DeepSeek: DeepSeek V4.1 Flash

DeepSeek V4.1 Flash is a text generation model from DeepSeek, released by the provider on 10 September 2026. It is a sparse mixture-of-experts model and the first to be built on DeepSeek's Causal Encoder-Decoder (CED) architecture. The model activates 8B parameters on input and 16B on output, which is the level of detail the provider gives about its internal design. It sits in the text category and is identified on api14 as deepseek/deepseek-v4.1-flash.

The model accepts text and image input and returns text output. Its context window is 1,048,576 tokens, and the maximum output length is 393,216 tokens. That combination of a very large context and a large output budget means you can send long documents, codebases or image sets in a single request and still receive a substantial response. Because input can include images, the model can handle tasks that mix written material with screenshots, diagrams or scanned pages, as long as the output you need is text.

Practical uses follow from those properties. Long-context work such as summarising or querying large document collections, reviewing entire repositories, and extracting structured data from mixed text and image inputs are all reasonable fits. The large output ceiling supports tasks that produce long results, for example generating extended reports, transcribing or reformatting large bodies of content, or drafting multi-section documents in one call. Image input extends this to workflows like reading charts alongside their surrounding commentary, or pulling fields out of forms and screenshots into a text format.

You call the model through api14's OpenAI-compatible API using the model id deepseek/deepseek-v4.1-flash, so existing OpenAI client libraries work with only a base URL and key change. The model supports a broad set of request parameters: frequency_penalty, presence_penalty, repetition_penalty, temperature, top_p, top_k, min_p, seed, stop, max_tokens, logit_bias, logprobs and top_logprobs. For structured work it supports response_format and structured_outputs, and for tool use it supports tools and tool_choice. Reasoning behaviour is configurable through reasoning, reasoning_effort and include_reasoning.

There are no stated restrictions beyond the modality and length facts above, so the main practical question is whether your task needs image input, very long context or very long output. If it needs none of those, a smaller or faster model may be simpler to work with. If it needs audio or video input, or non-text output, this model does not provide it.