Model comparison
GPT-5.6 Luna vs Mistral Small 3.1
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 31.7 on the Noometry Index.
Last verified . 23 shared benchmarks.
Mistral AISummary
- They share 23 benchmarks with published results for both. GPT-5.6 Luna scores higher in 9 categories and Mistral Small 3.1 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 14.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for GPT-5.6 Luna and 3.9% for Mistral Small 3.1.
- Mistral Small 3.1 is cheaper at $0.35 / $0.56 per million input/output tokens, against $0.20 / $1.20 for GPT-5.6 Luna.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 128K.
- Mistral Small 3.1 has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Luna | Mistral Small 3.1 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 54.6 | 31.7 |
| Released | 2026-07-09 | 2025-03-17 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 102K |
| Input $ / M tokens | $0.20 | $0.35 |
| Output $ / M tokens | $1.20 | $0.56 |
| Results tracked | 52 | 28 |
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Category by category
Coding GPT-5.6 Luna leads
GPT-5.6 Luna: 54.5 (#28), Mistral Small 3.1: 38.3 (#179)
| Benchmark | GPT-5.6 Luna | Mistral Small 3.1 |
|---|---|---|
| LMArena Coding | 1466 | 1309 |
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| CursorBench | 35.9% | — |
| LMArena WebDev | 1519 | — |
| SciCode | 53.6% | — |
| WeirdML | 60.9% | — |
| ALE-Bench | 1,667 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Luna: 34.4 (#45), Mistral Small 3.1: —
| Benchmark | GPT-5.6 Luna | Mistral Small 3.1 |
|---|---|---|
| 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), Mistral Small 3.1: 19.7 (#254)
| Benchmark | GPT-5.6 Luna | Mistral Small 3.1 |
|---|---|---|
| Chess Puzzles | 40% | 1% |
| LMArena Hard Prompts | 1451 | 1278 |
| Epoch Capabilities Index | 156.39 | 127.48 |
| 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% | — |
| Mystery Game Puzzles | 21% | — |
| DTBench | 89.1% | — |
| LMCA | 48.5% | — |
| Surface Evolver Bench | 61.9% | — |
Math GPT-5.6 Luna leads
GPT-5.6 Luna: 77.7 (#14), Mistral Small 3.1: 14.7 (#301)
| Benchmark | GPT-5.6 Luna | Mistral Small 3.1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 3.9% |
| LMArena Math | 1458 | 1262 |
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| ProofBench | 60% | — |
| Omni-MATH | — | 24.8% |
Knowledge GPT-5.6 Luna leads
GPT-5.6 Luna: 58.5 (#34), Mistral Small 3.1: 22.6 (#271)
| Benchmark | GPT-5.6 Luna | Mistral Small 3.1 |
|---|---|---|
| GPQA Diamond | 91.6% | 41.9% |
| LMArena Expert | 1478 | 1257 |
| SimpleQA Verified | 41% | — |
| MMLU-Pro | — | 61% |
| GPQA (HELM) | — | 39.2% |
Multimodal GPT-5.6 Luna leads
GPT-5.6 Luna: 42.7 (#28), Mistral Small 3.1: 33.2 (#99)
| Benchmark | GPT-5.6 Luna | Mistral Small 3.1 |
|---|---|---|
| LMArena Vision | 1258 | 1136 |
| Blueprint-Bench 2 | 22.6% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1457 | — |
Multilingual GPT-5.6 Luna leads
GPT-5.6 Luna: 52.8 (#78), Mistral Small 3.1: 41.2 (#209)
| Benchmark | GPT-5.6 Luna | Mistral Small 3.1 |
|---|---|---|
| LMArena Non-English | 1417 | 1255 |
| LMArena Chinese | 1470 | 1253 |
| LMArena French | 1456 | 1273 |
| LMArena German | 1454 | 1266 |
| LMArena Japanese | 1411 | 1208 |
| LMArena Korean | 1415 | 1206 |
| LMArena Russian | 1428 | 1263 |
| LMArena Spanish | 1448 | 1283 |
Instruction Following GPT-5.6 Luna leads
GPT-5.6 Luna: 75.6 (#57), Mistral Small 3.1: 63.6 (#230)
| Benchmark | GPT-5.6 Luna | Mistral Small 3.1 |
|---|---|---|
| LMArena Instruction Following | 1437 | 1264 |
| IFEval | — | 75% |
Long Context GPT-5.6 Luna leads
GPT-5.6 Luna: 43.9 (#82), Mistral Small 3.1: 39.5 (#178)
| Benchmark | GPT-5.6 Luna | Mistral Small 3.1 |
|---|---|---|
| LMArena Longer Query | 1436 | 1299 |
Writing & Preference GPT-5.6 Luna leads
GPT-5.6 Luna: 68.0 (#29), Mistral Small 3.1: 37.0 (#259)
| Benchmark | GPT-5.6 Luna | Mistral Small 3.1 |
|---|---|---|
| LMArena Text | 1431 | 1277 |
| LMArena Creative Writing | 1396 | 1253 |
| EQ-Bench Creative Writing | 1829 | 761 |
| LMArena Multi-Turn | 1434 | 1270 |
| WildBench | — | 78.8% |
| EQ-Bench 4 | 1156 | — |
Frequently asked questions
Is GPT-5.6 Luna better than Mistral Small 3.1?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 31.7 on the Noometry Index.
Which is cheaper, GPT-5.6 Luna or Mistral Small 3.1?
Mistral Small 3.1 is cheaper. It lists at $0.35 per million input tokens and $0.56 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.
Is GPT-5.6 Luna or Mistral Small 3.1 better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 38.3 in the Noometry coding category.
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 Mistral Small 3.1 share?
23 benchmarks have published results for both models. GPT-5.6 Luna has 52 scored results on Noometry and Mistral Small 3.1 has 28.