Z.ai GLM API Pricing

Z.ai (Zhipu) API cost depends on the selected model, input tokens, output tokens, request volume, retries, and any cached input pricing or discount rules that apply. Use the pricing table and calculator with your own workload assumptions instead of relying on a single average price.

Estimate one request with input tokens, expected output tokens, model choice, and current per-1M-token pricing. Then multiply by requests per user, active users, days per month, retry rate, and fallback calls.

GLM models are usually evaluated for workflows such as coding agents, low-cost multimodal work, cache-heavy prompts. The best route depends on quality, latency, context size, output length, and monthly budget.

Z.ai (Zhipu) can be cheaper or more expensive depending on the exact model, prompt size, output length, cache eligibility, retry rate, and quality target. Compare total workflow cost across providers before switching production traffic.

Not listed means the current local pricing source does not expose a confirmed numeric value for that field. It is intentionally not treated as zero. Check the official Z.ai (Zhipu) pricing page and billing dashboard before launch.

Control spend by setting monthly budget limits, measuring input and output tokens separately, capping output length, reducing repeated context, reviewing retry cost, and comparing model routes before high-volume traffic grows.