Phi-4 Multimodal on real-time video
Phi-4 Multimodal · Microsoft · Small VLM · Open-weight
Phi-4 Multimodal is Microsoft’s compact vision, speech, and text model, released February 2025 at just 5.6B parameters and built with LoRA adapters that let it specialize per modality. Phi-4 Multimodal is not currently in Overshoot's live model catalog. When a compact model like this is available through the API, live camera or screen-share video can be queried with the same low-latency, OpenAI-compatible interface as any other model in the catalog.
Phi-4 Multimodal is not currently in the live Overshoot model catalog (verified against the live model catalog on 2026-07-14). Availability changes over time - query GET /v1beta/models for the current list.
- Developer
- Microsoft
- Parameters
- 5.6B
- Context window
- 128K tokens
- License
- MIT
- Released
- Feb 2025
- Inputs
- Text, images, video frames, speech
- Overshoot availability
- Not in live catalogas of 2026-07-14
What Phi-4 Multimodal is good at
Phi-4 Multimodal punches far above its size on document and math benchmarks, which is unusual for a model under 6B parameters. Its LoRA-adapter design lets vision, speech, and text capabilities coexist without one modality degrading the others, so it holds up on tasks that mix reading a frame with reasoning about numbers or structured text within it.
Its compact size means lower serving cost and faster inference than dense flagship models, which makes it attractive for products that need to query video frequently rather than occasionally, or that want a model small enough to reason about quickly at high request volume.
- Document and math-heavy visual question answering
- High-frequency queries where serving cost matters
- Combined speech and vision tasks via its LoRA adapters
Phi-4 Multimodal and Overshoot's streaming workflow
Phi-4 Multimodal is not currently in Overshoot's live model catalog; GET /v1beta/models lists what is live at any time. The workflow it would plug into is the same for every model on the platform: publish video over WebRTC through LiveKit to open a Stream, then send a chat-completions request that references frames with an ovs:// URL, the latest frame, an exact timestamp, or a recent segment sampled at up to 1 fps from the 600 seconds of retained frame history.
A model this small keeps first-token latency and per-token cost low, which is exactly the profile that suits assistants polling a live stream on a tight loop, so Phi-4 Multimodal is a natural candidate for that pattern wherever it is served.
How it compares to Phi-3.5 Vision
Phi-4 Multimodal is the direct successor to Phi-3.5 Vision, adding speech as a first-class modality alongside vision and text and improving document and math performance. Teams already using Phi-3.5 Vision for image-only tasks can generally move to Phi-4 Multimodal for a capability upgrade at a similar serving cost, and gain speech handling in the process.
Frequently asked questions
Can Phi-4 Multimodal analyze live video?
Phi-4 Multimodal accepts images and video frames, so it can analyze video when self-hosted or served by another provider. It is not currently in Overshoot's live model catalog, so it cannot reference an Overshoot WebRTC stream today. The catalog changes over time; check GET /v1beta/models for the current list.
Is Phi-4 Multimodal open source?
Yes. It ships under the MIT license, one of the most permissive open-source licenses available, which allows unrestricted commercial use, modification, and self-hosting of the weights.
Is Phi-4 Multimodal available on Overshoot?
Not currently. Phi-4 Multimodal is not in Overshoot's live model catalog. The catalog changes over time, so check GET /v1beta/models for the current list of hosted and passthrough models.
What is the difference between Phi-4 Multimodal and Phi-3.5 Vision?
Phi-4 Multimodal is the newer, larger model at 5.6B parameters and adds speech as a supported modality alongside vision and text, with stronger document and math performance. Phi-3.5 Vision is smaller at 4.2B parameters and focused on multi-frame image understanding without speech input.