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No. GLM-4.5 is a large language model for agentic reasoning, coding, tool use, and text generation; it is listed here as an AI model rather than a turnkey voice-agent platform. To build a complete voice agent, you would still need speech recognition, text-to-speech, a call or realtime transport layer, state management, and production observability. GLM-4.5 is better suited to engineering teams building their own agent infrastructure than teams looking for a ready-made call center product.
The main GLM-4.5 model uses a Mixture-of-Experts architecture with 355 billion total parameters and 32 billion active parameters per forward pass. Z.AI documentation lists a 128K-token context length and up to 96K maximum output tokens. The series was pretrained on 15 trillion tokens and includes GLM-4.5-Air, a smaller 106B total / 12B active model for more cost-sensitive deployments. These numbers make it a large, infrastructure-heavy model rather than a lightweight local assistant.
Yes, the official materials state that GLM-4.5 and GLM-4.5-Air are released under the MIT open-source license. That allows commercial use, modification, self-hosting, and secondary development without paying a model license fee. However, free licensing does not mean free operation: self-hosting a 355B-parameter MoE model requires substantial GPU infrastructure, and hosted API providers charge usage-based token fees. Z.AI documentation lists GLM-4.5 at $0.60 per million input tokens, $0.11 per million cached input tokens, and $2.20 per million output tokens, with GLM-4.5-Air listed at $0.20 per million input tokens, $0.03 per million cached input tokens, and $1.10 per million output tokens.
GLM-4.5's main advantage over closed models is control: teams can download weights, self-host, fine-tune, inspect deployment behavior, and avoid sending sensitive data to a third-party model API. Z.AI reports a 63.2 aggregate score across 12 benchmarks and positions GLM-4.5 as one of the strongest open-source models for reasoning, coding, and agent tasks. Closed models may still offer easier operations, stronger managed safety tooling, broader enterprise support, and simpler procurement. For teams with GPU capacity and model-serving expertise, GLM-4.5 is a serious open alternative; for teams without that infrastructure, a managed API may be more practical.
GLM-4.5 is not designed for casual laptop deployment. The Hugging Face model card lists GLM-4.5 BF16 inference on H100 x 16 or H200 x 8, and full 128K-context BF16 inference on H100 x 32 or H200 x 16. FP8 reduces the requirement, with GLM-4.5 FP8 listed at H100 x 8 or H200 x 4 for standard inference and H100 x 16 or H200 x 8 for full 128K context. The same official requirements also state that server memory should exceed 1T for normal model loading and operation. Smaller teams should evaluate GLM-4.5-Air, quantized builds, or hosted APIs before committing to self-hosting.
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Last verified March 2026