Compare AI models
| Specifications | ||
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. | 1M | 400k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $0.05 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $0.40 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.005 |
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. | $0.05Relace | $0.025OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.2352DeepInfra | $0.20OpenAI |
| $34.30DeepInfra · fp8 | $30.00OpenAI list price | |
| 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 | OpenAI |
| Release date | Oct 17 2026 | Aug 7 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek-V4.1-Flash-1017vsClaude Haiku 5.5GPT-5 nanovsClaude Haiku 5.5DeepSeek-V4.1-Flash-1017vsGemini 4 ArgonGPT-5 nanovsGemini 4 ArgonDeepSeek-V4.1-Flash-1017vsMuse Spark OpenGPT-5 nanovsMuse Spark OpenDeepSeek-V4.1-Flash-1017vsGrok 4.7GPT-5 nanovsGrok 4.7DeepSeek-V4.1-Flash-1017vsMistral Large 4GPT-5 nanovsMistral Large 4DeepSeek-V4.1-Flash-1017vsKimi K3GPT-5 nanovsKimi K3Frequently asked questions
DeepSeek-V4.1-Flash-1017 and GPT-5 nano don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-5 nano has a verified first-party API price: $0.05 per million input tokens and $0.40 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek-V4.1-Flash-1017. GPT-5 nano shipped 436 days before DeepSeek-V4.1-Flash-1017, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (DeepSeek-V4.1-Flash-1017) vs 400k (GPT-5 nano). DeepSeek-V4.1-Flash-1017 is open weight, while GPT-5 nano is closed.
Direct benchmark comparisons are unavailable — DeepSeek-V4.1-Flash-1017 and GPT-5 nano don't publish scores on any of the same benchmarks.