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. | — | 512k |
| 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. | $0.15DeepSeek | $0.68Mistral |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.60DeepSeek | $2.09Mistral |
| $60.00DeepSeek | $241Mistral · zdr | |
| Benchmarks | ||
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. | 77% | 61.7% |
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. | 42.3% | 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. | 60.5% | 59.9% |
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. | 68.2% | — |
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 |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 42.6% | — |
Agent's Last Exam · pass@1Agentic computer use — A hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | 35.4% | — |
AA-Briefcase v1.1Knowledge work — Artificial Analysis agentic office-work eval (Elo, v1.1 rating fit) | — | 1393 |
Chartography · with toolsChart tasks — A chart-centred test run with tools available to the AI, reported separately from the chart-reading benchmarks above it. Higher is better. | 83% | — |
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 | Oct 17 2026 | Oct 6 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek-V4.1-Flash-1017vsClaude Haiku 5.5Mistral Large 4vsClaude Haiku 5.5DeepSeek-V4.1-Flash-1017vsGPT-6.1 SolMistral Large 4vsGPT-6.1 SolDeepSeek-V4.1-Flash-1017vsGemini 4 ArgonMistral Large 4vsGemini 4 ArgonDeepSeek-V4.1-Flash-1017vsMuse Spark OpenMistral Large 4vsMuse Spark OpenDeepSeek-V4.1-Flash-1017vsGrok 4.7Mistral Large 4vsGrok 4.7DeepSeek-V4.1-Flash-1017vsKimi K3Mistral Large 4vsKimi K3Frequently asked questions
DeepSeek-V4.1-Flash-1017 leads Mistral Large 4 on 3 of the 3 benchmarks they both report (DeepSWE 1.1, Terminal-Bench 4.0, AutomationBench). Mistral Large 4 shipped 11 days before DeepSeek-V4.1-Flash-1017, so benchmark comparisons should account for the intervening progress.
DeepSeek-V4.1-Flash-1017 is open weight, while Mistral Large 4 is closed.
On DeepSWE 1.1, DeepSeek-V4.1-Flash-1017 leads at 77% vs Mistral Large 4 at 61.7%. On Terminal-Bench 4.0, DeepSeek-V4.1-Flash-1017 leads at 42.3% vs Mistral Large 4 at 28.3%. On AutomationBench, DeepSeek-V4.1-Flash-1017 leads at 60.5% vs Mistral Large 4 at 59.9%.