DeepSeek V4 Flash
DeepSeek V4 Flash is a text generation model from DeepSeek with a 1,000,000-token context window and configurable reasoning effort.
DeepSeek
DeepInfraLong-context reasoning with adjustable effort
DeepSeek V4 Flash is a large language model developed by DeepSeek and released in April 2026. It is a flagship reasoning model designed for text generation tasks, offering a context window of up to 1,000,000 tokens and a maximum response size of 384,000 tokens. The model is served through Deep Infra and is accessible on MindStudio without requiring separate API credentials.
One of the model's distinguishing features is its configurable reasoning effort, which allows developers to tune how much compute the model applies to a given problem. It also exposes a range of sampling controls including Top P, Top K, Min P, presence penalty, frequency penalty, repetition penalty, and seed, giving users fine-grained control over output behavior. These characteristics make it well-suited for tasks that involve long documents, multi-step reasoning, or applications where output consistency and reproducibility matter.
What DeepSeek V4 Flash supports
Adjustable Reasoning
Exposes a reasoning effort parameter that lets developers control how much compute the model applies to a given prompt, useful for balancing speed and depth.
1M Token Context
Supports a context window of 1,000,000 tokens, enabling processing of very long documents, codebases, or multi-turn conversation histories in a single request.
Large Response Output
Allows responses of up to 384,000 tokens, making it suitable for generating lengthy structured outputs, reports, or extended code files.
Sampling Controls
Provides Top P, Top K, and Min P parameters for fine-grained control over token sampling distributions during generation.
Repetition Management
Includes presence penalty, frequency penalty, and repetition penalty inputs to reduce redundant or looping output in long-form generation tasks.
Reproducible Outputs
Accepts a seed parameter so developers can reproduce the same output for a given input, useful for testing and debugging.
Multi-Step Reasoning
Tagged as a reasoning model, it is designed to handle tasks requiring chained logical steps such as math problems, code analysis, and complex question answering.
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Get Started FreeCommon questions about DeepSeek V4 Flash
What is the context window for DeepSeek V4 Flash?
DeepSeek V4 Flash supports a context window of 1,000,000 tokens, allowing very long inputs such as large documents or extended conversation histories to be processed in a single request.
What is the maximum response length?
The model can generate responses of up to 384,000 tokens in a single call.
What does the reasoning effort parameter do?
The reasoning effort input lets you control how much computational effort the model applies when generating a response. Adjusting it can trade off between response speed and reasoning depth.
Is pricing available for DeepSeek V4 Flash on MindStudio?
Published pricing was not available in the model metadata at the time of listing. Check the MindStudio platform or DeepInfra for current pricing details.
Who developed DeepSeek V4 Flash and when was it released?
DeepSeek V4 Flash was developed by DeepSeek and released in April 2026. It is served on MindStudio via the DeepInfra provider.
Does DeepSeek V4 Flash support image or video inputs?
Based on the available metadata, DeepSeek V4 Flash does not have confirmed support for image or video inputs. It is listed as a text generation model.
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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