Qwen: Qwen3 32B

Qwen: Qwen3 32B is a text model from Qwen, part of the Qwen3 series. It is a dense causal language model with 32.8 billion parameters, released by the provider on 2025-04-28. The model is built for both complex reasoning and efficient dialogue, and it supports switching between a thinking mode and a non-thinking mode. Its knowledge cutoff is 2025-03-31.

The model accepts text input and produces text output, so it is a text-to-text system with no image or audio handling. It offers a context window of 131,072 tokens, which allows long documents, extended conversations or large code files to be processed in a single request. The maximum output is 16,384 tokens, so responses can be substantial but are bounded. Because it is designed for reasoning as well as dialogue, it can work through multi-step problems before producing an answer, and the reasoning behaviour can be controlled through request parameters.

These capabilities suit a range of developer tasks. Long-context analysis of contracts, research papers or technical documentation is practical given the 131,072 token window. The reasoning mode helps with tasks such as mathematical problem solving, logic puzzles, code generation and debugging, or planning steps in an agent workflow. The dialogue mode is a better fit for chat assistants, summarisation, classification and extraction where latency and verbosity matter. Tool calling and structured outputs make it usable in pipelines that need the model to call functions or return JSON-shaped data rather than free text.

You can call the model through api14's OpenAI-compatible API using the model id "qwen/qwen3-32b". The supported request parameters 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_a, top_k, top_logprobs and top_p. The reasoning and include_reasoning parameters relate to the model's thinking mode, while tools and tool_choice support function calling, and response_format and structured_outputs support constrained output shapes. Sampling behaviour can be tuned with temperature, top_p, top_k, min_p, top_a, repetition_penalty, frequency_penalty and presence_penalty, and seed, stop, logprobs and top_logprobs give further control over reproducibility and token-level output.

The main practical limits are the text-only input and output and the 16,384 token maximum output, so tasks that need image understanding or very long single responses may fit another model better. The 131,072 token context is large but finite, so extremely long inputs still need chunking. If your workload depends on modalities beyond text, or on outputs longer than the stated maximum, consider a model with those capabilities instead.