Qwen: Qwen3 235B A22B Instruct 2507

Qwen: Qwen3 235B A22B Instruct 2507 is a text generation model from Qwen. It is an instruction-tuned, multilingual mixture-of-experts model built on the Qwen3 235B architecture, with 22 billion active parameters per forward pass. The provider release date is 21 July 2025, and the model's knowledge cutoff is 30 June 2025.

The model works with text input and produces text output only, so it is aimed at language tasks rather than image or audio work. Its context window is 262,144 tokens, which allows very long prompts, large documents or extended conversations to be handled in a single request. The maximum output is 16,384 tokens per response. As an instruction-tuned model, it is optimised for general-purpose text generation, including instruction following, and its multilingual training makes it suitable for work across more than one language.

These capabilities point to a fairly wide set of practical uses. Long document analysis, summarisation and question answering over large inputs are natural fits given the context window. Because the model follows instructions and handles multilingual text, it also suits chat assistants, content drafting, translation-adjacent workflows and structured data extraction. The combination of tool use and structured output support means it can be placed behind an agent loop or used to produce machine-readable responses for downstream systems.

You can call the model through api14's OpenAI-compatible API using the model id "qwen/qwen3-235b-a22b-2507". Requests support a broad set of parameters: temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty and repetition_penalty for sampling control; max_tokens and stop for output length and termination; seed for reproducibility; and logprobs with top_logprobs for token-level probabilities. For structured work there are response_format and structured_outputs, and for function calling there are tools and tool_choice. logit_bias is also accepted. Any client that speaks the OpenAI chat completions format should work with these fields.

One practical limit to keep in mind is output length: 16,384 tokens per response, even though the input context is much larger, so tasks that need very long single completions may require splitting the work. The model is text-only, so if your input or output involves images or audio, a different model is the better fit. Since the knowledge cutoff is 30 June 2025, anything after that date depends on the prompt or on tools you provide rather than on the model's own knowledge.