Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). MiniMax-01 (4M Context) delivers a 65% cost reduction over GLM-4.5V (Z.ai).
| Traffic Volume Tier | GLM-4.5V (Z.ai) Monthly | MiniMax-01 (4M Context) Monthly | Monthly Savings by picking MiniMax-01 (4M Context) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $14.855 | $5.26 | Save $9.595 / mo |
| 10,000 reqs/mo (Small App) | $148.55 | $52.60 | Save $95.95 / mo |
| 100,000 reqs/mo (Growth Production) | $1,485.50 | $526.00 | Save $959.50 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $14,855.00 | $5,260.00 | Save $9,595.00 / mo |
MiniMax-01 (4M Context) is 65% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), MiniMax-01 (4M Context) costs $0.00526 per request compared to $0.0149 on GLM-4.5V (Z.ai).
GLM-4.5V (Z.ai) offers a context window of 64,000 tokens (max output: 16,384), while MiniMax-01 (4M Context) offers 4,000,000 tokens (max output: 64,000).
At 100,000 requests per month, using MiniMax-01 (4M Context) saves $959.50 every month (or $11,514.00 annually) compared to GLM-4.5V (Z.ai).
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.