Qwen: Qwen3.5-Flash

Qwen3.5-Flash is a text generation model from Qwen, released under the model id qwen/qwen3.5-flash-02-23. It belongs to the Qwen3.5 native vision-language Flash family, which Qwen describes as built on a hybrid architecture combining a linear attention mechanism with a sparse mixture-of-experts model, aimed at higher inference efficiency. It sits in the text category on api14, and the provider release date is 2026-02-25.

The model accepts text, images and video as input, and it produces text as output. Its context window is 1,000,000 tokens, and the maximum output is 65,536 tokens. Because the input side is multimodal, you can send prompts that mix written instructions with visual material, including still images and video, and receive a single text response. The large context window means long documents, extended transcripts or lengthy media-heavy prompts can be handled in one request rather than being split across many calls.

Those capabilities point to a fairly wide set of practical uses. Long-context text work such as summarising large documents, answering questions over lengthy source material or extracting structured data from reports fits naturally within the million-token window. The vision and video inputs extend this to tasks like describing or reasoning about images, reviewing footage, pulling information out of screenshots, or combining visual evidence with written instructions in a single prompt. The reasoning and structured output support also makes it suitable for workflows where the model needs to work through a problem before returning an answer, or where the response must conform to a schema your code can parse.

You call the model through api14's OpenAI-compatible API using the model id "qwen/qwen3.5-flash-02-23". The supported request parameters are frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k and top_p. Tool calling is available through tools and tool_choice, structured responses through response_format and structured_outputs, and reasoning behaviour can be controlled with reasoning and include_reasoning. Sampling can be tuned with temperature, top_p, top_k, frequency_penalty, presence_penalty and seed, while max_tokens and stop control the length and termination of the output.

Keep the 65,536 token output cap in mind when planning responses: a request can carry a very large input, but the generated answer is bounded. If your task depends on output modalities other than text, this model does not provide them, since text is its only output type. Within those bounds, it is a reasonable choice for multimodal input, long-context and tool-using text workloads.