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. | 22B | 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 | 128k |
| 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. | — | $0.57Novita |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $2.30Novita |
| — | $229Novita · fp8 | |
| 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. | — | 10% |
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. | — | 65.8% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 4.7% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 75.1% |
| Overview | ||
| Company | Mistral | Moonshot AI |
| Release date | May 29 2024 | Jul 11 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Codestral 22BvsClaude Haiku 5.5Kimi K2vsClaude Haiku 5.5Codestral 22BvsGPT-6.1 SolKimi K2vsGPT-6.1 SolCodestral 22BvsGemini 4 ArgonKimi K2vsGemini 4 ArgonCodestral 22BvsMuse Spark OpenKimi K2vsMuse Spark OpenCodestral 22BvsGrok 4.7Kimi K2vsGrok 4.7Codestral 22BvsDeepSeek-V4.1-Flash-1017Kimi K2vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Codestral 22B and Kimi K2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Codestral 22B shipped 408 days before Kimi K2, so benchmark comparisons should account for the intervening progress.
Codestral 22B has 22B parameters, while Kimi K2 has 1T. Context windows are 32k (Codestral 22B) vs 128k (Kimi K2). Codestral 22B is closed, while Kimi K2 is open weight.
Direct benchmark comparisons are unavailable — Codestral 22B and Kimi K2 don't publish scores on any of the same benchmarks.