Model comparison
GPT-5.6 Luna vs Mistral Medium
GPT-5.6 Luna is the stronger model overall, scoring 54.7 to 36.0 on the Noometry Index.
Last verified . 30 shared benchmarks.
Mistral AISummary
- They share 30 benchmarks with published results for both. GPT-5.6 Luna scores higher in 10 categories and Mistral Medium in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.5 to 27.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for GPT-5.6 Luna and 32.2% for Mistral Medium.
- GPT-5.6 Luna is cheaper at $0.20 / $1.20 per million input/output tokens, against $1.50 / $7.50 for Mistral Medium.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 262K.
- Mistral Medium has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Luna | Mistral Medium | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 54.7 | 36.0 |
| Released | 2026-07-09 | 2023-12-11 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 128K | 262K |
| Input $ / M tokens | $0.20 | $1.50 |
| Output $ / M tokens | $1.20 | $7.50 |
| Results tracked | 51 | 36 |
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Category by category
Coding GPT-5.6 Luna leads
GPT-5.6 Luna: 53.9 (#25), Mistral Medium: 33.8 (#241)
| Benchmark | GPT-5.6 Luna | Mistral Medium |
|---|---|---|
| FrontierCode | 39.8% | 8% |
| SciCode | 53.6% | 40.2% |
| WeirdML | 60.9% | 43.7% |
| LMArena Coding | 1466 | 1434 |
| ALE-Bench | 1,667 | 763.98 |
| DeepSWE | 67.2% | — |
| CursorBench | 35.9% | — |
| LMArena WebDev | 1517 | — |
Agentic & Tool Use GPT-5.6 Luna leads
GPT-5.6 Luna: 35.3 (#37), Mistral Medium: 28.1 (#84)
| Benchmark | GPT-5.6 Luna | Mistral Medium |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.7% |
| BALROG | 45.6% | — |
| GDP.pdf | 22.7% | — |
| Vending-Bench 2 | 4,095 | — |
Reasoning GPT-5.6 Luna leads
GPT-5.6 Luna: 47.5 (#38), Mistral Medium: 23.3 (#165)
| Benchmark | GPT-5.6 Luna | Mistral Medium |
|---|---|---|
| Kagi LLM Benchmark | 49.1% | 50% |
| CritPt | 20.6% | 0% |
| LMArena Hard Prompts | 1452 | 1426 |
| DTBench | 89.1% | 75.5% |
| LMCA | 48.5% | 26.1% |
| Surface Evolver Bench | 61.9% | 26.9% |
| ARC-AGI-2 | 59.5% | — |
| SimpleBench | 46.8% | — |
| NYT Connections (extended) | 69.4% | — |
| ARC-AGI-1 | 88% | — |
| Chess Puzzles | 40% | — |
| Mystery Game Puzzles | 21% | — |
| Epoch Capabilities Index | 156.32 | — |
Math GPT-5.6 Luna leads
GPT-5.6 Luna: 77.5 (#12), Mistral Medium: 27.2 (#238)
| Benchmark | GPT-5.6 Luna | Mistral Medium |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 32.2% |
| ProofBench | 60% | 9% |
| LMArena Math | 1455 | 1409 |
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| MATH Level 5 | — | 81.6% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge GPT-5.6 Luna leads
GPT-5.6 Luna: 58.6 (#31), Mistral Medium: 24.9 (#260)
| Benchmark | GPT-5.6 Luna | Mistral Medium |
|---|---|---|
| GPQA Diamond | 91.6% | 59.5% |
| LMArena Expert | 1479 | 1408 |
| Humanity's Last Exam | — | 4.5% |
| SimpleQA Verified | 41% | — |
| Vectara Hallucination Rate | — | 22.7% |
Multimodal GPT-5.6 Luna leads
GPT-5.6 Luna: 43.0 (#22), Mistral Medium: 34.6 (#82)
| Benchmark | GPT-5.6 Luna | Mistral Medium |
|---|---|---|
| LMArena Vision | 1259 | 1172 |
| Blueprint-Bench 2 | 22.6% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1457 | — |
Multilingual Too close to call
GPT-5.6 Luna: 53.0 (#73), Mistral Medium: 52.3 (#86)
| Benchmark | GPT-5.6 Luna | Mistral Medium |
|---|---|---|
| LMArena Non-English | 1418 | 1408 |
| LMArena Chinese | 1469 | 1448 |
| LMArena French | 1457 | 1460 |
| LMArena German | 1459 | 1433 |
| LMArena Japanese | 1416 | 1378 |
| LMArena Korean | 1414 | 1382 |
| LMArena Russian | 1428 | 1411 |
| LMArena Spanish | 1445 | 1433 |
Instruction Following GPT-5.6 Luna leads
GPT-5.6 Luna: 76.0 (#48), Mistral Medium: 73.9 (#111)
| Benchmark | GPT-5.6 Luna | Mistral Medium |
|---|---|---|
| LMArena Instruction Following | 1440 | 1398 |
Long Context GPT-5.6 Luna leads
GPT-5.6 Luna: 43.9 (#78), Mistral Medium: 42.9 (#108)
| Benchmark | GPT-5.6 Luna | Mistral Medium |
|---|---|---|
| LMArena Longer Query | 1437 | 1406 |
Writing & Preference GPT-5.6 Luna leads
GPT-5.6 Luna: 68.3 (#27), Mistral Medium: 60.2 (#97)
| Benchmark | GPT-5.6 Luna | Mistral Medium |
|---|---|---|
| LMArena Text | 1432 | 1424 |
| LMArena Creative Writing | 1397 | 1391 |
| LMArena Multi-Turn | 1433 | 1418 |
| Short-Story Creative Writing | — | 77.3% |
| EQ-Bench Creative Writing | 1829 | — |
| EQ-Bench 4 | 1156 | — |
Frequently asked questions
Is GPT-5.6 Luna better than Mistral Medium?
GPT-5.6 Luna is the stronger model overall, scoring 54.7 to 36.0 on the Noometry Index.
Which is cheaper, GPT-5.6 Luna or Mistral Medium?
GPT-5.6 Luna is cheaper. It lists at $0.20 per million input tokens and $1.20 per million output tokens; Mistral Medium lists at $1.50 and $7.50.
Is GPT-5.6 Luna or Mistral Medium better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 53.9 versus 33.8 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 262K.
How many benchmarks do GPT-5.6 Luna and Mistral Medium share?
30 benchmarks have published results for both models. GPT-5.6 Luna has 51 scored results on Noometry and Mistral Medium has 36.