Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). GLM-4.6 (Z.ai) delivers a 91% cost reduction over GPT-5.6 Sol.
| Traffic Volume Tier | GPT-5.6 Sol Monthly | GLM-4.6 (Z.ai) Monthly | Monthly Savings by picking GLM-4.6 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $87.60 | $8.072 | Save $79.528 / mo |
| 10,000 reqs/mo (Small App) | $876.00 | $80.72 | Save $795.28 / mo |
| 100,000 reqs/mo (Growth Production) | $8,760.00 | $807.20 | Save $7,952.80 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $87,600.00 | $8,072.00 | Save $79,528.00 / mo |
GLM-4.6 (Z.ai) is 91% 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 $0.0876 on GPT-5.6 Sol.
GPT-5.6 Sol offers a context window of 1,050,000 tokens (max output: 128,000), while GLM-4.6 (Z.ai) offers 200,000 tokens (max output: 131,072).
At 100,000 requests per month, using GLM-4.6 (Z.ai) saves $7,952.80 every month (or $95,433.60 annually) compared to GPT-5.6 Sol.
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.