Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). GLM-5 (Z.ai) delivers a 21% cost reduction over o4-mini.
| Traffic Volume Tier | o4-mini Monthly | GLM-5 (Z.ai) Monthly | Monthly Savings by picking GLM-5 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $16.06 | $12.64 | Save $3.42 / mo |
| 10,000 reqs/mo (Small App) | $160.60 | $126.40 | Save $34.20 / mo |
| 100,000 reqs/mo (Growth Production) | $1,606.00 | $1,264.00 | Save $342.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $16,060.00 | $12,640.00 | Save $3,420.00 / mo |
GLM-5 (Z.ai) is 21% 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-5 (Z.ai) costs $0.0126 per request compared to $0.0161 on o4-mini.
o4-mini offers a context window of 256,000 tokens (max output: 100,000), while GLM-5 (Z.ai) offers 200,000 tokens (max output: 131,072).
At 100,000 requests per month, using GLM-5 (Z.ai) saves $342.00 every month (or $4,104.00 annually) compared to o4-mini.
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.