Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). Llama 4 Maverick (400B MoE) delivers a 66% cost reduction over GLM-5.1 (Z.ai).
| Traffic Volume Tier | Llama 4 Maverick (400B MoE) Monthly | GLM-5.1 (Z.ai) Monthly | Monthly Savings by picking Llama 4 Maverick (400B MoE) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $12.00 | $34.79 | Save $22.79 / mo |
| 10,000 reqs/mo (Small App) | $120.00 | $347.90 | Save $227.90 / mo |
| 100,000 reqs/mo (Growth Production) | $1,200.00 | $3,479.00 | Save $2,279.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $12,000.00 | $34,790.00 | Save $22,790.00 / mo |
Llama 4 Maverick (400B MoE) is 66% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), Llama 4 Maverick (400B MoE) costs $0.012 per request compared to $0.0348 on GLM-5.1 (Z.ai).
Llama 4 Maverick (400B MoE) offers a context window of 1,000,000 tokens (max output: 16,384), while GLM-5.1 (Z.ai) offers 200,000 tokens (max output: 131,072).
At 100,000 requests per month, using Llama 4 Maverick (400B MoE) saves $2,279.00 every month (or $27,348.00 annually) compared to GLM-5.1 (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.