CohereAya Vision 32B
Cohere's Aya Vision 32B is a 32-billion parameter, open-source chat model that accepts both text and image inputs and is optimized for various vision-language use cases, including OCR, captioning, and visual reasoning, with capabilities in 23 languages. It is particularly suited for tasks that require understanding and generating text based on visual information.
Input
Output
Context
16K
Max Output
4K
Parameters
33.1B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Aya Vision 32B
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 Aya Vision 32B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Aya Vision 32B advertises a context window of 16,384 tokens, with a maximum output of 4,000 tokens in a single response. 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. Aya Vision 32B is an open-weight model released under the Cc by nc 4.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Aya Vision 32B 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 Aya Vision 32B 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; Aya Vision 32B 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 32.5 GB at INT8 for the weights and a default context, from the catalog's 33.1B 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 14, 2026
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