Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). GLM-4.5 (Z.ai) delivers a 50% cost reduction over Grok 4.3.
| Traffic Volume Tier | Grok 4.3 Monthly | GLM-4.5 (Z.ai) Monthly | Monthly Savings by picking GLM-4.5 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $30.125 | $15.095 | Save $15.03 / mo |
| 10,000 reqs/mo (Small App) | $301.25 | $150.95 | Save $150.30 / mo |
| 100,000 reqs/mo (Growth Production) | $3,012.50 | $1,509.50 | Save $1,503.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $30,125.00 | $15,095.00 | Save $15,030.00 / mo |
GLM-4.5 (Z.ai) is 50% 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 $0.0301 on Grok 4.3.
Grok 4.3 offers a context window of 1,000,000 tokens (max output: 32,768), 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 $1,503.00 every month (or $18,036.00 annually) compared to Grok 4.3.
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.