Hugging Face model card
Fields: tags, gated, license, summary, languages, library name, pipeline tag
We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity.
- Context
- 198K
- Max output
- 128K
- Input
- $0.60
- Output
- $1.92
Recommended use cases
Strengths in this dataset
- 198,000-token context window
- text input
- 19 supported API parameters listed
Limits and caveats
- Provider behavior and pricing can change; verify the linked sources before production use.
Capability
Model-native facts
Model- Reasoning
- Supported
- Open weights
- Supported
Provider endpoint facts
Provider endpoint- Tool calling
- Supported
- Structured output
- Supported
- Streaming
- Unknown
- Prompt cache
- Supported
- Batch
- Unknown
- Fine-tuning
- Unknown
GLM 5 Provider pricing
Provider endpoint: z-ai/glm-5
- Input
- $0.60 per 1M tokens
- Output
- $1.92 per 1M tokens
- Cached input
- $0.12 per 1M tokens
- Image output
- Unknown per 1M tokens
Sources and verification
Official model documentation
Fields: model identity
OpenRouter Models API
Fields: identity, description, modalities, context window, maximum output, pricing, supported parameters
Frequently asked questions
Answers are generated from the same sourced model and provider facts shown above.
We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity.
Model specifications and prices may vary by provider and change over time. AIToolly displays sources and verification dates so users can confirm critical details before production use.