Model comparison
GPT-5 Nano vs Pixtral Large
GPT-5 Nano is the stronger model overall, scoring 33.5 to 32.2 on the Noometry Index.
Last verified . 2 shared benchmarks.
Mistral AISummary
- They share 2 benchmarks with published results for both. GPT-5 Nano scores higher in 2 categories and Pixtral Large in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-5 Nano leads 39.1 to 32.9.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $2 / $6 for Pixtral Large.
- GPT-5 Nano accepts more context: 400K tokens versus 128K.
- Pixtral Large has downloadable open weights; the other is API-only.
Side by side
| GPT-5 Nano | Pixtral Large | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 33.5 | 32.2 |
| Released | 2025-08-07 | 2024-11-01 |
| Weights | Proprietary | Open |
| Context window | 400K | 128K |
| Max output | 128K | 128K |
| Input $ / M tokens | $0.05 | $2 |
| Output $ / M tokens | $0.40 | $6 |
| Results tracked | 49 | 3 |
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Category by category
Coding Not comparable
GPT-5 Nano: 33.6 (#254), Pixtral Large: —
| Benchmark | GPT-5 Nano | Pixtral Large |
|---|---|---|
| SWE-bench Verified (bash only) | 34.8% | — |
| WeirdML | 38.1% | — |
| LMArena Coding | 1351 | — |
| ALE-Bench | 718.67 | — |
Agentic & Tool Use Not comparable
GPT-5 Nano: 25.8 (#106), Pixtral Large: —
| Benchmark | GPT-5 Nano | Pixtral Large |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| Berkeley Function Calling Leaderboard | 51.5% | — |
Reasoning Pixtral Large leads
GPT-5 Nano: 16.3 (#306), Pixtral Large: 21.7 (#218)
| Benchmark | GPT-5 Nano | Pixtral Large |
|---|---|---|
| ARC-AGI-2 | 2.6% | — |
| Kagi LLM Benchmark | 62.2% | — |
| ARC-AGI-1 | 20.7% | — |
| Chess Puzzles | 27% | — |
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1328 | — |
| Mystery Game Puzzles | 9% | — |
| DTBench | 62.7% | — |
| LMCA | 7.9% | — |
| Epoch Capabilities Index | 139.38 | — |
| ForecastBench | 59.1 | — |
Math Not comparable
GPT-5 Nano: 29.4 (#241), Pixtral Large: —
| Benchmark | GPT-5 Nano | Pixtral Large |
|---|---|---|
| FrontierMath (Tiers 1-3) | 20% | — |
| FrontierMath Tier 4 | 2.4% | — |
| OTIS Mock AIME 2024-2025 | 81.1% | — |
| ProofBench | 12% | — |
| Omni-MATH | 54.6% | — |
| LMArena Math | 1317 | — |
| MATH Level 5 | 95.2% | — |
| FrontierMath (Feb 2025 set) | 8.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Not comparable
GPT-5 Nano: 35.9 (#178), Pixtral Large: —
| Benchmark | GPT-5 Nano | Pixtral Large |
|---|---|---|
| GPQA Diamond | 69.4% | — |
| SimpleQA Verified | 11.7% | — |
| MMLU-Pro | 77.8% | — |
| Vectara Hallucination Rate | 10.5% | — |
| GPQA (HELM) | 67.9% | — |
| LMArena Expert | 1321 | — |
Multimodal Too close to call
GPT-5 Nano: 31.3 (#108), Pixtral Large: 30.6 (#111)
| Benchmark | GPT-5 Nano | Pixtral Large |
|---|---|---|
| LMArena Vision | 1159 | 1089 |
| VPCT | 37.2% | — |
Multilingual Not comparable
GPT-5 Nano: 45.3 (#172), Pixtral Large: —
| Benchmark | GPT-5 Nano | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1313 | — |
| LMArena Chinese | 1356 | — |
| LMArena German | 1327 | — |
| LMArena Japanese | 1226 | — |
| LMArena Korean | 1269 | — |
| LMArena Russian | 1296 | — |
| LMArena Spanish | 1360 | — |
Instruction Following Not comparable
GPT-5 Nano: 75.0 (#79), Pixtral Large: —
| Benchmark | GPT-5 Nano | Pixtral Large |
|---|---|---|
| IFEval | 93.2% | — |
| LMArena Instruction Following | 1306 | — |
Long Context Not comparable
GPT-5 Nano: 31.3 (#281), Pixtral Large: —
| Benchmark | GPT-5 Nano | Pixtral Large |
|---|---|---|
| Fiction.LiveBench | 44.4% | — |
| LMArena Longer Query | 1312 | — |
Writing & Preference GPT-5 Nano leads
GPT-5 Nano: 39.1 (#249), Pixtral Large: 32.9 (#278)
| Benchmark | GPT-5 Nano | Pixtral Large |
|---|---|---|
| EQ-Bench Creative Writing | 705 | 988 |
| LMArena Text | 1320 | — |
| LMArena Creative Writing | 1249 | — |
| WildBench | 80.6% | — |
| LMArena Multi-Turn | 1311 | — |
Frequently asked questions
Is GPT-5 Nano better than Pixtral Large?
GPT-5 Nano is the stronger model overall, scoring 33.5 to 32.2 on the Noometry Index.
Which is cheaper, GPT-5 Nano or Pixtral Large?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Pixtral Large lists at $2 and $6.
Which has the bigger context window?
GPT-5 Nano does, with 400K tokens against 128K.
How many benchmarks do GPT-5 Nano and Pixtral Large share?
2 benchmarks have published results for both models. GPT-5 Nano has 49 scored results on Noometry and Pixtral Large has 3.