DeepSeek: DeepSeek V4 Flash 0731

DeepSeek V4 Flash 0731 is a text model from DeepSeek, released by the provider on 31 July 2026. It is a sparse mixture-of-experts model with 13B active parameters out of 284B total, and this revision is a re-post-trained build. The provider positions it for coding, reasoning, and agent workflows, which tells you where it is meant to sit in a stack: general text generation with an emphasis on tasks that involve multi-step thinking and tool use rather than pure chat.

On the capability side, the model takes text in and returns text out, so there is no image, audio, or file input to plan around. The context window is 1,048,576 tokens, and the maximum output is 943,718 tokens. That combination is unusual in the sense that the output ceiling is close to the input ceiling, so long generations are possible without immediately hitting a hard output cap. The model also supports a reasoning mode, exposed through the reasoning and reasoning_effort parameters, plus include_reasoning if you want the reasoning content surfaced in the response. Tool calling is supported, including parallel_tool_calls and tool_choice, and structured outputs and response_format are available for constraining the shape of the reply.

Those capabilities point to a few practical uses. Large codebases or long document sets fit inside the context window, so repository-level code review, refactoring suggestions, and cross-file question answering are reasonable fits. Agent workflows benefit from tool calling and parallel tool calls, which let the model issue several calls in one turn instead of serialising them. Reasoning effort control is useful when you want to trade latency for more deliberate problem solving on maths, logic, or planning tasks. Structured outputs make it suitable for extraction and classification jobs where the downstream system expects a fixed schema, and the logprobs and top_logprobs parameters support scoring and confidence analysis.

You call it through api14's OpenAI-compatible API using the model id "deepseek/deepseek-v4-flash-0731". Because the endpoint follows the OpenAI chat completions shape, existing OpenAI SDKs and clients work with a base URL change and your api14 key. The request parameters it accepts are frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, parallel_tool_calls, presence_penalty, reasoning, reasoning_effort, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_a, top_k, top_logprobs, and top_p. Sampling controls such as temperature, top_p, top_k, min_p, and top_a are all present, so you can tune determinism fairly precisely, and seed plus logit_bias help with reproducibility and token-level steering.

The main constraint to keep in mind is modality. Input and output are text only, so if your task depends on images or audio, this model is not the right choice and you should look at a multimodal model instead. The very large context window is a strength, but it does not remove the need to manage what you put in it; retrieval and trimming still matter for cost and latency, even though this page does not quote prices. If you need a model with vision or audio support, pick something else. If your work is text, code, reasoning, or tool-driven agents, the parameter set here covers most of what you would reach for.