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Blended $/1M across tracked versions of this line.
Typical 3:1 output-to-input mix, per 1M tokens
Source: litellm
Mixed text, image, audio and document workloads that benefit from one model across modalities.
GLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media. It supports up to 128K tokens, processes complex page layouts...
glm-4.6v is a Multimodal model from Zhipu AI (GLM) (CN). HotON.ai tracks it at $0.30 per 1M input tokens and $0.90 per 1M output tokens, with a 131K-token context window. Its composite efficiency score is 89/100 at an estimated $0.001 per successful task.
glm-4.6v is tracked at $0.30 per 1M input tokens and $0.90 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.75 per 1M tokens. Figures are illustrative demo data.
Mixed text, image, audio and document workloads that benefit from one model across modalities.
glm-4.6v supports up to a 131K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, glm-4.6v is cheaper than 51% of models on input price and ranks #202 of 535 by overall efficiency.
Yes — gemini-2.0-flash-001 is a lower-cost option at $0.40 per 1M output tokens, while still covering similar Multimodal use cases. Compare them side by side on HotON.ai.
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HotON.ai — glm-4.6v (Zhipu AI (GLM)): $0.30/1M input, $0.90/1M output. https://hoton.ai/en/models/z-ai-glm-4-6vPricing is real (via the TestKey catalog, updated daily). Quality (Arena Elo) is real where the model is ranked on LMArena. Efficiency is a modeled composite of real price and context.