Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). GLM-4.5 (Z.ai) delivers a 48% cost reduction over o3-mini.
| Traffic Volume Tier | o3-mini Monthly | GLM-4.5 (Z.ai) Monthly | Monthly Savings by picking GLM-4.5 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $28.765 | $15.095 | Save $13.67 / mo |
| 10,000 reqs/mo (Small App) | $287.65 | $150.95 | Save $136.70 / mo |
| 100,000 reqs/mo (Growth Production) | $2,876.50 | $1,509.50 | Save $1,367.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $28,765.00 | $15,095.00 | Save $13,670.00 / mo |
GLM-4.5 (Z.ai) is 48% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), GLM-4.5 (Z.ai) costs $0.0151 per request compared to $0.0288 on o3-mini.
o3-mini offers a context window of 200,000 tokens (max output: 100,000), while GLM-4.5 (Z.ai) offers 128,000 tokens (max output: 98,304).
At 100,000 requests per month, using GLM-4.5 (Z.ai) saves $1,367.00 every month (or $16,404.00 annually) compared to o3-mini.
Long-context models pay off here — compare price per 1M tokens at your true document size. Summarize once, store the result; don't re-summarize unchanged documents. For batch backfills, nightly jobs can use cache-friendly request ordering.