Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Kimi K2.7 Code (OpenRouter) delivers a 35% cost reduction over GLM-5.1 (Z.ai).
| Traffic Volume Tier | GLM-5.1 (Z.ai) Monthly | Kimi K2.7 Code (OpenRouter) Monthly | Monthly Savings by picking Kimi K2.7 Code (OpenRouter) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $17.392 | $11.384 | Save $6.008 / mo |
| 10,000 reqs/mo (Small App) | $173.92 | $113.84 | Save $60.08 / mo |
| 100,000 reqs/mo (Growth Production) | $1,739.20 | $1,138.40 | Save $600.80 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $17,392.00 | $11,384.00 | Save $6,008.00 / mo |
Kimi K2.7 Code (OpenRouter) is 35% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), Kimi K2.7 Code (OpenRouter) costs $0.0114 per request compared to $0.0174 on GLM-5.1 (Z.ai).
GLM-5.1 (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 Kimi K2.7 Code (OpenRouter) saves $600.80 every month (or $7,209.60 annually) compared to GLM-5.1 (Z.ai).
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.