DeepSeek: DeepSeek V4.1 Flash 1017.
Minor update to DeepSeek-V4.1-Flash with improved reasoning, coding, agent and vision results and shorter outputs, open weights under the MIT licence.
Cheapest hosted endpoints.
Variants in the DeepSeek family.
DeepSeek's flagship MoE — 671B total, 37B active, frontier-class.
DeepSeek's reasoning model — RL-trained, frontier-class, MIT-licensed.
70B Llama distilled from DeepSeek R1's reasoning traces.
32B Qwen base distilled from DeepSeek R1.
14B distilled R1 — laptop-friendly reasoning.
7B distilled R1 — runs on any modern GPU.
Tiny distilled R1 — phone / browser deployable.
Official release of DeepSeek-V4-Pro that supersedes the preview, adding a spe...
DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, wi...
DeepSeek-V3.1 is post-trained on the top of DeepSeek-V3.1-Base, which is buil...
DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iterat...
DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-cha...
DeepSeek-V3.2 is a large language model designed to harmonize high computatio...
DeepSeek-V3.2-Speciale is a high-compute variant of DeepSeek-V3.2 optimized f...
Open-weights 284B Mixture-of-Experts text model with 13B active parameters an...
Open-weights 1.6T Mixture-of-Experts text model with 49B active parameters an...
May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performa...
Open-weights multimodal 552B Mixture-of-Experts model that reads images and t...
DeepSeek-V4-Flash-Vision-Exp is an experimental MIT-licensed model adding ima...
DeepSeek-V3.2-Exp is an experimental large language model released by DeepSee...
Frequently asked.
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What it costs per month across providers.
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Total monthly cost — input + output tokens combined.
Bill breakdown.
About DeepSeek: DeepSeek V4.1 Flash 1017.
A minor update to DeepSeek-V4.1-Flash, the same multimodal model that reads images and text and writes text, with weights under the MIT licence and a one million token context. DeepSeek says the update improves reasoning, coding, agent tasks and visual understanding, and also shortens task execution time and output length. It keeps the base model's architecture and download format, so existing serving setups for V4.1-Flash apply.