Kimi VL A3B Thinking 2506
Developed by Moonshot AI, Kimi VL A 3B Thinking 2506 is a chat model that excels at multimodal reasoning and visual perception tasks, achieving notable scores such as 84.4 on MMBench-EN-v1.1 and 78.4 on MMVet. With a context window of 131,072 tokens and the ability to process both text and image inputs, this model showcases its capability in handling complex and high-resolution data.
Input
Output
Context
131K
Max Output
32K
Parameters
16.4B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Kimi VL A3B Thinking 2506
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 Kimi VL A3B Thinking 2506
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Kimi VL A3B Thinking 2506 advertises a context window of 131,072 tokens, with a maximum output of 32,000 tokens in a single response. 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. Kimi VL A3B Thinking 2506 is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
Kimi VL A3B Thinking 2506 accepts Text and Image 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.
No published benchmark result for Kimi VL A3B Thinking 2506 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; Kimi VL A3B Thinking 2506 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 16.4 GB at INT8 for the weights and a default context, from the catalog's 16.4B 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, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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