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. | — | 2.4T |
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. | 256k | 1M |
| These models have no shared benchmark scores. | ||
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. | — | 69.3% |
NL2Repo-BenchRepo-level code generation — Tests whether the AI can turn a natural-language requirement into working code across an entire repository, not just produce a single function or patch. Higher is better. | — | 64.9% |
QwenSWEBench V2Software engineering — Qwen's in-house coding benchmark, second version, built around complex real-world software-engineering tasks. Scores are not comparable with the first version. Higher is better. | — | 70% |
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. | — | 26.97% |
Terminal-Bench 3.0Agentic terminal coding — command-line task completion (v3.0, much harder task set) | — | 29% |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | — | 64% |
CoWorkBenchLong-horizon office work — Tests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | — | 76.1% |
Toolathlon-VerifiedPersonal tool use — Tests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | — | 73.3% |
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. | — | 50.8% |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | — | 82.7% |
| Overview | ||
| Company | Mistral | Qwen |
| Release date | Jan 13 2025 | Sep 2 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Codestral 25.01vsClaude Haiku 5.5Qwen3.8-Max-0902vsClaude Haiku 5.5Codestral 25.01vsGPT-6.1 SolQwen3.8-Max-0902vsGPT-6.1 SolCodestral 25.01vsGemini 4 ArgonQwen3.8-Max-0902vsGemini 4 ArgonCodestral 25.01vsMuse Spark OpenQwen3.8-Max-0902vsMuse Spark OpenCodestral 25.01vsGrok 4.7Qwen3.8-Max-0902vsGrok 4.7Codestral 25.01vsDeepSeek-V4.1-Flash-1017Qwen3.8-Max-0902vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Codestral 25.01 and Qwen3.8-Max-0902 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Codestral 25.01 shipped 597 days before Qwen3.8-Max-0902, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Codestral 25.01) vs 1M (Qwen3.8-Max-0902).
Direct benchmark comparisons are unavailable — Codestral 25.01 and Qwen3.8-Max-0902 don't publish scores on any of the same benchmarks.