Qwen: Qwen3.8 Omni Flash

Qwen3.8 Omni Flash is an omni-modal reasoning model from Qwen, the model family produced by Alibaba. It is described as the first Qwen model built around agentic capabilities with native audio-video understanding. The model id on api14 is qwen/qwen3.8-omni-flash, and its main category is text.

The model accepts text, image, audio and video as input, and it returns text only. Its context window is 1,000,000 tokens, and the maximum output is 131,072 tokens. Because it is a reasoning model, it can produce reasoning output alongside its final answer, which you can control through the relevant request parameters. The combination of a very large context and four input modalities means you can send long documents, images, audio and video in the same request rather than splitting them across separate pipelines.

The capabilities point to a fairly specific set of use cases. Audio-video analysis and summarization are the clearest fits, since the model understands audio and video natively rather than through a transcription step. From there, you can build agents that watch or listen to media and act on what they find, summarization tools for recorded meetings or media archives, and question answering over mixed inputs where a user supplies a clip, a screenshot and a written question together. The large context window also suits long-form analysis where the source material is lengthy, and the reasoning output helps when you need to inspect how the model reached an answer.

You call the model through api14's OpenAI-compatible API using the model id qwen/qwen3.8-omni-flash, so existing OpenAI client libraries work with a change of base URL and model name. The supported request parameters are frequency_penalty, include_reasoning, logprobs, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs and top_p. Tool calling and structured outputs are both available, which matters if you are building an agent loop or need the model to return data in a fixed shape. You can also set a seed for more reproducible sampling and use logprobs or top_logprobs when you need token-level confidence information.

One practical limit to keep in mind is that output is text only. If your application needs generated images, audio or video, this model will not produce them, and you would pair it with a generation model instead. The 131,072 token output ceiling is generous but finite, so very long generated documents may need to be produced in stages. Within those bounds, it is a reasonable choice when your inputs are multimodal and your outputs are text.