Qwen: Qwen3.5 Plus 2026-02-15

Qwen: Qwen3.5 Plus 2026-02-15 is a text generation model from Qwen, released under the model id qwen/qwen3.5-plus-02-15. It belongs to the Qwen3.5 native vision-language series of Plus models, which Qwen describes as built on a hybrid architecture that combines linear attention mechanisms with sparse mixture-of-experts models to achieve higher inference efficiency. It is a multimodal input model in the text category, so it accepts more than text while producing text as its only output.

On the input side, the model accepts text, images and video, and it returns text. Its context window is 1,000,000 tokens, which is large enough to hold very long documents, long conversations or extended media transcripts in a single request. The maximum output is 65,536 tokens, so responses can be long, but they are bounded well below the input capacity. Because images and video are accepted as input, the model can work over visual material directly rather than relying on a separate captioning step.

Those capabilities point to a few practical uses. Long-context text work such as summarising large document sets, searching across a big corpus, or maintaining a long running assistant conversation fits the 1,000,000 token window. Mixed-media tasks fit the input modalities: describing or extracting information from images, reviewing video content, or answering questions grounded in visual material alongside text. The tool and structured output parameters also make it a reasonable choice for pipelines where the model calls functions or returns JSON-shaped results, and the reasoning parameters suit tasks where you want the model's reasoning surfaced or controlled.

You call it through api14's OpenAI-compatible API using the model id qwen/qwen3.5-plus-02-15. 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. In practice that means you can tune sampling with temperature, top_p and top_k, control repetition with the penalty parameters, cap length with max_tokens, make runs reproducible with seed, constrain output with response_format and structured_outputs, and wire up function calling with tools and tool_choice. Log probability options are available through logprobs and top_logprobs, and reasoning behaviour can be requested or inspected via reasoning and include_reasoning.

The main limits to keep in mind are the output ceiling of 65,536 tokens and the fact that the model outputs text only, so it cannot generate images or video. If your task is purely text and does not need the vision-language input path, a text-only model may be a simpler fit, and if you need generated media rather than text, this model is not the right choice.