DeepSeek: DeepSeek V4.1 Flash (batch)

DeepSeek V4.1 Flash (batch) is a text generation model from DeepSeek, listed on api14 under the model id deepseek/deepseek-v4.1-flash:batch. It is a sparse mixture-of-experts model and the first from the company built on its Causal Encoder-Decoder architecture. The provider release date is 2026-09-10. The batch label marks this variant as the batch processing option for the Flash model.

The model accepts text and image input and returns text output, so it can handle both language prompts and prompts that include images. It has a context window of 1,048,576 tokens, which is large enough to hold very long documents, codebases or long conversation histories in a single request. The maximum output length is 131,072 tokens, so responses can be long as well. Supported request parameters include temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty, repetition_penalty, logit_bias, seed, stop, max_tokens, response_format, structured_outputs, tool_choice, tools, reasoning, reasoning_effort and include_reasoning.

Because it reads both text and images and can produce long outputs, the model suits tasks such as summarising long reports or contracts, answering questions over large document sets, extracting structured data from mixed text and image inputs, and generating long form drafts. The tool calling parameters (tools and tool_choice) make it usable as the reasoning engine in an agent or workflow that needs to call external functions. Structured outputs and response_format help when you need the reply to follow a fixed schema, and the reasoning parameters let you control how much internal reasoning the model performs before answering.

You call the model through api14's OpenAI-compatible API using the model id deepseek/deepseek-v4.1-flash:batch, in the same way you would call any OpenAI-style chat completions endpoint. Set the model field to that id and send your messages, including image content where needed. The request supports the parameters listed above, so you can set temperature, top_p, top_k, min_p, the penalty parameters, logit_bias, seed, stop, max_tokens, response_format, structured_outputs, tools, tool_choice, reasoning, reasoning_effort and include_reasoning directly in the request body.

The batch variant is the one to pick when your workload can be processed in batch rather than interactively. If you need a different trade-off between latency and capability, or a model that outputs modalities other than text, another model on api14 may fit better. The facts here cover only text output, so tasks that require image, audio or video generation fall outside what this model provides.