OpenAI
availableShows if the model has enough results for an index.GPT-5.4 nano
GPT-5.4 nano is a reasoning model from OpenAI in the GPT-5.4 family. 46 benchmarks count toward its score, in 8 categories.
IndexOverall score out of 100.53.1 ±2.4
CoverageShare of the index weight with results.100%
SpeedOutput tokens per second.76/s
Input / 1MUS dollars per 1M input tokens.$0.2 batch $0.1
Output / 1MUS dollars per 1M output tokens.$1.25 batch $0.625 US dollars per 1M output tokens in a batch.
ContextMaximum tokens in one request.400K
EloLMArena rating and rank.1373 (#167)
The index is a score out of 100. The ± range shows how much it can change. Batch work costs less.
58,424 votes. Elo shows what people prefer. It does not change the score.
CapabilitiesScore per category, out of 100.
Out of 100Results
46 counted| BenchmarkThe test name. | CategoryThe capability that the test measures. | ResultThe score from the publisher. | IndexThis result as a score out of 100. | RunThe settings of the run. | DateDate of the result. | Published byThe source of the result. |
|---|---|---|---|---|---|---|
| τ²-Bench Tool-Agent-User Evaluation | Agentic | 92.5% | 65.2 | — | — | Victor Barres et al. |
| LiveBench Mathematics | Math | 91.0% | 69.5 | xhigh effort | 25 Jun 2026 | LiveBench |
| AIME | Math | 88.8% | 55.1 | — | 16 Apr 2026 | Vals AI |
| OTIS Mock AIME 2024-2025 | Math | 87.8% | 60.6 | high effort | — | Epoch AI |
| LiveCodeBench | Coding | 84.0% | 60.7 | — | 1 Sept 2026 | Vals AI |
| Graduate-Level Google-Proof Q&A | Knowledge | 82.8% | 54.7 | — | — | David Rein et al. |
| Artificial Analysis GPQA Diamond | Knowledge | 81.7% | 52.3 | — | — | Artificial Analysis |
| LiveBench Reasoning | Reasoning | 81.1% | 68.6 | xhigh effort | 25 Jun 2026 | LiveBench |
| GPQA diamond | Knowledge | 78.5% | 50.7 | high effort | — | Epoch AI |
| GPQA Diamond | Knowledge | 77.5% | 49.8 | — | 1 Sept 2026 | Vals AI |
| MMLU Pro | Knowledge | 77.2% | 42.1 | — | 1 Sept 2026 | Vals AI |
| Artificial Analysis Long Context Reasoning | Reasoning | 76.7% | 61.3 | — | — | Artificial Analysis |
| Artificial Analysis IFBench | Instruction | 75.9% | 66.1 | — | — | Artificial Analysis |
| MMMU Pro | Multimodal | 73.6% | 47.1 | — | 1 Sept 2026 | Vals AI |
| LiveBench Coding | Coding | 70.8% | 55.3 | xhigh effort | 25 Jun 2026 | LiveBench |
| SWE-bench | Coding | 69.8% | 53.5 | — | 1 Sept 2026 | Vals AI |
| MMMU-Pro with Python | Multimodal | 69.5% | — | — | — | OpenAI |
| LiveBench Data Analysis | Reasoning | 67.6% | 49.9 | xhigh effort | 25 Jun 2026 | LiveBench |
| LiveBench Instruction Following | Instruction | 67.2% | 66.1 | xhigh effort | 25 Jun 2026 | LiveBench |
| Massive Multi-discipline Multimodal Understanding Pro | Multimodal | 66.1% | 34.9 | — | — | MMMU-Pro authors |
| Artificial Analysis MMMU-Pro | Multimodal | 65.4% | 47.0 | — | — | Artificial Analysis |
| LiveBench Language | Knowledge | 62.5% | 46.1 | xhigh effort | 25 Jun 2026 | LiveBench |
| EuroEval Swedish | Multilingual | 59.1% | 76.1 | high effort | — | EuroEval |
| EuroEval Swedish | Multilingual | 57.8% | 74.4 | medium effort | — | EuroEval |
| EuroEval Portuguese | Multilingual | 56.7% | 73.1 | high effort | — | EuroEval |
| EuroEval Swedish | Multilingual | 56.4% | 72.7 | low effort | — | EuroEval |
| MCP Atlas | Agentic | 56.1% | 50.5 | — | — | OpenAI |
| Artificial Analysis Coding Index | Coding | 56.1% | 58.5 | — | — | Artificial Analysis |
| EuroEval Portuguese | Multilingual | 55.8% | 72.0 | medium effort | — | EuroEval |
| EuroEval Italian | Multilingual | 55.7% | 71.9 | medium effort | — | EuroEval |
| EuroEval Polish | Multilingual | 55.2% | 71.3 | high effort | — | EuroEval |
| EuroEval Italian | Multilingual | 55.1% | 71.0 | high effort | — | EuroEval |
| EuroEval French | Multilingual | 54.9% | 70.8 | medium effort | — | EuroEval |
| EuroEval French | Multilingual | 54.8% | 70.7 | high effort | — | EuroEval |
| EuroEval Spanish | Multilingual | 53.4% | 68.9 | medium effort | — | EuroEval |
| EuroEval Spanish | Multilingual | 53.3% | 68.8 | high effort | — | EuroEval |
| EuroEval Dutch | Multilingual | 53.1% | 68.6 | medium effort | — | EuroEval |
| EuroEval French | Multilingual | 53.0% | 68.5 | low effort | — | EuroEval |
| EuroEval Dutch | Multilingual | 53.0% | 68.5 | high effort | — | EuroEval |
| EuroEval Portuguese | Multilingual | 52.7% | 68.1 | low effort | — | EuroEval |
| EuroEval Italian | Multilingual | 52.5% | 67.9 | low effort | — | EuroEval |
| EuroEval Polish | Multilingual | 52.4% | 67.8 | medium effort | — | EuroEval |
| EuroEval Dutch | Multilingual | 51.6% | 66.7 | low effort | — | EuroEval |
| ARC-AGI-1 (semi-private) | Reasoning | 51.5% | 51.5 | xhigh effort | — | ARC Prize Foundation |
| EuroEval Spanish | Multilingual | 50.8% | 65.8 | low effort | — | EuroEval |
| EuroEval Polish | Multilingual | 50.7% | 65.6 | low effort | — | EuroEval |
| EuroEval German | Multilingual | 49.2% | 63.7 | medium effort | — | EuroEval |
| EuroEval German | Multilingual | 48.7% | 63.1 | high effort | — | EuroEval |
| Artificial Analysis SciCode | Coding | 47.2% | 58.4 | — | — | Artificial Analysis |
| LiveBench Agentic Coding | Agentic | 46.8% | 60.7 | xhigh effort | 25 Jun 2026 | LiveBench |
| EuroEval German | Multilingual | 46.5% | 60.3 | low effort | — | EuroEval |
| FrontierMath-Tiers-1-3-v2-Private | Math | 44.9% | 56.7 | high effort | — | Epoch AI |
| Terminal-Bench 2.1 | Agentic | 41.6% | 48.6 | — | 21 Sept 2026 | Vals AI |
| Terminal-Bench 2.0 | Agentic | 39.9% | 51.1 | — | 4 Jun 2026 | Vals AI |
| OSWorld-Verified | Agentic | 39.0% | 29.9 | — | — | Tianbao Xie et al. |
| Humanity's Last Exam | Knowledge | 37.7% | 60.7 | — | — | Center for AI Safety et al. |
| Toolathlon | Agentic | 35.5% | 51.1 | — | — | OpenAI |
| Chess Puzzles | Reasoning | 30.0% | 62.3 | high effort | — | Epoch AI |
| Artificial Analysis Humanity's Last Exam | Knowledge | 28.3% | 55.2 | — | — | Artificial Analysis |
| Vibe Code Bench v1.1 | Coding | 26.1% | 53.0 | OpenHands | 21 Sept 2026 | Vals AI |
| FrontierMath-2025-02-28-Private | Math | 25.9% | 54.8 | high effort | — | Epoch AI |
| Artificial Analysis Omniscience Accuracy | Knowledge | 25.7% | 45.7 | — | — | Artificial Analysis |
| APEX-Agents-AA | Agentic | 24.9% | 60.7 | — | — | Artificial Analysis / Mercor |
| Humanity's Last Exam without tools | Knowledge | 24.3% | 49.4 | — | — | OpenAI |
| GDPval-AA normalized | Agentic | 21.8% | 52.3 | — | — | Artificial Analysis |
| Artificial Analysis Intelligence Index | Knowledge | 20.7% | 48.3 | — | — | Artificial Analysis |
| Artificial Analysis Agentic Index | Agentic | 17.7% | 51.6 | — | — | Artificial Analysis |
| IOI v1 | Coding | 15.3% | 48.6 | — | 9 Aug 2026 | Vals AI |
| Code Migration | Coding | 14.5% | 54.2 | — | 21 Sept 2026 | Vals AI |
| FrontierMath-Tier-4-v2-Private | Math | 12.2% | 53.8 | high effort | — | Epoch AI |
| SimpleQA Verified | Knowledge | 11.7% | 32.4 | high effort | — | Epoch AI |
| Critical Physics Tasks | Reasoning | 9.3% | 57.9 | — | — | Artificial Analysis |
| FrontierMath-Tier-4-2025-07-01-Private | Math | 6.3% | 50.2 | high effort | — | Epoch AI |
| ARC-AGI-2 (semi-private) | Reasoning | 5.7% | 42.6 | xhigh effort | — | ARC Prize Foundation |
| Mystery Game Puzzles | Reasoning | 5.0% | 39.2 | high effort | — | Epoch AI |
46 benchmarks count, from 74 of 75 results. A grey row does not count. Too few models took that benchmark.