Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). GLM-5 (Z.ai) delivers a 90% cost reduction over o3 (Reasoning Frontier).
| Traffic Volume Tier | o3 (Reasoning Frontier) Monthly | GLM-5 (Z.ai) Monthly | Monthly Savings by picking GLM-5 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $251.50 | $24.92 | Save $226.58 / mo |
| 10,000 reqs/mo (Small App) | $2,515.00 | $249.20 | Save $2,265.80 / mo |
| 100,000 reqs/mo (Growth Production) | $25,150.00 | $2,492.00 | Save $22,658.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $251,500.00 | $24,920.00 | Save $226,580.00 / mo |
GLM-5 (Z.ai) is 90% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), GLM-5 (Z.ai) costs $0.0249 per request compared to $0.2515 on o3 (Reasoning Frontier).
o3 (Reasoning Frontier) offers a context window of 1,050,000 tokens (max output: 128,000), 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 $22,658.00 every month (or $271,896.00 annually) compared to o3 (Reasoning Frontier).
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.