Qwen 3 Embedding 8B
Qwen develops the Qwen 3 Embedding 8B, an open-source embedding model that excels at text embedding and ranking tasks, including text retrieval, code retrieval, and text classification. Its exceptional multilingual capabilities, supporting over 100 languages, make it a versatile tool for a wide range of applications.
Input
Output
Context
33K
Max Output
-
Parameters
7.6B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Resources & Links
Estimated GPU requirements for Qwen 3 Embedding 8B
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.
More models by Qwen
The creator's other models in the catalog, with their context, size and license where published.
Compare it, or keep browsing
Put this model beside its alternatives on the same evidence, or go back to the full catalog.
Frequently asked questions about Qwen 3 Embedding 8B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Qwen 3 Embedding 8B 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 8B 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 8B 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 8B 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 8B is served on the managed pool through the OpenAI-compatible endpoint as Qwen/Qwen3-Embedding-8B, 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 7.8 GB at INT8 for the weights and a default context, from the catalog's 7.6B 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
Put intelligence in the middle.
One gateway in front of every model, with your policies applied and every decision on record. Start with $5 of credit and 5,000 routing decisions a month, no card required.