Qwen 3 Embedding 0.6B
Qwen develops the Qwen 3 Embedding 0.6B, an open-source embedding model capable of generating text embeddings, particularly suited for tasks such as text retrieval, classification, and clustering. It is genuinely best at handling long-text understanding and multilingual tasks, supporting over 100 languages.
Input
Output
Context
33K
Max Output
-
Parameters
595.8M
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Qwen 3 Embedding 0.6B
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 0.6B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Qwen 3 Embedding 0.6B advertises a context window of 32,768 tokens. 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. Qwen 3 Embedding 0.6B 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 0.6B 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 0.6B 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 0.6B is served on the managed pool through the OpenAI-compatible endpoint as Qwen/Qwen3-Embedding-0.6B, 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 1.0 GB at INT8 for the weights and a default context, from the catalog's 595.8M 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 13, 2026
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