Model comparison
GPT-5.6 Luna vs Mistral Small 3
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 31.2 on the Noometry Index. Mistral Small 3 costs 7.8× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Mistral AISummary
- They share 21 benchmarks with published results for both. GPT-5.6 Luna scores higher in 8 categories and Mistral Small 3 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 16.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for GPT-5.6 Luna and 6.7% for Mistral Small 3.
- Mistral Small 3 is cheaper at $0.05 / $0.08 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 33K.
- Mistral Small 3 has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Luna | Mistral Small 3 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 54.6 | 31.2 |
| Released | 2026-07-09 | 2025-01-30 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 33K |
| Max output | 128K | 16K |
| Input $ / M tokens | $0.20 | $0.05 |
| Output $ / M tokens | $1.20 | $0.08 |
| Results tracked | 52 | 24 |
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Category by category
Coding GPT-5.6 Luna leads
GPT-5.6 Luna: 54.5 (#28), Mistral Small 3: 36.5 (#207)
| Benchmark | GPT-5.6 Luna | Mistral Small 3 |
|---|---|---|
| LMArena Coding | 1466 | 1246 |
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| CursorBench | 35.9% | — |
| LMArena WebDev | 1519 | — |
| SciCode | 53.6% | — |
| WeirdML | 60.9% | — |
| BigCodeBench Instruct | — | 45.3% |
| BigCodeBench Complete | — | 50.4% |
| ALE-Bench | 1,667 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Luna: 34.4 (#45), Mistral Small 3: —
| Benchmark | GPT-5.6 Luna | Mistral Small 3 |
|---|---|---|
| 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: 18.9 (#273)
| Benchmark | GPT-5.6 Luna | Mistral Small 3 |
|---|---|---|
| Chess Puzzles | 40% | 0% |
| LMArena Hard Prompts | 1451 | 1233 |
| Epoch Capabilities Index | 156.39 | 127.07 |
| 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: 16.3 (#295)
| Benchmark | GPT-5.6 Luna | Mistral Small 3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 6.7% |
| LMArena Math | 1458 | 1240 |
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| ProofBench | 60% | — |
Knowledge GPT-5.6 Luna leads
GPT-5.6 Luna: 58.5 (#34), Mistral Small 3: 25.1 (#263)
| Benchmark | GPT-5.6 Luna | Mistral Small 3 |
|---|---|---|
| GPQA Diamond | 91.6% | 47.3% |
| LMArena Expert | 1478 | 1202 |
| SimpleQA Verified | 41% | — |
| Confabulations | — | 25.2% |
Multimodal Not comparable
GPT-5.6 Luna: 42.7 (#28), Mistral Small 3: —
| Benchmark | GPT-5.6 Luna | Mistral Small 3 |
|---|---|---|
| LMArena Vision | 1258 | — |
| 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: 37.3 (#236)
| Benchmark | GPT-5.6 Luna | Mistral Small 3 |
|---|---|---|
| LMArena Non-English | 1417 | 1198 |
| LMArena Chinese | 1470 | 1204 |
| LMArena French | 1456 | 1203 |
| LMArena German | 1454 | 1211 |
| LMArena Japanese | 1411 | 1111 |
| LMArena Korean | 1415 | 1188 |
| LMArena Russian | 1428 | 1216 |
| LMArena Spanish | 1448 | — |
Instruction Following GPT-5.6 Luna leads
GPT-5.6 Luna: 75.6 (#57), Mistral Small 3: 63.7 (#229)
| Benchmark | GPT-5.6 Luna | Mistral Small 3 |
|---|---|---|
| LMArena Instruction Following | 1437 | 1214 |
Long Context GPT-5.6 Luna leads
GPT-5.6 Luna: 43.9 (#82), Mistral Small 3: 37.8 (#211)
| Benchmark | GPT-5.6 Luna | Mistral Small 3 |
|---|---|---|
| LMArena Longer Query | 1436 | 1246 |
Writing & Preference GPT-5.6 Luna leads
GPT-5.6 Luna: 68.0 (#29), Mistral Small 3: 32.2 (#280)
| Benchmark | GPT-5.6 Luna | Mistral Small 3 |
|---|---|---|
| LMArena Text | 1431 | 1234 |
| LMArena Creative Writing | 1396 | 1195 |
| EQ-Bench Creative Writing | 1829 | 707 |
| LMArena Multi-Turn | 1434 | 1217 |
| EQ-Bench 4 | 1156 | — |
Frequently asked questions
Is GPT-5.6 Luna better than Mistral Small 3?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 31.2 on the Noometry Index. Mistral Small 3 costs 7.8× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Luna or Mistral Small 3?
Mistral Small 3 is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.
Is GPT-5.6 Luna or Mistral Small 3 better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 36.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 33K.
How many benchmarks do GPT-5.6 Luna and Mistral Small 3 share?
21 benchmarks have published results for both models. GPT-5.6 Luna has 52 scored results on Noometry and Mistral Small 3 has 24.