GLM 4.5V
GLM 4.5V is a vision-language chat model from Z.ai, based on the GLM-4.5-Air text foundation model. It is genuinely best at handling diverse visual content for tasks like image reasoning, video understanding, document analysis, and GUI agent operations.
Input
Output
Context
66K
Max Output
16K
Parameters
107.7B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Benchmarks
Estimated GPU requirements for GLM 4.5V
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 GLM 4.5V
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
GLM 4.5V advertises a context window of 65,536 tokens, with a maximum output of 16,384 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. GLM 4.5V is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
GLM 4.5V 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.
GLM 4.5V 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 GLM 4.5V is shown as not available rather than estimated.
Yes. GLM 4.5V is served on the managed pool through the OpenAI-compatible endpoint as zai-glm-4-5v, pinned by name or chosen by routing when it is the best fit for a request. The Try in Playground button opens it directly.
About 104.6 GB at INT8 for the weights and a default context, from the catalog's 107.7B 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 22, 2026
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