Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 70B | 1.05T |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 16k | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.68Mistral |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $2.09Mistral |
| — | $241Mistral · zdr | |
| 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 |
threejsevalCommunity preference (Three.js) — Every model gets the same prompt — "the Eiffel Tower", "a glass fishbowl", "a robot arm picking toys into a box" — and builds a 3D scene in Three.js. Real people then see two scenes side by side, names hidden, and vote for the one they prefer. The votes become a chess-style Elo rating on threejseval.com, averaged across all the prompts. It measures whether the scene looks and moves right to a human eye, not whether the code passes a test. Higher is better. | — | 1104 |
| Overview | ||
| Company | Meta | Mistral |
| Release date | Jan 29 2024 | Oct 6 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Code Llama 70BvsClaude Haiku 5.5Mistral Large 4vsClaude Haiku 5.5Code Llama 70BvsGPT-6.1 SolMistral Large 4vsGPT-6.1 SolCode Llama 70BvsGemini 4 ArgonMistral Large 4vsGemini 4 ArgonCode Llama 70BvsGrok 4.7Mistral Large 4vsGrok 4.7Code Llama 70BvsDeepSeek-V4.1-Flash-1017Mistral Large 4vsDeepSeek-V4.1-Flash-1017Code Llama 70BvsKimi K3Mistral Large 4vsKimi K3Frequently asked questions
Code Llama 70B and Mistral Large 4 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama 70B shipped 981 days before Mistral Large 4, so benchmark comparisons should account for the intervening progress.
Code Llama 70B has 70B parameters, while Mistral Large 4 has 1.05T. Context windows are 16k (Code Llama 70B) vs 1M (Mistral Large 4). Code Llama 70B is open weight, while Mistral Large 4 is closed.
Direct benchmark comparisons are unavailable — Code Llama 70B and Mistral Large 4 don't publish scores on any of the same benchmarks.