Simulating realistic Content generation parameters (800 in / 1,200 out with 30% cache reuse). Kimi K2.7 Code (OpenRouter) delivers a 27% cost reduction over GLM-5.2 (Z.ai).
| Traffic Volume Tier | GLM-5.2 (Z.ai) Monthly | Kimi K2.7 Code (OpenRouter) Monthly | Monthly Savings by picking Kimi K2.7 Code (OpenRouter) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $6.126 | $4.501 | Save $1.626 / mo |
| 10,000 reqs/mo (Small App) | $61.264 | $45.008 | Save $16.256 / mo |
| 100,000 reqs/mo (Growth Production) | $612.64 | $450.08 | Save $162.56 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $6,126.40 | $4,500.80 | Save $1,625.60 / mo |
Kimi K2.7 Code (OpenRouter) is 27% cheaper for Content generation workloads. At standard Content generation parameter ratios (800 input tokens, 1,200 output tokens, 30% cache hit), Kimi K2.7 Code (OpenRouter) costs $0.004501 per request compared to $0.006126 on GLM-5.2 (Z.ai).
GLM-5.2 (Z.ai) offers a context window of 1,048,576 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 Kimi K2.7 Code (OpenRouter) saves $162.56 every month (or $1,950.72 annually) compared to GLM-5.2 (Z.ai).
Output-heavy workloads favor models with a low output price, not a low input price. Batch similar generation tasks with shared style prompts to exploit caching. Draft with a cheap tier, refine the winners with a premium model.