Simulating realistic Customer support chatbot parameters (3,500 in / 350 out with 70% cache reuse). GLM-4.6 (Z.ai) delivers a 29% cost reduction over Kimi K2.7 Code (OpenRouter).
| Traffic Volume Tier | GLM-4.6 (Z.ai) Monthly | Kimi K2.7 Code (OpenRouter) Monthly | Monthly Savings by picking GLM-4.6 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $1.67 | $2.359 | Save $0.6895 / mo |
| 10,000 reqs/mo (Small App) | $16.695 | $23.59 | Save $6.895 / mo |
| 100,000 reqs/mo (Growth Production) | $166.95 | $235.90 | Save $68.95 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $1,669.50 | $2,359.00 | Save $689.50 / mo |
GLM-4.6 (Z.ai) is 29% cheaper for Customer support chatbot workloads. At standard Customer support chatbot parameter ratios (3,500 input tokens, 350 output tokens, 70% cache hit), GLM-4.6 (Z.ai) costs $0.00167 per request compared to $0.002359 on Kimi K2.7 Code (OpenRouter).
GLM-4.6 (Z.ai) offers a context window of 200,000 tokens (max output: 131,072), while Kimi K2.7 Code (OpenRouter) offers 262,144 tokens (max output: 235,929).
At 100,000 requests per month, using GLM-4.6 (Z.ai) saves $68.95 every month (or $827.40 annually) compared to Kimi K2.7 Code (OpenRouter).
Cache the system prompt and static documentation chunks — cached input is often 4–10× cheaper. Route simple FAQ turns to a nano-tier model and escalate only complex tickets. Cap max_output per reply; support answers rarely need more than a few hundred tokens.