Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% 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) | $8.072 | $11.384 | Save $3.312 / mo |
| 10,000 reqs/mo (Small App) | $80.72 | $113.84 | Save $33.12 / mo |
| 100,000 reqs/mo (Growth Production) | $807.20 | $1,138.40 | Save $331.20 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $8,072.00 | $11,384.00 | Save $3,312.00 / mo |
GLM-4.6 (Z.ai) is 29% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), GLM-4.6 (Z.ai) costs $0.008072 per request compared to $0.0114 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 $331.20 every month (or $3,974.40 annually) compared to Kimi K2.7 Code (OpenRouter).
Output is the expensive side — prefer models with cheap output for autocomplete-style calls. Cache repository context between keystrokes; diffs change far less than the full file. Measure acceptance rate: paying for output users delete is pure waste.