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. | 1B/3B | 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 |
| 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. | $0.027Cloudflare | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.201Cloudflare | — |
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. | — | 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% |
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 | Meta | Mistral |
| Release date | Sep 25 2024 | Oct 6 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3.2vsClaude Sonnet 5.5Mistral Large 4vsClaude Sonnet 5.5LLaMA 3.2vsGPT-6.1 SolMistral Large 4vsGPT-6.1 SolLLaMA 3.2vsGemini 4 ArgonMistral Large 4vsGemini 4 ArgonLLaMA 3.2vsGrok 4.7Mistral Large 4vsGrok 4.7LLaMA 3.2vsDeepSeek-V4.1-FlashMistral Large 4vsDeepSeek-V4.1-FlashLLaMA 3.2vsKimi K3Mistral Large 4vsKimi K3Frequently asked questions
LLaMA 3.2 and Mistral Large 4 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.2 shipped 741 days before Mistral Large 4, so benchmark comparisons should account for the intervening progress.
LLaMA 3.2 has 1B/3B parameters, while Mistral Large 4 has 1T. LLaMA 3.2 is open weight, while Mistral Large 4 is closed.
Direct benchmark comparisons are unavailable — LLaMA 3.2 and Mistral Large 4 don't publish scores on any of the same benchmarks.