GLM-4.5V on live video streams

What GLM-4.5V is good at

GLM-4.5V posts leading open-weight results across more than forty multimodal benchmarks, spanning general visual question answering, document understanding, and video comprehension. The mixture-of-experts design keeps per-token compute close to a 12B model while drawing on a much larger 106B parameter pool for quality.

Its switchable thinking mode lets an application choose between a fast direct answer and a slower, more deliberate reasoning pass on the same model, without switching to a different checkpoint or provider.

• Broad multimodal benchmark strength across image, document, and video tasks
• Switchable thinking mode for harder analytical queries
• Efficient serving from sparse activation despite the large total parameter count

GLM-4.5V and Overshoot's live video API

GLM-4.5V is not currently in Overshoot's live model catalog. The catalog changes over time, and GET /v1beta/models is the authoritative list of what is available, so check there before building around a specific model.

For models that are in the catalog, the workflow is consistent: create a Stream, publish a LiveKit video track, then send a chat-completions request whose image_url or video_url is an ovs:// reference. Overshoot retains 600 seconds of frame history, so a request can anchor the latest frame, an exact timestamp, or a recent segment sampled at up to 1 fps, with answers streaming token by token over SSE and thread_id keeping prompt-cache hits across repeated queries. If GLM-4.5V joins the catalog, its default mode would suit low-latency queries, with thinking mode reserved for questions where the extra latency is worth the accuracy.

How it fits against other open flagships

GLM-4.5V competes directly with the Qwen3-VL flagships and shares the MIT license, which is more permissive than Qwen’s own license terms. Teams choosing between them typically weigh GLM-4.5V’s broad benchmark strength and thinking-mode flexibility against Qwen3-VL’s longer 256K context window, and pick based on which matters more for the specific application.

Frequently asked questions

Can GLM-4.5V analyze live video?

GLM-4.5V posts strong video-comprehension results, so the model itself can reason about frames sampled from a live feed. It is not currently in Overshoot's live model catalog, however; the catalog changes over time, so check GET /v1beta/models for the models that can be pointed at a live WebRTC stream today.

Is GLM-4.5V open source?

Yes. GLM-4.5V ships under the MIT license, one of the most permissive terms among open vision-language models, with freely downloadable weights that can be self-hosted or served by any provider.

How fast is GLM-4.5V?

GLM-4.5V activates only 12B of its 106B parameters per token, which keeps per-token compute and latency close to a much smaller dense model in default mode. Its optional thinking mode trades some speed for deeper reasoning on harder analytical questions.

What is the difference between GLM-4.5V and its thinking mode?

Default mode answers directly and suits low-latency live-video queries. Thinking mode has the model work through a visible reasoning process first, which improves accuracy on harder analytical questions at the cost of a longer response.

Related models

• Qwen3-VL 235B-A22B
• Qwen3-VL 32B
• CogVLM2
• QVQ-72B
• Holo 3.1 35B A3B
• Holo3 35B A3B