Qwen: Qwen3.5 Plus 2026-04-20

Qwen: Qwen3.5 Plus 2026-04-20 is a large-scale multimodal language model from Qwen (Alibaba). It belongs to the Qwen3.5 Plus line, and the model id carries the snapshot date 2026-04-20, which identifies this specific version of the model on api14. It is a text-generation model at its core, but it is built to take in more than text, which places it in the multimodal tier of the Qwen family rather than the text-only tier.

The model accepts text, image, and video as input and produces text as output. Its context window is 1,000,000 tokens, which is large enough to hold very long documents, long transcripts, or extended multi-turn conversations without trimming. The maximum output is 65,536 tokens, so a single response can be substantial, though it is far smaller than the input window. Supported request parameters include temperature, top_p, top_k, frequency_penalty, presence_penalty, max_tokens, stop, seed, logprobs, top_logprobs, response_format, structured_outputs, tools, tool_choice, reasoning, and include_reasoning. The presence of tools and tool_choice means the model can be used in function-calling setups, and structured_outputs and response_format let you constrain the shape of the returned text.

Those capabilities point to a few practical uses. Because it reads images and video as well as text, it fits tasks such as describing or summarizing video content, answering questions about frames or screenshots, and extracting text or structured data from visual material. The 1M token context suits long-document analysis, codebase review, and conversations that accumulate a lot of history. Tool calling makes it a candidate for agent-style workflows where the model decides when to call an external function, and structured outputs help when the result must be parsed by another program.

You call it through api14's OpenAI-compatible API using the model id "qwen/qwen3.5-plus-20260420". Because the endpoint follows the OpenAI chat completions shape, you send messages with roles and content, and you can pass any of the supported parameters listed above in the request body. Reasoning-related fields (reasoning and include_reasoning) are available if you want the model's reasoning surfaced or controlled, and sampling controls such as temperature, top_p, and top_k let you tune output variability. Set max_tokens within the 65,536 output ceiling, and use stop, seed, or response_format where you need deterministic or constrained behavior.

One limit worth noting is that output is text only, so if you need generated images, audio, or video, this model is not the right fit. The 65,536 token output cap also means very long generations must be split across calls. For purely text tasks with modest context, a smaller text-only model may be cheaper and faster, but when the input includes images or video, or the context runs into hundreds of thousands of tokens, this model's combination of modalities and window size is the reason to choose it.