GPT-4.1 Nano (batch)
OpenAI · released Apr 14, 2025
For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...
Specification
- Context window
- 1.05M
- Max output
- 33K
- Knowledge cutoff
- Jun 30, 2024
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 1
- Moderated
- Yes
Intelligence
9.6
34th percentile
Coding
11.1
Coding Index
Agentic
1.2
Agentic Index
Output speed
—
Median across providers
Latency
—
Time to first token
Cost per task
$0.008
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-Pro65.7%
- GPQA Diamond51.2%
- LiveCodeBench32.6%
- IFBench32.0%
- SciCode25.9%
- AIME 202524.0%
- AA-LCR (long context)19.3%
- τ²-bench (Telecom)17.3%
- Humanity's Last Exam3.8%
- Terminal-Bench Hard3.8%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 65.7% |
| GPQA Diamond | 51.2% |
| LiveCodeBench | 32.6% |
| IFBench | 32.0% |
| SciCode | 25.9% |
| AIME 2025 | 24.0% |
| AA-LCR (long context) | 19.3% |
| τ²-bench (Telecom) | 17.3% |
| Humanity's Last Exam | 3.8% |
| Terminal-Bench Hard | 3.8% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- 9.9
- R1 Distill Llama 70BDeepSeek9.8
- GPT-4.1 Nano (batch)OpenAI9.6
- GPT-4.1 NanoOpenAI9.6
- SonarPerplexity9.4
- 9.4
- GPT-4o (2024-08-06)OpenAI9.4
- Sonar ProPerplexity9.1
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Qwen3 VL 30B A3B Instruct | 9.9 |
| R1 Distill Llama 70B | 9.8 |
| GPT-4.1 Nano (batch) | 9.6 |
| GPT-4.1 Nano | 9.6 |
| Sonar | 9.4 |
| Qwen2.5 72B Instruct | 9.4 |
| GPT-4o (2024-08-06) | 9.4 |
| Sonar Pro | 9.1 |
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
- $0.05
- Output / 1M tokens
- $0.2
- Cached input / 1M
- $0.013
- Blended 3:1
- $0.088
One task, estimated
$0.008
- 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).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.
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
- 1,011
- Win rate
- 49.6%
- Strongest at
- Game development
- Tournaments
- 250
Elo by category
Dot position on a 900–1,025 scale · Elo has no meaningful zero
- 1,011
- 995
- 980
- 951
- 918
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 | 1011 | 49.6% |
| Websites | 995 | 48.1% |
| 3D scenes | 980 | 46.0% |
| UI components | 951 | 43.9% |
| Data visualisation | 918 | 41.1% |