Microsoft's Phi 3 Vision 128K Instruct is a chat model that excels at instruction adherence and safety, thanks to its rigorous enhancement process incorporating supervised fine-tuning and direct preference optimization. It is notable for its large context window of 131,072 tokens, allowing it to process and respond to lengthy inputs.
Input
Output
Context
131K
Max Output
-
Parameters
4.1B
Input Modalities
Output Modalities
Loading capabilities…
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.
The creator's other models in the catalog, with their context, size and license where published.
Put this model beside its alternatives on the same evidence, or go back to the full catalog.
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Phi 3 Vision 128K Instruct advertises a context window of 131,072 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. Phi 3 Vision 128K Instruct is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
Phi 3 Vision 128K Instruct 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 Phi 3 Vision 128K Instruct 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; Phi 3 Vision 128K Instruct 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 4.4 GB at INT8 for the weights and a default context, from the catalog's 4.1B 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, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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