DeepSeek: DeepSeek V4 Flash Latest

DeepSeek V4 Flash Latest is a text model from DeepSeek, distributed under the model id ~deepseek/deepseek-v4-flash-latest. The "latest" suffix matters: this entry always redirects to the newest model in the DeepSeek V4 Flash family, so requests sent to this id track the current member of that family rather than a frozen snapshot. If you need fully reproducible behaviour across time, that redirect behaviour is worth keeping in mind when you pin a model in production.

The model is text in, text out. It accepts text prompts and returns text completions, with no image, audio or video input. Its context window is 1,048,576 tokens, and the maximum output is 943,718 tokens. That combination of a very large input window and a very large output ceiling is the main structural fact about the model: it can take in long documents and produce long responses in a single call, rather than forcing you to chunk input or stitch together many short completions.

Those capabilities point to a fairly specific set of use cases. Long-context work is the obvious fit: analysing or summarising lengthy documents, contracts, transcripts or codebases that would not fit in a smaller window, and doing so in one pass. The large output ceiling also suits tasks where the answer itself is long, such as generating extended reports, drafting large files, or transforming a long input into an equally long structured output. Because the model supports tools and structured outputs, it also fits agent-style and extraction workflows where the model must call functions or return JSON that conforms to a schema.

You call it through api14's OpenAI-compatible API using the model id ~deepseek/deepseek-v4-flash-latest, which means existing OpenAI SDK clients and tooling generally work with only a base URL and model name change. The request parameters it supports include temperature, top_p, top_k, min_p, top_a, frequency_penalty, presence_penalty, repetition_penalty, max_tokens, stop, seed, logit_bias, logprobs, top_logprobs, response_format, structured_outputs, tools, tool_choice, parallel_tool_calls, reasoning, reasoning_effort and include_reasoning. Reasoning controls are exposed separately from the sampling parameters, so you can tune how much internal reasoning the model does and whether that reasoning is returned, independently of how you shape the final answer.

On limits: the model handles text only, so anything involving images or audio needs a different model. The redirect behaviour of the "latest" id is also a consideration if you require a stable, unchanging model version over time. Beyond those points, the facts here do not indicate other restrictions, so the main decision is simply whether a text-only model with a very large context and output budget matches your workload.