Model comparison
Codestral vs Kimi K2.7 Code
Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 30.1 on the Noometry Index. Codestral costs 3.8× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
Mistral AISummary
- They share 1 benchmark with published results for both. Codestral scores higher in 0 categories and Kimi K2.7 Code in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Kimi K2.7 Code leads 38.2 to 19.1.
- Codestral is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- Kimi K2.7 Code accepts more context: 262K tokens versus 256K.
Side by side
| Codestral | Kimi K2.7 Code | |
|---|---|---|
| Provider | Mistral AI | Moonshot AI |
| Noometry Index | 30.1 | 43.0 |
| Released | 2024-05-29 | 2026-06-12 |
| Weights | Open | Open |
| Context window | 256K | 262K |
| Max output | 4K | 262K |
| Input $ / M tokens | $0.30 | $0.95 |
| Output $ / M tokens | $0.90 | $4 |
| Results tracked | 7 | 19 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Kimi K2.7 Code leads
Codestral: 26.9 (#316), Kimi K2.7 Code: 42.4 (#92)
| Benchmark | Codestral | Kimi K2.7 Code |
|---|---|---|
| ALE-Bench | 137.78 | 886.23 |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| Aider Polyglot | 11.1% | — |
| LMArena WebDev | — | 1473 |
| SciCode | — | 47.5% |
| WeirdML | — | 54.1% |
| BigCodeBench Instruct | 41.8% | — |
| BigCodeBench Complete | 52.5% | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Agentic & Tool Use Not comparable
Codestral: —, Kimi K2.7 Code: 23.9 (#115)
| Benchmark | Codestral | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | — | 37.6% |
| GBAEval | — | 0.9% |
| Vending-Bench 2 | — | 5,083 |
Reasoning Kimi K2.7 Code leads
Codestral: 19.1 (#244), Kimi K2.7 Code: 38.2 (#56)
| Benchmark | Codestral | Kimi K2.7 Code |
|---|---|---|
| SimpleBench | — | 57.9% |
| Kagi LLM Benchmark | 32.5% | — |
| CritPt | — | 10% |
| Chess Puzzles | — | 21% |
| Surface Evolver Bench | — | 48.8% |
| Epoch Capabilities Index | — | 150.01 |
Math Not comparable
Codestral: —, Kimi K2.7 Code: 52.6 (#39)
| Benchmark | Codestral | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| OTIS Mock AIME 2024-2025 | — | 95.6% |
Knowledge Not comparable
Codestral: —, Kimi K2.7 Code: 53.6 (#51)
| Benchmark | Codestral | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | — | 87.9% |
| SimpleQA Verified | — | 36.5% |
Frequently asked questions
Is Codestral better than Kimi K2.7 Code?
Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 30.1 on the Noometry Index. Codestral costs 3.8× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Which is cheaper, Codestral or Kimi K2.7 Code?
Codestral is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.
Is Codestral or Kimi K2.7 Code better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.4 versus 26.9 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.7 Code does, with 262K tokens against 256K.
How many benchmarks do Codestral and Kimi K2.7 Code share?
1 benchmark has published results for both models. Codestral has 7 scored results on Noometry and Kimi K2.7 Code has 19.