Model comparison
GPT-5.6 Luna vs Pixtral Large
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 32.2 on the Noometry Index.
Last verified . 2 shared benchmarks.
Mistral AISummary
- They share 2 benchmarks with published results for both. GPT-5.6 Luna scores higher in 3 categories and Pixtral Large in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-5.6 Luna leads 68.0 to 32.9.
- GPT-5.6 Luna is cheaper at $0.20 / $1.20 per million input/output tokens, against $2 / $6 for Pixtral Large.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 128K.
- Pixtral Large has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Luna | Pixtral Large | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 54.6 | 32.2 |
| Released | 2026-07-09 | 2024-11-01 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 128K |
| Input $ / M tokens | $0.20 | $2 |
| Output $ / M tokens | $1.20 | $6 |
| Results tracked | 52 | 3 |
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Category by category
Coding Not comparable
GPT-5.6 Luna: 54.5 (#28), Pixtral Large: —
| Benchmark | GPT-5.6 Luna | Pixtral Large |
|---|---|---|
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| CursorBench | 35.9% | — |
| LMArena WebDev | 1519 | — |
| SciCode | 53.6% | — |
| WeirdML | 60.9% | — |
| LMArena Coding | 1466 | — |
| ALE-Bench | 1,667 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Luna: 34.4 (#45), Pixtral Large: —
| Benchmark | GPT-5.6 Luna | Pixtral Large |
|---|---|---|
| APEX-Agents | 43% | — |
| BALROG | 45.6% | — |
| GDP.pdf | 22.7% | — |
| Vending-Bench 2 | 4,095 | — |
Reasoning GPT-5.6 Luna leads
GPT-5.6 Luna: 47.6 (#43), Pixtral Large: 21.7 (#218)
| Benchmark | GPT-5.6 Luna | Pixtral Large |
|---|---|---|
| ARC-AGI-2 | 59.5% | — |
| SimpleBench | 46.8% | — |
| Kagi LLM Benchmark | 49.1% | — |
| NYT Connections (extended) | 69.4% | — |
| ARC-AGI-1 | 88% | — |
| CritPt | 20.6% | — |
| Chess Puzzles | 40% | — |
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1451 | — |
| Mystery Game Puzzles | 21% | — |
| DTBench | 89.1% | — |
| LMCA | 48.5% | — |
| Surface Evolver Bench | 61.9% | — |
| Epoch Capabilities Index | 156.39 | — |
Math Not comparable
GPT-5.6 Luna: 77.7 (#14), Pixtral Large: —
| Benchmark | GPT-5.6 Luna | Pixtral Large |
|---|---|---|
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 60% | — |
| LMArena Math | 1458 | — |
Knowledge Not comparable
GPT-5.6 Luna: 58.5 (#34), Pixtral Large: —
| Benchmark | GPT-5.6 Luna | Pixtral Large |
|---|---|---|
| GPQA Diamond | 91.6% | — |
| SimpleQA Verified | 41% | — |
| LMArena Expert | 1478 | — |
Multimodal GPT-5.6 Luna leads
GPT-5.6 Luna: 42.7 (#28), Pixtral Large: 30.6 (#111)
| Benchmark | GPT-5.6 Luna | Pixtral Large |
|---|---|---|
| LMArena Vision | 1258 | 1089 |
| Blueprint-Bench 2 | 22.6% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1457 | — |
Multilingual Not comparable
GPT-5.6 Luna: 52.8 (#78), Pixtral Large: —
| Benchmark | GPT-5.6 Luna | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1470 | — |
| LMArena French | 1456 | — |
| LMArena German | 1454 | — |
| LMArena Japanese | 1411 | — |
| LMArena Korean | 1415 | — |
| LMArena Russian | 1428 | — |
| LMArena Spanish | 1448 | — |
Instruction Following Not comparable
GPT-5.6 Luna: 75.6 (#57), Pixtral Large: —
| Benchmark | GPT-5.6 Luna | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1437 | — |
Long Context Not comparable
GPT-5.6 Luna: 43.9 (#82), Pixtral Large: —
| Benchmark | GPT-5.6 Luna | Pixtral Large |
|---|---|---|
| LMArena Longer Query | 1436 | — |
Writing & Preference GPT-5.6 Luna leads
GPT-5.6 Luna: 68.0 (#29), Pixtral Large: 32.9 (#278)
| Benchmark | GPT-5.6 Luna | Pixtral Large |
|---|---|---|
| EQ-Bench Creative Writing | 1829 | 988 |
| LMArena Text | 1431 | — |
| LMArena Creative Writing | 1396 | — |
| EQ-Bench 4 | 1156 | — |
| LMArena Multi-Turn | 1434 | — |
Frequently asked questions
Is GPT-5.6 Luna better than Pixtral Large?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 32.2 on the Noometry Index.
Which is cheaper, GPT-5.6 Luna or Pixtral Large?
GPT-5.6 Luna is cheaper. It lists at $0.20 per million input tokens and $1.20 per million output tokens; Pixtral Large lists at $2 and $6.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 128K.
How many benchmarks do GPT-5.6 Luna and Pixtral Large share?
2 benchmarks have published results for both models. GPT-5.6 Luna has 52 scored results on Noometry and Pixtral Large has 3.