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 |
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. | 100k | 512k |
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 |
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | 58.7% | — |
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% |
GSM8KGrade-school math — Grade-school maths word problems that take a few steps of arithmetic to work through. It separated the models of 2022 and 2023 sharply, then saturated. One caveat on the historical numbers: OpenAI included part of the GSM8K training set in GPT-4's pre-training mix, so GPT-4's score is not a clean few-shot result. Higher is better. | 86.7% | — |
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 | Anthropic | Mistral |
| Release date | Aug 9 2023 | Oct 6 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Claude Instant 1.2vsGPT-6.1 SolMistral Large 4vsGPT-6.1 SolClaude Instant 1.2vsGemini 4 ArgonMistral Large 4vsGemini 4 ArgonClaude Instant 1.2vsMuse Spark 1.3Mistral Large 4vsMuse Spark 1.3Claude Instant 1.2vsGrok 4.7Mistral Large 4vsGrok 4.7Claude Instant 1.2vsDeepSeek-V4.1-FlashMistral Large 4vsDeepSeek-V4.1-FlashClaude Instant 1.2vsKimi K3Mistral Large 4vsKimi K3Frequently asked questions
Claude Instant 1.2 and Mistral Large 4 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Claude Instant 1.2 shipped 1154 days before Mistral Large 4, so benchmark comparisons should account for the intervening progress.
Context windows are 100k (Claude Instant 1.2) vs 512k (Mistral Large 4).
Direct benchmark comparisons are unavailable — Claude Instant 1.2 and Mistral Large 4 don't publish scores on any of the same benchmarks.