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. | 119B | 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. | 256k | 256k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.15 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $0.60 | — |
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. | $0.15Mistral | $0.60Google |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.60Mistral | $2.50Google |
| $60.00Mistral list price | $245Novita · bf16 | |
| These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 6% | — |
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 | Mar 16 2026 | Nov 6 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Mistral Small 4vsClaude Haiku 5.5Kimi K2 ThinkingvsClaude Haiku 5.5Mistral Small 4vsGPT-6.1 SolKimi K2 ThinkingvsGPT-6.1 SolMistral Small 4vsGemini 4 ArgonKimi K2 ThinkingvsGemini 4 ArgonMistral Small 4vsMuse Spark OpenKimi K2 ThinkingvsMuse Spark OpenMistral Small 4vsGrok 4.7Kimi K2 ThinkingvsGrok 4.7Mistral Small 4vsDeepSeek-V4.1-Flash-1017Kimi K2 ThinkingvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Mistral Small 4 and Kimi K2 Thinking don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Mistral Small 4 has a verified first-party API price: $0.15 per million input tokens and $0.60 per million output tokens. No pay-as-you-go API rate is tracked for Kimi K2 Thinking. Kimi K2 Thinking shipped 130 days before Mistral Small 4, so benchmark comparisons should account for the intervening progress.
Mistral Small 4 has 119B parameters, while Kimi K2 Thinking has 1T. Context windows are 256k (Mistral Small 4) vs 256k (Kimi K2 Thinking). Mistral Small 4 is closed, while Kimi K2 Thinking is open weight.
Direct benchmark comparisons are unavailable — Mistral Small 4 and Kimi K2 Thinking don't publish scores on any of the same benchmarks.