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. | 671B | 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. | 128k | 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. | $0.27SiliconFlow | $0.68Mistral |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.41SiliconFlow | $2.09Mistral |
| $74.50SiliconFlow · fp8 | $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 | DeepSeek | Mistral |
| Release date | Sep 29 2025 | Oct 6 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.2 ExpvsClaude Haiku 5.5Mistral Large 4vsClaude Haiku 5.5DeepSeek V3.2 ExpvsGPT-6.1 SolMistral Large 4vsGPT-6.1 SolDeepSeek V3.2 ExpvsGemini 4 ArgonMistral Large 4vsGemini 4 ArgonDeepSeek V3.2 ExpvsMuse Spark OpenMistral Large 4vsMuse Spark OpenDeepSeek V3.2 ExpvsGrok 4.7Mistral Large 4vsGrok 4.7DeepSeek V3.2 ExpvsKimi K3Mistral Large 4vsKimi K3Frequently asked questions
DeepSeek V3.2 Exp and Mistral Large 4 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V3.2 Exp shipped 372 days before Mistral Large 4, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.2 Exp has 671B parameters, while Mistral Large 4 has 1.05T. Context windows are 128k (DeepSeek V3.2 Exp) vs 1M (Mistral Large 4). DeepSeek V3.2 Exp is open weight, while Mistral Large 4 is closed.
Direct benchmark comparisons are unavailable — DeepSeek V3.2 Exp and Mistral Large 4 don't publish scores on any of the same benchmarks.