Model comparison
Codestral vs Kimi K2 (Jul 2025)
Kimi K2 (Jul 2025) is the stronger model overall, scoring 41.2 to 30.6 on the Noometry Index. Codestral costs 2.2× less per token, which makes it the better buy when Kimi K2 (Jul 2025)'s lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
Mistral AISummary
- They share 3 benchmarks with published results for both. Codestral scores higher in 0 categories and Kimi K2 (Jul 2025) in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where Kimi K2 (Jul 2025) leads 42.4 to 27.3.
- The biggest single-benchmark swing is Aider Polyglot: 11.1% for Codestral and 59.1% for Kimi K2 (Jul 2025).
- Codestral is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.57 / $2.30 for Kimi K2 (Jul 2025).
- Kimi K2 (Jul 2025) accepts more context: 262K tokens versus 256K.
- Kimi K2 (Jul 2025) has downloadable open weights; the other is API-only.
Side by side
| Codestral | Kimi K2 (Jul 2025) | |
|---|---|---|
| Provider | Mistral AI | Moonshot AI |
| Noometry Index | 30.6 | 41.2 |
| Released | 2024-05-29 | 2025-07-12 |
| Weights | Proprietary | Open |
| Context window | 256K | 262K |
| Max output | 8K | 262K |
| Input $ / M tokens | $0.30 | $0.57 |
| Output $ / M tokens | $0.90 | $2.30 |
| Results tracked | 7 | 42 |
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Category by category
Coding Kimi K2 (Jul 2025) leads
Codestral: 27.3 (#321), Kimi K2 (Jul 2025): 42.4 (#102)
| Benchmark | Codestral | Kimi K2 (Jul 2025) |
|---|---|---|
| Aider Polyglot | 11.1% | 59.1% |
| ALE-Bench | 137.78 | 597.5 |
| SWE-bench Verified (bash only) | — | 63.4% |
| GSO | — | 4.9% |
| WeirdML | — | 42.8% |
| BigCodeBench Instruct | 41.8% | — |
| LMArena Coding | — | 1399 |
| BigCodeBench Complete | 52.5% | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Agentic & Tool Use Not comparable
Codestral: —, Kimi K2 (Jul 2025): 32.4 (#64)
| Benchmark | Codestral | Kimi K2 (Jul 2025) |
|---|---|---|
| Terminal-Bench | — | 35.7% |
| Berkeley Function Calling Leaderboard | — | 59.1% |
| METR Time Horizons | — | 59.2% |
Reasoning Kimi K2 (Jul 2025) leads
Codestral: 19.8 (#251), Kimi K2 (Jul 2025): 23.3 (#179)
| Benchmark | Codestral | Kimi K2 (Jul 2025) |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | 64.4% |
| SimpleBench | — | 26.3% |
| LMArena Hard Prompts | — | 1384 |
| Epoch Capabilities Index | — | 146.01 |
| ForecastBench | — | 60.2 |
Math Not comparable
Codestral: —, Kimi K2 (Jul 2025): 42.7 (#83)
| Benchmark | Codestral | Kimi K2 (Jul 2025) |
|---|---|---|
| Omni-MATH | — | 65.4% |
| LMArena Math | — | 1397 |
| FrontierMath (Feb 2025 set) | — | 21.4% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Not comparable
Codestral: —, Kimi K2 (Jul 2025): 37.3 (#157)
| Benchmark | Codestral | Kimi K2 (Jul 2025) |
|---|---|---|
| MMLU-Pro | — | 81.9% |
| Confabulations | — | 20.4% |
| Vectara Hallucination Rate | — | 17.9% |
| GPQA (HELM) | — | 65.3% |
| LMArena Expert | — | 1365 |
Multilingual Not comparable
Codestral: —, Kimi K2 (Jul 2025): 49.6 (#130)
| Benchmark | Codestral | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Non-English | — | 1372 |
| LMArena Chinese | — | 1415 |
| LMArena French | — | 1379 |
| LMArena German | — | 1387 |
| LMArena Japanese | — | 1349 |
| LMArena Korean | — | 1325 |
| LMArena Russian | — | 1385 |
| LMArena Spanish | — | 1386 |
Instruction Following Not comparable
Codestral: —, Kimi K2 (Jul 2025): 71.1 (#156)
| Benchmark | Codestral | Kimi K2 (Jul 2025) |
|---|---|---|
| IFEval | — | 85% |
| LMArena Instruction Following | — | 1348 |
Long Context Not comparable
Codestral: —, Kimi K2 (Jul 2025): 41.2 (#145)
| Benchmark | Codestral | Kimi K2 (Jul 2025) |
|---|---|---|
| Fiction.LiveBench | — | 66.7% |
| CL-bench | — | 17.6% |
| LMArena Longer Query | — | 1353 |
Writing & Preference Not comparable
Codestral: —, Kimi K2 (Jul 2025): 62.3 (#78)
| Benchmark | Codestral | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Text | — | 1380 |
| LMArena Creative Writing | — | 1350 |
| Short-Story Creative Writing | — | 85.6% |
| EQ-Bench Creative Writing | — | 1666 |
| WildBench | — | 86.2% |
| LMArena Multi-Turn | — | 1371 |
Frequently asked questions
Is Codestral better than Kimi K2 (Jul 2025)?
Kimi K2 (Jul 2025) is the stronger model overall, scoring 41.2 to 30.6 on the Noometry Index. Codestral costs 2.2× less per token, which makes it the better buy when Kimi K2 (Jul 2025)'s lead doesn't matter for your workload.
Which is cheaper, Codestral or Kimi K2 (Jul 2025)?
Codestral is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Kimi K2 (Jul 2025) lists at $0.57 and $2.30.
Is Codestral or Kimi K2 (Jul 2025) better for coding?
Kimi K2 (Jul 2025) scores higher on coding benchmarks: 42.4 versus 27.3 in the Noometry coding category.
Which has the bigger context window?
Kimi K2 (Jul 2025) does, with 262K tokens against 256K.
How many benchmarks do Codestral and Kimi K2 (Jul 2025) share?
3 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and Kimi K2 (Jul 2025) has 42.