Phi 4 Multimodal Instruct
Microsoft's Phi 4 Multimodal Instruct is a lightweight, open-source model that processes text, image, and audio inputs to generate text outputs, with a context window of 131,072 tokens. It is genuinely best at audio processing and speech-to-text tasks, and notably, it supports a wide range of languages across its modalities, including English, Chinese, and several European languages.
Input
Output
Context
131K
Max Output
4K
Parameters
5.6B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Benchmarks
Resources & Links
Estimated GPU requirements for Phi 4 Multimodal Instruct
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 Phi 4 Multimodal Instruct
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Phi 4 Multimodal Instruct advertises a context window of 131,072 tokens, with a maximum output of 4,096 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. Phi 4 Multimodal 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 4 Multimodal Instruct accepts Audio 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.
Phi 4 Multimodal Instruct has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured Phi 4 Multimodal Instruct is shown as not available rather than estimated.
Not on the managed pool today; Phi 4 Multimodal 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 5.9 GB at INT8 for the weights and a default context, from the catalog's 5.6B 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 11, 2026
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