Simulating realistic Document summarization parameters (25,000 in / 600 out with 10% cache reuse). GPT-5.3 Codex delivers a 14% cost reduction over GLM-4.5-X (Z.ai).
| Traffic Volume Tier | GPT-5.3 Codex Monthly | GLM-4.5-X (Z.ai) Monthly | Monthly Savings by picking GPT-5.3 Codex |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $48.213 | $55.965 | Save $7.753 / mo |
| 10,000 reqs/mo (Small App) | $482.125 | $559.65 | Save $77.525 / mo |
| 100,000 reqs/mo (Growth Production) | $4,821.25 | $5,596.50 | Save $775.25 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $48,212.50 | $55,965.00 | Save $7,752.50 / mo |
GPT-5.3 Codex is 14% cheaper for Document summarization workloads. At standard Document summarization parameter ratios (25,000 input tokens, 600 output tokens, 10% cache hit), GPT-5.3 Codex costs $0.0482 per request compared to $0.056 on GLM-4.5-X (Z.ai).
GPT-5.3 Codex offers a context window of 256,000 tokens (max output: 64,000), while GLM-4.5-X (Z.ai) offers 128,000 tokens (max output: 98,304).
At 100,000 requests per month, using GPT-5.3 Codex saves $775.25 every month (or $9,303.00 annually) compared to GLM-4.5-X (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.