QwenQwen 3 4B
Qwen 3 4B is a chat model developed by Qwen, offering capabilities such as function calling, JSON mode, reasoning, streaming, and text generation. It is genuinely best at delivering a comprehensive suite of dense and mixture-of-experts models, with significant enhancements in reasoning capabilities and superior human preference alignment.
Input
Output
Context
41K
Max Output
-
Parameters
4B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Benchmarks
Resources & Links
Estimated GPU requirements for Qwen 3 4B
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 Qwen 3 4B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Qwen 3 4B advertises a context window of 40,960 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. Qwen 3 4B 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.
Qwen 3 4B 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.
Qwen 3 4B 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 Qwen 3 4B is shown as not available rather than estimated.
Not on the managed pool today; Qwen 3 4B 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 4.3 GB at INT8 for the weights and a default context, from the catalog's 4B 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, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Oct 8, 2026
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