QwenQwen 3 Embedding 4B
Qwen develops the Qwen 3 Embedding 4B, an open-source embedding model exceling at text embedding and ranking tasks, including text retrieval, code retrieval, and text classification. Its notable technical trait is a long context window of 40,960 tokens, allowing for comprehensive understanding of lengthy texts.
Input
Output
Context
33K
Max Output
n/a
Parameters
4B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Qwen 3 Embedding 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 Embedding 4B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Qwen 3 Embedding 4B advertises a context window of 32,768 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 Embedding 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 Embedding 4B accepts Text and produces Embedding. 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.
No published benchmark result for Qwen 3 Embedding 4B is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Yes. Qwen 3 Embedding 4B is served on the managed pool through the OpenAI-compatible endpoint as qwen3-embedding-4b, pinned by name or chosen by routing when it is the best fit for a request. The Try in Playground button opens it directly.
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 15, 2026
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