Model comparison
GPT-5.6 Luna vs Mistral 7B
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 23.0 on the Noometry Index. Mistral 7B costs 1.8× 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 7B 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 8.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for GPT-5.6 Luna and 0.3% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 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 8K.
- Mistral 7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Luna | Mistral 7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 54.6 | 23.0 |
| Released | 2026-07-09 | 2023-09-27 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 8K |
| Max output | 128K | 8K |
| Input $ / M tokens | $0.20 | $0.25 |
| Output $ / M tokens | $1.20 | $0.25 |
| Results tracked | 52 | 37 |
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Category by category
Coding GPT-5.6 Luna leads
GPT-5.6 Luna: 54.5 (#28), Mistral 7B: 26.4 (#326)
| Benchmark | GPT-5.6 Luna | Mistral 7B |
|---|---|---|
| LMArena Coding | 1466 | 1082 |
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| CursorBench | 35.9% | — |
| LMArena WebDev | 1519 | — |
| SciCode | 53.6% | — |
| WeirdML | 60.9% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 1,667 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
GPT-5.6 Luna: 34.4 (#45), Mistral 7B: —
| Benchmark | GPT-5.6 Luna | Mistral 7B |
|---|---|---|
| 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 7B: 13.1 (#336)
| Benchmark | GPT-5.6 Luna | Mistral 7B |
|---|---|---|
| Chess Puzzles | 40% | 0% |
| LMArena Hard Prompts | 1451 | 1067 |
| DTBench | 89.1% | 42.5% |
| Epoch Capabilities Index | 156.39 | 112.21 |
| 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% | — |
| LMCA | 48.5% | — |
| Surface Evolver Bench | 61.9% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math GPT-5.6 Luna leads
GPT-5.6 Luna: 77.7 (#14), Mistral 7B: 8.1 (#325)
| Benchmark | GPT-5.6 Luna | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 0.3% |
| LMArena Math | 1458 | 1085 |
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| ProofBench | 60% | — |
| MATH Level 5 | — | 3.7% |
| GSM8K | — | 54.4% |
Knowledge GPT-5.6 Luna leads
GPT-5.6 Luna: 58.5 (#34), Mistral 7B: 7.4 (#311)
| Benchmark | GPT-5.6 Luna | Mistral 7B |
|---|---|---|
| GPQA Diamond | 91.6% | 15.2% |
| LMArena Expert | 1478 | 1036 |
| SimpleQA Verified | 41% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multimodal Not comparable
GPT-5.6 Luna: 42.7 (#28), Mistral 7B: —
| Benchmark | GPT-5.6 Luna | Mistral 7B |
|---|---|---|
| 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 7B: 25.8 (#283)
| Benchmark | GPT-5.6 Luna | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1417 | 1012 |
| LMArena Chinese | 1470 | 1009 |
| LMArena French | 1456 | 1037 |
| LMArena German | 1454 | 987 |
| LMArena Japanese | 1411 | 878 |
| LMArena Russian | 1428 | 1018 |
| LMArena Spanish | 1448 | 1026 |
| LMArena Korean | 1415 | — |
Instruction Following GPT-5.6 Luna leads
GPT-5.6 Luna: 75.6 (#57), Mistral 7B: 54.2 (#280)
| Benchmark | GPT-5.6 Luna | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1437 | 1060 |
Long Context GPT-5.6 Luna leads
GPT-5.6 Luna: 43.9 (#82), Mistral 7B: 32.2 (#271)
| Benchmark | GPT-5.6 Luna | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1436 | 1060 |
Writing & Preference GPT-5.6 Luna leads
GPT-5.6 Luna: 68.0 (#29), Mistral 7B: 30.7 (#286)
| Benchmark | GPT-5.6 Luna | Mistral 7B |
|---|---|---|
| LMArena Text | 1431 | 1090 |
| LMArena Creative Writing | 1396 | 1068 |
| LMArena Multi-Turn | 1434 | 1062 |
| EQ-Bench Creative Writing | 1829 | — |
| EQ-Bench 4 | 1156 | — |
Frequently asked questions
Is GPT-5.6 Luna better than Mistral 7B?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 23.0 on the Noometry Index. Mistral 7B costs 1.8× 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 7B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.
Is GPT-5.6 Luna or Mistral 7B better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 8K.
How many benchmarks do GPT-5.6 Luna and Mistral 7B share?
21 benchmarks have published results for both models. GPT-5.6 Luna has 52 scored results on Noometry and Mistral 7B has 37.