Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). GLM-4.6 (Z.ai) delivers a 78% cost reduction over Cohere Command R+.
| Traffic Volume Tier | Cohere Command R+ Monthly | GLM-4.6 (Z.ai) Monthly | Monthly Savings by picking GLM-4.6 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $68.50 | $15.095 | Save $53.405 / mo |
| 10,000 reqs/mo (Small App) | $685.00 | $150.95 | Save $534.05 / mo |
| 100,000 reqs/mo (Growth Production) | $6,850.00 | $1,509.50 | Save $5,340.50 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $68,500.00 | $15,095.00 | Save $53,405.00 / mo |
GLM-4.6 (Z.ai) is 78% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), GLM-4.6 (Z.ai) costs $0.0151 per request compared to $0.0685 on Cohere Command R+.
Cohere Command R+ offers a context window of 128,000 tokens (max output: 4,096), while GLM-4.6 (Z.ai) offers 200,000 tokens (max output: 131,072).
At 100,000 requests per month, using GLM-4.6 (Z.ai) saves $5,340.50 every month (or $64,086.00 annually) compared to Cohere Command R+.
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.