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 | 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. | — | $0.95 |
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. | — | $4.00 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.19 |
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. | — | $0.6712Inceptron |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $3.00StreamLake |
| Benchmarks | ||
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% | 31% |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | — | 74% |
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% | — |
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 | Jun 12 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Mistral Large 4vsClaude Sonnet 5.5Kimi K2.7 CodevsClaude Sonnet 5.5Mistral Large 4vsGPT-6.1 SolKimi K2.7 CodevsGPT-6.1 SolMistral Large 4vsGemini 4 ArgonKimi K2.7 CodevsGemini 4 ArgonMistral Large 4vsMuse Spark 1.3Kimi K2.7 CodevsMuse Spark 1.3Mistral Large 4vsGrok 4.7Kimi K2.7 CodevsGrok 4.7Mistral Large 4vsDeepSeek-V4.1-FlashKimi K2.7 CodevsDeepSeek-V4.1-FlashFrequently asked questions
Mistral Large 4 leads Kimi K2.7 Code on 1 of the 1 benchmark they both report (DeepSWE 1.1). Only Kimi K2.7 Code has a verified first-party API price: $0.95 per million input tokens and $4.00 per million output tokens. No pay-as-you-go API rate is tracked for Mistral Large 4. Kimi K2.7 Code shipped 116 days before Mistral Large 4, so benchmark comparisons should account for the intervening progress.
Mistral Large 4 has 1T parameters, while Kimi K2.7 Code has 1T. Context windows are 512k (Mistral Large 4) vs 256k (Kimi K2.7 Code). Mistral Large 4 is closed, while Kimi K2.7 Code is open weight.
On DeepSWE 1.1, Mistral Large 4 leads at 61.7% vs Kimi K2.7 Code at 31%.