GPT-5 mini on streaming video
GPT-5 mini · OpenAI · Multimodal · Closed API
GPT-5 mini is OpenAI's fast, low-cost tier of the GPT-5 family, released in August 2025 with the same 400K-token context window as the full model. On Overshoot it is available as a passthrough model, with gpt-5.4-mini as the current live id, reachable through the same stream publishing and OpenAI-compatible chat-completions request shape used across the API.
GPT-5 mini is available through the Overshoot API as a passthrough model (gpt-5.4-mini, status: ready, verified against the live model catalog on 2026-07-14) - requests are forwarded to the upstream provider rather than running on Overshoot's hosted infrastructure.
- Developer
- OpenAI
- Parameters
- Undisclosed
- Context window
- 400K tokens
- License
- Proprietary
- Released
- Aug 2025
- Inputs
- Text, images, video frames
- Overshoot availability
- API passthroughas of 2026-07-14
What GPT-5 mini is good at
GPT-5 mini keeps solid general vision understanding and strong instruction following while cutting cost and response time relative to the full GPT-5 model. It handles everyday visual question answering, object and scene description, and structured extraction from images without the overhead of deeper routed reasoning.
Its 400K-token context window is unchanged from the flagship, so applications can still carry long conversation history or many sampled frames in a single thread even when they choose the cheaper tier for the per-call model.
- Everyday visual question answering at low per-call cost
- Structured extraction from images and screenshots
- Strong instruction following for well-scoped prompts
Running GPT-5 mini on Overshoot
Publish a camera or screen share over WebRTC through LiveKit to create a Stream, then reference frames with an ovs:// URL in a chat-completions request: the latest frame, an exact timestamp, or a bounded recent segment drawn from the 600-second retention window.
GPT-5 mini reaches the API as a passthrough model (the current live id on Overshoot is gpt-5.4-mini), so Overshoot manages the stream lifecycle, frame selection, and SSE streaming transport, then forwards the resolved request to OpenAI. The request and response shape match Overshoot-hosted models exactly.
GPT-5 mini versus GPT-5
GPT-5 mini is the tier to reach for first when a task is well-scoped and volume or cost matters more than maximum reasoning depth. Step up to full GPT-5 when a query needs deeper multi-step reasoning over dense documents or ambiguous visual scenes; both share the same 400K context window and request format on Overshoot.
Frequently asked questions
Can GPT-5 mini analyze live video?
Yes. Through Overshoot, GPT-5 mini answers questions about a live WebRTC stream using ovs:// frame references inside a standard chat-completions request. Overshoot resolves the reference and forwards it to OpenAI, then streams the response back over SSE.
Is GPT-5 mini open source?
No. GPT-5 mini is a proprietary OpenAI model reached through OpenAI’s API. Overshoot exposes it as a passthrough option using the same key management as its hosted open-weight models.
How fast is GPT-5 mini compared to GPT-5?
GPT-5 mini generally responds faster and costs less per call than full GPT-5, since it targets lighter reasoning workloads. Both are passthrough models on Overshoot, so overall latency still depends on OpenAI’s serving stack rather than Overshoot’s hosted path.
What use cases fit GPT-5 mini best?
GPT-5 mini suits high-volume or latency-sensitive visual queries such as monitoring dashboards, quick scene descriptions, and structured data extraction from frames, where the full reasoning depth of GPT-5 is not required.