QwQ 32B
Qwen QwQ-32B is a chat model developed by Qwen, capable of handling a wide range of tasks including streaming, code execution, and function calling. It is genuinely best at extended thinking and generating structured output, making it a versatile tool for various applications.
Input
Output
Context
131K
Max Output
33K
Parameters
32.8B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Benchmarks
Resources & Links
Estimated GPU requirements for QwQ 32B
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about QwQ 32B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
QwQ 32B advertises a context window of 131,072 tokens, with a maximum output of 32,768 tokens in a single response. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. QwQ 32B is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
QwQ 32B accepts Text and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
QwQ 32B has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured QwQ 32B is shown as not available rather than estimated.
Not on the managed pool today; QwQ 32B is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 32.2 GB at INT8 for the weights and a default context, from the catalog's 32.8B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Sep 22, 2026
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