Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 1T |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 32k | 256k |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.60Google |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $2.50Google |
| — | $245Novita · bf16 | |
| These models have no shared benchmark scores. | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 71.3% |
| Overview | ||
| Company | Mistral | Moonshot AI |
| Release date | Dec 11 2023 | Nov 6 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Mistral MediumvsClaude Haiku 5.5Kimi K2 ThinkingvsClaude Haiku 5.5Mistral MediumvsGPT-6.1 SolKimi K2 ThinkingvsGPT-6.1 SolMistral MediumvsGemini 4 ArgonKimi K2 ThinkingvsGemini 4 ArgonMistral MediumvsMuse Spark OpenKimi K2 ThinkingvsMuse Spark OpenMistral MediumvsGrok 4.7Kimi K2 ThinkingvsGrok 4.7Mistral MediumvsDeepSeek-V4.1-Flash-1017Kimi K2 ThinkingvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Mistral Medium and Kimi K2 Thinking don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mistral Medium shipped 696 days before Kimi K2 Thinking, so benchmark comparisons should account for the intervening progress.
Context windows are 32k (Mistral Medium) vs 256k (Kimi K2 Thinking). Mistral Medium is closed, while Kimi K2 Thinking is open weight.
Direct benchmark comparisons are unavailable — Mistral Medium and Kimi K2 Thinking don't publish scores on any of the same benchmarks.