OpenAI
availableShows if the model has enough results for an index.GPT-4.1 nano
GPT-4.1 nano is a non-reasoning model from OpenAI in the GPT-4.1 family. 24 benchmarks count toward its score, in 7 categories.
IndexOverall score. 50 is the middle.25.5 ±3.8
CoverageShare of the index weight with results.95%
SpeedOutput tokens per second.39/s
Input / 1MUS dollars per 1M input tokens.$0.1 batch $0.05
Output / 1MUS dollars per 1M output tokens.$0.4 batch $0.2 US dollars per 1M output tokens in a batch.
ContextMaximum tokens in one request.1.05M
EloLMArena rating and rank.1285 (#250)
50 is the middle of the board. The range shows the doubt in the index. Batch work costs less.
6,103 votes. Elo shows what people prefer. It does not change the score.
CapabilitiesScore per category. 50 is the middle.
50 is the middleResults
24 counted| BenchmarkThe test name. | CategoryThe capability that the test measures. | ResultThe score from the publisher. | IndexThis result on the index scale. | RunThe settings of the run. | DateDate of the result. | Published byThe source of the result. |
|---|---|---|---|---|---|---|
| Instruction-Following Eval | Instruction | 83.2% | 30.6 | — | — | Jeffrey Zhou et al. |
| MATH 500 | Math | 80.2% | 31.4 | — | 9 Jan 2026 | Vals AI |
| Massive Multitask Language Understanding | Knowledge | 80.1% | — | — | — | Dan Hendrycks et al. |
| MATH level 5 | Math | 70.0% | 33.1 | — | — | Epoch AI |
| MGSM | Multilingual | 69.3% | — | — | 9 Jan 2026 | Vals AI |
| MMLU Pro | Knowledge | 63.5% | 20.4 | — | 1 Sept 2026 | Vals AI |
| MMMU Pro | Multimodal | 55.1% | 16.9 | — | 1 Sept 2026 | Vals AI |
| Artificial Analysis GPQA Diamond | Knowledge | 51.2% | 21.0 | — | — | Artificial Analysis |
| GPQA Diamond | Knowledge | 50.8% | 25.1 | — | 1 Sept 2026 | Vals AI |
| Graduate-Level Google-Proof Q&A | Knowledge | 50.3% | 24.7 | — | — | David Rein et al. |
| GPQA diamond | Knowledge | 48.9% | 23.4 | — | — | Epoch AI |
| LiveCodeBench | Coding | 42.7% | 23.2 | — | 1 Sept 2026 | Vals AI |
| Artificial Analysis MMMU-Pro | Multimodal | 40.1% | 16.1 | — | — | Artificial Analysis |
| Artificial Analysis IFBench | Instruction | 32.0% | 21.6 | — | — | Artificial Analysis |
| OTIS Mock AIME 2024-2025 | Math | 28.9% | 27.8 | — | — | Epoch AI |
| AIME | Math | 26.5% | 26.0 | — | 16 Apr 2026 | Vals AI |
| Artificial Analysis Long Context Reasoning | Reasoning | 20.3% | 22.3 | — | — | Artificial Analysis |
| τ²-Bench Tool-Agent-User Evaluation | Agentic | 17.3% | 11.2 | — | — | Victor Barres et al. |
| Artificial Analysis Omniscience Accuracy | Knowledge | 13.7% | 30.8 | — | — | Artificial Analysis |
| Artificial Analysis Coding Index | Coding | 11.1% | 26.8 | — | — | Artificial Analysis |
| Artificial Analysis Intelligence Index | Knowledge | 7.8% | 32.2 | — | — | Artificial Analysis |
| SimpleQA Verified | Knowledge | 6.0% | 27.1 | — | — | Epoch AI |
| Artificial Analysis Humanity's Last Exam | Knowledge | 3.8% | 28.7 | — | — | Artificial Analysis |
| FrontierMath-2025-02-28-Private | Math | 1.0% | 31.6 | — | — | Epoch AI |
| GDPval-AA normalized | Agentic | 0.0% | 35.6 | — | — | Artificial Analysis |
| Critical Physics Tasks | Reasoning | 0.0% | 38.4 | — | — | Artificial Analysis |
| ARC-AGI-1 (semi-private) | Reasoning | 0.0% | 27.2 | — | — | ARC Prize Foundation |
| ARC-AGI-2 (semi-private) | Reasoning | 0.0% | 39.7 | — | — | ARC Prize Foundation |
24 benchmarks count, from 26 of 28 results. A grey row does not count. Too few models took that benchmark.