Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). GLM 4.7 (Zhipu) delivers a 54% cost reduction over GLM-5.3 (Z.ai).
| Traffic Volume Tier | GLM-5.3 (Z.ai) Monthly | GLM 4.7 (Zhipu) Monthly | Monthly Savings by picking GLM 4.7 (Zhipu) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $17.392 | $8.072 | Save $9.32 / mo |
| 10,000 reqs/mo (Small App) | $173.92 | $80.72 | Save $93.20 / mo |
| 100,000 reqs/mo (Growth Production) | $1,739.20 | $807.20 | Save $932.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $17,392.00 | $8,072.00 | Save $9,320.00 / mo |
GLM 4.7 (Zhipu) is 54% 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 $0.0174 on GLM-5.3 (Z.ai).
GLM-5.3 (Z.ai) offers a context window of 1,048,576 tokens (max output: 131,072), while GLM 4.7 (Zhipu) offers 200,000 tokens (max output: 131,072).
At 100,000 requests per month, using GLM 4.7 (Zhipu) saves $932.00 every month (or $11,184.00 annually) compared to GLM-5.3 (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.