z-ai/glm-5.3

z-ai/glm-5.3 is a advanced-tier language model from RouterPlus. z-ai/glm-5.3 is a proprietary model available exclusively through RouterPlus's API. The GLM-5.3 family is RouterPlus's model series for this capability tier.

z-ai/glm-5.3 is priced at $1.19/1M input tokens and $6/1M output tokens. Output tokens are 5× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, z-ai/glm-5.3 costs approximately $4.19 per 1,000 API calls. Prompt caching reduces repeated input tokens to $0.2/1M — a 83% discount that compounds significantly for applications with long shared system prompts, multi-turn conversations, or document Q&A where the same context is reused across many queries.

Its 1.0M-token context window is among the largest available — sufficient for processing entire codebases, book-length documents, or hours of transcribed audio in a single API call. Maximum output length is 524K tokens — sufficient for long-form content generation, detailed code files, and extended reports.

z-ai/glm-5.3 is best suited to general language tasks. Supported capabilities include streaming token output for real-time UIs. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how z-ai/glm-5.3 compares to similar models across these dimensions.

z-ai/glm-5.3 sits in the advanced tier — strong general-purpose models balancing capability and cost, suitable for most production applications. Unlike open-weights alternatives, z-ai/glm-5.3 is only available via RouterPlus's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing z-ai/glm-5.3 to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (N/A/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.