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. | 123B | 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 | 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. | $0.40Mistral | $0.57Novita |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.00Mistral | $2.30Novita |
| $180Mistral | $229Novita · fp8 | |
| Benchmarks | ||
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. | 72.2% | 65.8% |
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 MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 61.3% | — |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 32.6% | — |
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 | Dec 9 2025 | Jul 11 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Devstral 2 (123B)vsClaude Haiku 5.5Kimi K2vsClaude Haiku 5.5Devstral 2 (123B)vsGPT-6.1 SolKimi K2vsGPT-6.1 SolDevstral 2 (123B)vsGemini 4 ArgonKimi K2vsGemini 4 ArgonDevstral 2 (123B)vsMuse Spark OpenKimi K2vsMuse Spark OpenDevstral 2 (123B)vsGrok 4.7Kimi K2vsGrok 4.7Devstral 2 (123B)vsDeepSeek-V4.1-Flash-1017Kimi K2vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Devstral 2 (123B) leads Kimi K2 on 1 of the 1 benchmark they both report (SWE-Bench Verified). Kimi K2 shipped 151 days before Devstral 2 (123B), so benchmark comparisons should account for the intervening progress.
Devstral 2 (123B) has 123B parameters, while Kimi K2 has 1T. Context windows are 256k (Devstral 2 (123B)) vs 128k (Kimi K2).
On SWE-Bench Verified, Devstral 2 (123B) leads at 72.2% vs Kimi K2 at 65.8%.