DeepSeek-V4.1-Flash overview and key facts (October 2026)

DeepSeek-V4.1-Flash is an open-weight multimodal mixture-of-experts model from DeepSeek, built for long contexts at lower memory cost and released under the MIT license.

The model has 552 billion backbone parameters and uses a causal encoder-decoder design that shrinks the key-value cache per token, which DeepSeek reports as about four times smaller than DeepSeek-V4-Flash. It was trained from scratch on 45 trillion multimodal tokens, handles images and text and supports one million tokens of context. It is also the model behind the deepseek-flash API. It had more than 660,000 downloads on Hugging Face in the last month at our check.

• Processes very long documents in houseOne million tokens of context with a smaller cache make long inputs cheaper to serve. Companies can analyze large archives without a cloud API.
• Understands scanned and visual materialNative image input covers screenshots, forms and charts. Back-office teams extract data from mixed documents.
• Powers internal agentsPost-training on synthesized agent tasks targets tool use. Developers build agents that run inside their own network.

• MIT license, one of the most permissive licenses among large open models.
• Architecture focused on reducing memory needs for long contexts.

DeepSeek is based in Hangzhou, China.

Self-hosting means your team runs, secures and updates the model, including GPU capacity, monitoring and patching. Check the license terms for commercial use and keep a record of which version you deploy. If outputs feed decisions about people, a human must stay in the loop. A model of this size needs a multi-GPU server; plan capacity before committing.

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