Simulating realistic Agentic workflow parameters (40,000 in / 1,500 out with 65% cache reuse). GLM-5.1 (Z.ai) delivers a 54% cost reduction over Kimi K3 (Moonshot).
| Traffic Volume Tier | Kimi K3 (Moonshot) Monthly | GLM-5.1 (Z.ai) Monthly | Monthly Savings by picking GLM-5.1 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $72.30 | $32.96 | Save $39.34 / mo |
| 10,000 reqs/mo (Small App) | $723.00 | $329.60 | Save $393.40 / mo |
| 100,000 reqs/mo (Growth Production) | $7,230.00 | $3,296.00 | Save $3,934.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $72,300.00 | $32,960.00 | Save $39,340.00 / mo |
GLM-5.1 (Z.ai) is 54% cheaper for Agentic workflow workloads. At standard Agentic workflow parameter ratios (40,000 input tokens, 1,500 output tokens, 65% cache hit), GLM-5.1 (Z.ai) costs $0.033 per request compared to $0.0723 on Kimi K3 (Moonshot).
Kimi K3 (Moonshot) offers a context window of 1,000,000 tokens (max output: 64,000), while GLM-5.1 (Z.ai) offers 200,000 tokens (max output: 131,072).
At 100,000 requests per month, using GLM-5.1 (Z.ai) saves $3,934.00 every month (or $47,208.00 annually) compared to Kimi K3 (Moonshot).
Prompt caching is the single biggest lever — each step re-reads prior context. Summarize or prune tool outputs before appending them to the transcript. Cap the step budget; runaway loops are the #1 surprise on agent invoices.