DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 is a text generation model from DeepSeek with a 1,048,576-token context window and configurable reasoning effort.
DeepSeek
DeepInfraLarge-context reasoning with adjustable effort
DeepSeek V4 Flash 0731 is a chat-oriented large language model developed by DeepSeek and served through DeepInfra. It belongs to the DeepSeek V4 Flash line and carries the FLAGSHIP and LATEST tags, indicating it represents DeepSeek's current generation of models at the time of its August 2026 release. The model supports a context window of up to 1,048,576 tokens and a maximum response size of 384,000 tokens, making it suited for tasks that require processing or generating large volumes of text in a single session.
A distinguishing feature of DeepSeek V4 Flash 0731 is its configurable reasoning effort, which allows developers to tune how much computational reasoning the model applies to a given prompt. Alongside reasoning effort, the model exposes sampling controls including Top P, Top K, Min P, presence penalty, frequency penalty, repetition penalty, and a seed parameter for reproducibility. These controls make it well-suited for applications that require fine-grained output behavior, such as long-document summarization, multi-step reasoning tasks, and structured text generation workflows.
What DeepSeek V4 Flash 0731 supports
Configurable Reasoning
Exposes a reasoning effort selector that lets developers adjust how much reasoning the model applies per request, enabling a trade-off between depth and speed.
Extended Context Window
Supports up to 1,048,576 tokens of context, allowing entire large documents or long conversation histories to be processed in a single call.
Large Response Output
Can generate responses of up to 384,000 tokens, making it suitable for producing long-form content such as detailed reports or extended code files.
Sampling Parameter Control
Provides Top P, Top K, and Min P sampling inputs alongside presence, frequency, and repetition penalties for precise control over output diversity and repetition.
Reproducible Outputs
Includes a seed parameter that enables deterministic generation, allowing developers to reproduce the same output given identical inputs and settings.
Text Generation
Generates coherent, multi-turn chat responses as a chat-completion model, supporting dialogue, summarization, and instruction-following tasks.
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Get Started FreeCommon questions about DeepSeek V4 Flash 0731
What is the context window size for DeepSeek V4 Flash 0731?
The model supports a context window of 1,048,576 tokens, which allows very large documents or long conversation histories to be included in a single request.
What is the maximum response length this model can generate?
DeepSeek V4 Flash 0731 can generate responses of up to 384,000 tokens in a single call.
What does the reasoning effort setting do?
The reasoning effort input is a selectable parameter that controls how much reasoning the model applies when generating a response. Adjusting it lets you balance response depth against latency depending on your use case.
Is pricing information available for this model?
No published pricing is listed in the current metadata. You should check DeepInfra's pricing page directly, as the model is served through DeepInfra.
Does DeepSeek V4 Flash 0731 support image or video inputs?
Based on the available metadata, image and video analysis support are not indicated for this model. It is listed as a text generation model accepting text inputs only.
When was DeepSeek V4 Flash 0731 released?
The model has a listed release date of August 2026 and was added to MindStudio on August 3, 2026.
Documentation & links
Parameters & options
Non-think for fast responses, High for complex problem-solving, Max to push reasoning to its fullest extent.
Nucleus sampling. Considers only tokens whose cumulative probability exceeds this threshold.
Limits sampling to the K most likely tokens at each step. Set to 0 to disable.
Minimum probability threshold relative to the most likely token.
Penalizes tokens that have already appeared in the output, encouraging new topics.
Penalizes tokens based on how often they have already appeared.
Penalizes repeated tokens. Values above 1 discourage repetition.
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