Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 1T | 1T |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 512k | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. 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 third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $2.30Novita |
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% |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 61.7% | — |
SWEAtlas CodeBase QnACodebase understanding — Questions about how an unfamiliar codebase actually works — where something is handled, what a change would touch — answered by reading the repository rather than editing it. Tests understanding rather than patch-writing. Higher is better. | 59.4% | — |
AA Coding Agent IndexAgentic coding — Artificial Analysis' overall score for coding agents, combining three coding benchmarks with what each run costs and how many tokens it burns. It rates a model paired with a particular agent harness rather than the model alone, so the same model scores differently in different tools. Higher is better. | 49.8 | — |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 28.3% | — |
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% |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | 59.9% | — |
AA-Briefcase v1.1Knowledge work — Artificial Analysis agentic office-work eval (Elo, v1.1 rating fit) | 1393 | — |
| Overview | ||
| Company | Mistral | Moonshot AI |
| Release date | Oct 6 2026 | Jul 11 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Mistral Large 4vsClaude Sonnet 5.5Kimi K2vsClaude Sonnet 5.5Mistral Large 4vsGPT-6.1 SolKimi K2vsGPT-6.1 SolMistral Large 4vsGemini 4 ArgonKimi K2vsGemini 4 ArgonMistral Large 4vsMuse Spark 1.3Kimi K2vsMuse Spark 1.3Mistral Large 4vsGrok 4.7Kimi K2vsGrok 4.7Mistral Large 4vsDeepSeek-V4.1-FlashKimi K2vsDeepSeek-V4.1-FlashFrequently asked questions
Mistral Large 4 and Kimi K2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi K2 shipped 452 days before Mistral Large 4, so benchmark comparisons should account for the intervening progress.
Mistral Large 4 has 1T parameters, while Kimi K2 has 1T. Context windows are 512k (Mistral Large 4) vs 128k (Kimi K2). Mistral Large 4 is closed, while Kimi K2 is open weight.
Direct benchmark comparisons are unavailable — Mistral Large 4 and Kimi K2 don't publish scores on any of the same benchmarks.