Model comparison
Codestral vs GPT-6 Luna
GPT-6 Luna is the stronger model overall, scoring 53.3 to 30.6 on the Noometry Index.
Last verified . 1 shared benchmarks.
Mistral AISummary
- They share 1 benchmark with published results for both. Codestral scores higher in 0 categories and GPT-6 Luna in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Luna leads 48.2 to 19.8.
- GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $0.30 / $0.90 for Codestral.
- GPT-6 Luna accepts more context: 1.05M tokens versus 256K.
Side by side
| Codestral | GPT-6 Luna | |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 30.6 | 53.3 |
| Released | 2024-05-29 | 2026-09-22 |
| Weights | Proprietary | Proprietary |
| Context window | 256K | 1.05M |
| Max output | 8K | 128K |
| Input $ / M tokens | $0.30 | $0.10 |
| Output $ / M tokens | $0.90 | $0.50 |
| Results tracked | 7 | 42 |
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Category by category
Coding GPT-6 Luna leads
Codestral: 27.3 (#321), GPT-6 Luna: 55.5 (#25)
| Benchmark | Codestral | GPT-6 Luna |
|---|---|---|
| ALE-Bench | 137.78 | 1,577 |
| DeepSWE | — | 66.6% |
| FrontierCode | — | 42.4% |
| Aider Polyglot | 11.1% | — |
| LMArena WebDev | — | 1581 |
| SciCode | — | 54.6% |
| BigCodeBench Instruct | 41.8% | — |
| LMArena Coding | — | 1439 |
| BigCodeBench Complete | 52.5% | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Agentic & Tool Use Not comparable
Codestral: —, GPT-6 Luna: 33.3 (#54)
| Benchmark | Codestral | GPT-6 Luna |
|---|---|---|
| APEX-Agents | — | 44.3% |
| GDP.pdf | — | 23% |
Reasoning GPT-6 Luna leads
Codestral: 19.8 (#251), GPT-6 Luna: 48.2 (#41)
| Benchmark | Codestral | GPT-6 Luna |
|---|---|---|
| ARC-AGI-2 | — | 59.3% |
| Kagi LLM Benchmark | 32.5% | — |
| NYT Connections (extended) | — | 68.7% |
| ARC-AGI-1 | — | 86.7% |
| CritPt | — | 19.4% |
| Chess Puzzles | — | 31% |
| LMArena Hard Prompts | — | 1411 |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 90.1% |
| LMCA | — | 44.5% |
| Epoch Capabilities Index | — | 156.28 |
Math Not comparable
Codestral: —, GPT-6 Luna: 76.1 (#15)
| Benchmark | Codestral | GPT-6 Luna |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 78.9% |
| FrontierMath Tier 4 | — | 56.1% |
| OTIS Mock AIME 2024-2025 | — | 98.9% |
| ProofBench | — | 64% |
| LMArena Math | — | 1416 |
Knowledge Not comparable
Codestral: —, GPT-6 Luna: 57.0 (#41)
| Benchmark | Codestral | GPT-6 Luna |
|---|---|---|
| GPQA Diamond | — | 90.5% |
| SimpleQA Verified | — | 41.4% |
| LMArena Expert | — | 1444 |
Multimodal Not comparable
Codestral: —, GPT-6 Luna: 42.4 (#30)
| Benchmark | Codestral | GPT-6 Luna |
|---|---|---|
| LMArena Vision | — | 1217 |
| Blueprint-Bench 2 | — | 31.2% |
| Furniture Assembly | — | 44.2% |
Multilingual Not comparable
Codestral: —, GPT-6 Luna: 50.5 (#117)
| Benchmark | Codestral | GPT-6 Luna |
|---|---|---|
| LMArena Non-English | — | 1386 |
| LMArena Chinese | — | 1433 |
| LMArena French | — | 1420 |
| LMArena German | — | 1369 |
| LMArena Japanese | — | 1369 |
| LMArena Korean | — | 1360 |
| LMArena Russian | — | 1394 |
| LMArena Spanish | — | 1393 |
Instruction Following Not comparable
Codestral: —, GPT-6 Luna: 74.3 (#99)
| Benchmark | Codestral | GPT-6 Luna |
|---|---|---|
| LMArena Instruction Following | — | 1409 |
Long Context Not comparable
Codestral: —, GPT-6 Luna: 43.0 (#111)
| Benchmark | Codestral | GPT-6 Luna |
|---|---|---|
| LMArena Longer Query | — | 1409 |
Writing & Preference Not comparable
Codestral: —, GPT-6 Luna: 58.3 (#119)
| Benchmark | Codestral | GPT-6 Luna |
|---|---|---|
| LMArena Text | — | 1391 |
| LMArena Creative Writing | — | 1363 |
| LMArena Multi-Turn | — | 1396 |
Frequently asked questions
Is Codestral better than GPT-6 Luna?
GPT-6 Luna is the stronger model overall, scoring 53.3 to 30.6 on the Noometry Index.
Which is cheaper, Codestral or GPT-6 Luna?
GPT-6 Luna is cheaper. It lists at $0.10 per million input tokens and $0.50 per million output tokens; Codestral lists at $0.30 and $0.90.
Is Codestral or GPT-6 Luna better for coding?
GPT-6 Luna scores higher on coding benchmarks: 55.5 versus 27.3 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Luna does, with 1.05M tokens against 256K.
How many benchmarks do Codestral and GPT-6 Luna share?
1 benchmark has published results for both models. Codestral has 7 scored results on Noometry and GPT-6 Luna has 42.