Qwen: Qwen3.6 35B A3B

Qwen: Qwen3.6 35B A3B is an open-weight multimodal model from Qwen, Alibaba Cloud's model family. It has 35 billion total parameters with 3 billion active parameters per token, using a hybrid sparse mixture-of-experts architecture. Because only a fraction of the weights are engaged for each token, it is designed to balance capability against inference efficiency, which matters when you plan to serve many requests.

The model accepts text, image and video inputs, and it produces text output only. Its context window is 262,144 tokens, with a maximum output of 65,536 tokens. That combination suits workloads where the input is large but the answer is comparatively short, such as reading a long document, a screenshot set or a video and returning a summary, an extraction or a decision.

Practical uses follow from those properties. Long-context text work is a natural fit: summarising lengthy reports, answering questions over large codebases or transcripts, and multi-document analysis that would otherwise need chunking. The image and video inputs extend this to visual material, so you can describe frames, read charts or diagrams, or extract structured data from recorded sessions. Tool and function calling support makes it viable as the reasoning core of an agent that queries APIs or databases, and structured outputs let you constrain responses to a schema your application can parse directly. Reasoning output can be requested and inspected separately when you want to see the model's intermediate steps.

You call it through api14's OpenAI-compatible API using the model id "qwen/qwen3.6-35b-a3b". The request parameters it supports are frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs and top_p. Since the endpoint follows the OpenAI schema, existing clients and SDKs generally work with only the base URL and model id changed, and tools plus tool_choice let you wire in function calling without a separate integration path.

Keep the output ceiling in mind: 65,536 tokens is generous for most answers but not unlimited, so tasks that must emit very long documents may need to be split across calls. The context window is large but still finite, so extremely long inputs will need truncation or retrieval. If your workload is text only and latency or throughput is the dominant concern, a smaller dense model may serve you better; if you need audio input or image generation as output, you will need a different model, since this one covers text, image and video in and text out.