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. | — | 24B |
| 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 | $0.075DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.60DeepSeek | $0.20DeepInfra |
| $60.00DeepSeek | $25.00DeepInfra · fp8 | |
| 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. | 77% | — |
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% | — |
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% | — |
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% | — |
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% | — |
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% | — |
| Overview | ||
| Company | DeepSeek | Mistral |
| Release date | Oct 17 2026 | Jun 20 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-V4.1-Flash-1017vsClaude Haiku 5.5Mistral Small 3.2vsClaude Haiku 5.5DeepSeek-V4.1-Flash-1017vsGPT-6.1 SolMistral Small 3.2vsGPT-6.1 SolDeepSeek-V4.1-Flash-1017vsGemini 4 ArgonMistral Small 3.2vsGemini 4 ArgonDeepSeek-V4.1-Flash-1017vsMuse Spark OpenMistral Small 3.2vsMuse Spark OpenDeepSeek-V4.1-Flash-1017vsGrok 4.7Mistral Small 3.2vsGrok 4.7DeepSeek-V4.1-Flash-1017vsKimi K3Mistral Small 3.2vsKimi K3Frequently asked questions
DeepSeek-V4.1-Flash-1017 and Mistral Small 3.2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mistral Small 3.2 shipped 484 days before DeepSeek-V4.1-Flash-1017, so benchmark comparisons should account for the intervening progress.
Published specifications for these two models are limited — see each model page for the latest details.
Direct benchmark comparisons are unavailable — DeepSeek-V4.1-Flash-1017 and Mistral Small 3.2 don't publish scores on any of the same benchmarks.