DeepSeek V4.1 Flash is a DeepSeek model for long-context work, programming and tasks that require several connected reasoning steps. It uses a Mixture of Experts architecture with 552 billion parameters in the main block and an asymmetric causal encoder-decoder. It activates 8 billion parameters when processing input and 16 billion when generating a response. This design separates reading the material from producing the result; the developer is specifically developing it for tasks with large volumes of input data.
Start with the result you need, such as “find the discrepancies and propose a unified version of the requirements.” Then add the material, comparison criteria and desired response structure. For code, provide the relevant section, the observed behavior and the constraints that must be preserved. You can request a cause explanation, a proposed patch and a verification method as separate parts, making the result easier to use in subsequent work.
The model supports reasoning tasks and agentic workflows within its capabilities. In an ordinary text request, a useful result is a plan, analysis or code; the application you are working in handles program execution and the application of changes.
This Ailoot API listing accepts text input and returns text. A request can include instructions, conversation history, document excerpts and source code. You can ask for an explanation, a list, a comparison table or a finished passage of text.
The stated context window is up to 1,048,576 tokens. This is the size of the working context, not a character count: it must accommodate the request itself, the supplied material and the conversation history. For a large document collection, specify source priorities and concrete questions, and describe the final format in advance. This helps focus the analysis on the relevant material and produces a result that can be checked against the source text.
", "aboutText": "DeepSeek V4.1 Flash is a DeepSeek model for long-context work, programming and tasks that require several connected reasoning steps. It uses a Mixture of Experts architecture with 552 billion parameters in the main block and an asymmetric causal encoder-decoder. It activates 8 billion parameters when processing input and 16 billion when generating a response. This design separates reading the material from producing the result; the developer is specifically developing it for tasks with large volumes of input data.\n\n### What you can use it for\n\n- Analyze a technical description, correspondence or a set of documents and produce a coherent conclusion addressing specific questions.\n- Explain code, propose a change, devise test scenarios and compare implementation options.\n- Turn a detailed brief into a sequence of actions with intermediate results.\n- Compare requirements from several text sources, identify contradictions and assemble a working specification.\n\n### How to structure a request\n\nStart with the result you need, such as “find the discrepancies and propose a unified version of the requirements.” Then add the material, comparison criteria and desired response structure. For code, provide the relevant section, the observed behavior and the constraints that must be preserved. You can request a cause explanation, a proposed patch and a verification method as separate parts, making the result easier to use in subsequent work.\n\nThe model supports reasoning tasks and agentic workflows within its capabilities. In an ordinary text request, a useful result is a plan, analysis or code; the application you are working in handles program execution and the application of changes.\n\n### Input, output and long documents\n\nThis Ailoot API listing accepts text input and returns text. A request can include instructions, conversation history, document excerpts and source code. You can ask for an explanation, a list, a comparison table or a finished passage of text.\n\nThe stated context window is up to 1,048,576 tokens. This is the size of the working context, not a character count: it must accommodate the request itself, the supplied material and the conversation history. For a large document collection, specify source priorities and concrete questions, and describe the final format in advance. This helps focus the analysis on the relevant material and produces a result that can be checked against the source text.", "locale": "en", "methods": [ { "html": "POST /v1/messagestexttextPOST /v1/responsestexttextPOST /v1/chat/completionstexttextPOST /v1beta/models/deepseek/deepseek-v4.1-flash:generateContenttexttext