1.25
- Blended 3:1
- $4.38
One task, estimated
$0.38
- 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).
Arena
Head-to-head generation quality
Elo from pairwise judgements, broken out by the kind of thing the model was asked to build.
- Overall Elo
- 947
- Win rate
- 42.3%
- Strongest at
- Game development
- Tournaments
- 241
Elo by category
Dot position on a 850–950 scale · Elo has no meaningful zero
- 947
- 924
- 920
- 884
- 854
Agent categories (full-stack apps, mobile apps) are only scored for models tested in agent harnesses.
View as table
| Category | Elo | Win rate |
|---|---|---|
| Game development | 947 | 42.3% |
| 3D scenes | 924 | 39.2% |
| UI components | 920 | 38.1% |
| Data visualisation | 884 | 36.0% |
| Websites | 854 | 31.5% |