glm-z1-flash

glm-z1-flash is a budget-tier language model from Z.AI. glm-z1-flash is a proprietary model available exclusively through Z.AI's API. It is a reasoning model that uses extended chain-of-thought (CoT) processing before producing a final answer — significantly more capable on complex math, science, and multi-step logic tasks, but slower and more expensive per query than standard models. The GLM-Z1 family is Z.AI's model series for this capability tier.

glm-z1-flash is priced at $0.07/1M input tokens and $0.21/1M output tokens. Output tokens are 3× 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, glm-z1-flash costs approximately $0.17 per 1,000 API calls. Z.AI does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $0.07/1M input rate.

Its 131K-token context window handles most long-document tasks — roughly 75,000 words or a full novel — without requiring chunking or retrieval augmentation. Median latency (P50 time-to-first-token) is approximately 220ms — fast enough for real-time interactive applications. Throughput is approximately 200 tokens/second — fast enough to stream responses at reading speed with headroom for concurrent requests. Maximum output length is 16K tokens — sufficient for long-form content generation, detailed code files, and extended reports.

glm-z1-flash is best suited to cost, reasoning, speed. Supported capabilities include function calling / tool use for agentic workflows, streaming token output for real-time UIs. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. 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 glm-z1-flash compares to similar models across these dimensions.

glm-z1-flash sits in the budget tier — the most cost-efficient models, best for high-volume simple tasks, classification, extraction, and applications where cost per call is the primary constraint. Unlike open-weights alternatives, glm-z1-flash is only available via Z.AI's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing glm-z1-flash to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (68/100) 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.