glm-4.5v
Built on the GLM-4.5-Air base model, GLM-4.5V inherits proven techniques from GLM-4.1V-Thinking while achieving effective scaling through a powerful 106B-parameter MoE architecture.
| Provider | Source | Input Price ($/1M) | Output Price ($/1M) | Description | Free |
|---|---|---|---|---|---|
| vercel | vercel | Input: $0.60 | Output: $1.80 | Built on the GLM-4.5-Air base model, GLM-4.5V inherits proven techniques from GLM-4.1V-Thinking while achieving effective scaling through a powerful 106B-parameter MoE architecture. | |
| siliconflowcn | models-dev | Input: $0.14 | Output: $0.86 | Provider: SiliconFlow (China), Context: 66000, Output Limit: 66000 | |
| siliconflow | models-dev | Input: $0.14 | Output: $0.86 | Provider: SiliconFlow, Context: 66000, Output Limit: 66000 | |
| zhipuai | models-dev | Input: $0.60 | Output: $1.80 | Provider: Zhipu AI, Context: 64000, Output Limit: 16384 | |
| openrouter | openrouter | Input: $0.60 | Output: $1.80 | GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding, image Q&A, OCR, and document parsing, with strong gains in front-end web coding, grounding, and spatial reasoning. It offers a hybrid inference mode: a "thinking mode" for deep reasoning and a "non-thinking mode" for fast responses. Reasoning behavior can be toggled via the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config) Context: 65536 | |
| zai | zai | Input: $0.60 | Output: $0.11 | - |