Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). GLM 4.7 (Zhipu) delivers a 14% cost reduction over Qwen3.5 397B A17B (OpenRouter).
| Traffic Volume Tier | GLM 4.7 (Zhipu) Monthly | Qwen3.5 397B A17B (OpenRouter) Monthly | Monthly Savings by picking GLM 4.7 (Zhipu) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $8.072 | $9.36 | Save $1.288 / mo |
| 10,000 reqs/mo (Small App) | $80.72 | $93.60 | Save $12.88 / mo |
| 100,000 reqs/mo (Growth Production) | $807.20 | $936.00 | Save $128.80 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $8,072.00 | $9,360.00 | Save $1,288.00 / mo |
GLM 4.7 (Zhipu) is 14% 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 4.7 (Zhipu) costs $0.008072 per request compared to $0.00936 on Qwen3.5 397B A17B (OpenRouter).
GLM 4.7 (Zhipu) offers a context window of 200,000 tokens (max output: 131,072), while Qwen3.5 397B A17B (OpenRouter) offers 262,144 tokens (max output: 65,536).
At 100,000 requests per month, using GLM 4.7 (Zhipu) saves $128.80 every month (or $1,545.60 annually) compared to Qwen3.5 397B A17B (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.