Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). GLM-5.1 (Z.ai) delivers a 74% cost reduction over GPT-5.5 Standard.
| Traffic Volume Tier | GPT-5.5 Standard Monthly | GLM-5.1 (Z.ai) Monthly | Monthly Savings by picking GLM-5.1 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $131.75 | $34.79 | Save $96.96 / mo |
| 10,000 reqs/mo (Small App) | $1,317.50 | $347.90 | Save $969.60 / mo |
| 100,000 reqs/mo (Growth Production) | $13,175.00 | $3,479.00 | Save $9,696.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $131,750.00 | $34,790.00 | Save $96,960.00 / mo |
GLM-5.1 (Z.ai) is 74% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), GLM-5.1 (Z.ai) costs $0.0348 per request compared to $0.1317 on GPT-5.5 Standard.
GPT-5.5 Standard offers a context window of 512,000 tokens (max output: 64,000), while GLM-5.1 (Z.ai) offers 200,000 tokens (max output: 131,072).
At 100,000 requests per month, using GLM-5.1 (Z.ai) saves $9,696.00 every month (or $116,352.00 annually) compared to GPT-5.5 Standard.
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.