Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). GLM-5.1 (Z.ai) delivers a 28% cost reduction over GPT-5.3 Codex.
| Traffic Volume Tier | GPT-5.3 Codex Monthly | GLM-5.1 (Z.ai) Monthly | Monthly Savings by picking GLM-5.1 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $48.213 | $34.79 | Save $13.422 / mo |
| 10,000 reqs/mo (Small App) | $482.125 | $347.90 | Save $134.225 / mo |
| 100,000 reqs/mo (Growth Production) | $4,821.25 | $3,479.00 | Save $1,342.25 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $48,212.50 | $34,790.00 | Save $13,422.50 / mo |
GLM-5.1 (Z.ai) is 28% 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 $0.0482 on GPT-5.3 Codex.
GPT-5.3 Codex offers a context window of 256,000 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 $1,342.25 every month (or $16,107.00 annually) compared to GPT-5.3 Codex.
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.