Qwen 3 VL Embedding 2B
Qwen develops the Qwen 3 VL Embedding 2B, an open-source embedding model that excels at generating high-dimensional vectors for multimodal information retrieval and cross-modal understanding tasks. It is particularly adept at handling diverse inputs, including text, images, and videos, and producing semantically rich vectors that facilitate efficient similarity computation and retrieval.
Input
Output
Context
32K
Max Output
-
Parameters
2B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Qwen 3 VL Embedding 2B
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 VL Embedding 2B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Qwen 3 VL Embedding 2B advertises a context window of 32,000 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 VL Embedding 2B 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 VL Embedding 2B accepts Text and Image 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 VL Embedding 2B 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.
Not on the managed pool today; Qwen 3 VL Embedding 2B 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 2.5 GB at INT8 for the weights and a default context, from the catalog's 2B 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: Aug 28, 2026
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