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. | — | 2.4T |
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. | — | 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. | $0.15DeepSeek | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.60DeepSeek | — |
| $60.00DeepSeek | — | |
| 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% | 69.3% |
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% | 64.9% |
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% | 26.97% |
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% | 50.8% |
QwenSWEBench V2Software engineering — Qwen's in-house coding benchmark, second version, built around complex real-world software-engineering tasks. Scores are not comparable with the first version. Higher is better. | — | 70% |
Terminal-Bench 3.0Agentic terminal coding — command-line task completion (v3.0, much harder task set) | — | 29% |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | — | 64% |
CoWorkBenchLong-horizon office work — Tests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | — | 76.1% |
Toolathlon-VerifiedPersonal tool use — Tests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | — | 73.3% |
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% | — |
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% | — |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | — | 82.7% |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Oct 17 2026 | Sep 2 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek-V4.1-Flash-1017vsClaude Haiku 5.5Qwen3.8-Max-0902vsClaude Haiku 5.5DeepSeek-V4.1-Flash-1017vsGPT-6.1 SolQwen3.8-Max-0902vsGPT-6.1 SolDeepSeek-V4.1-Flash-1017vsGemini 4 ArgonQwen3.8-Max-0902vsGemini 4 ArgonDeepSeek-V4.1-Flash-1017vsMuse Spark OpenQwen3.8-Max-0902vsMuse Spark OpenDeepSeek-V4.1-Flash-1017vsGrok 4.7Qwen3.8-Max-0902vsGrok 4.7DeepSeek-V4.1-Flash-1017vsMistral Large 4Qwen3.8-Max-0902vsMistral Large 4Frequently asked questions
DeepSeek-V4.1-Flash-1017 leads Qwen3.8-Max-0902 on 4 of the 4 benchmarks they both report (DeepSWE 1.1, NL2Repo-Bench, Terminal-Bench 4.0, AutomationBench). Qwen3.8-Max-0902 shipped 45 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 Qwen3.8-Max-0902 is closed.
On DeepSWE 1.1, DeepSeek-V4.1-Flash-1017 leads at 77% vs Qwen3.8-Max-0902 at 69.3%. On NL2Repo-Bench, DeepSeek-V4.1-Flash-1017 leads at 68.2% vs Qwen3.8-Max-0902 at 64.9%. On Terminal-Bench 4.0, DeepSeek-V4.1-Flash-1017 leads at 42.3% vs Qwen3.8-Max-0902 at 26.97%. On AutomationBench, DeepSeek-V4.1-Flash-1017 leads at 60.5% vs Qwen3.8-Max-0902 at 50.8%.