Simulating realistic RAG / search-augmented answers parameters (8,000 in / 500 out with 50% cache reuse). GLM-5 (Z.ai) delivers a 27% cost reduction over o3-mini.
| Traffic Volume Tier | o3-mini Monthly | GLM-5 (Z.ai) Monthly | Monthly Savings by picking GLM-5 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $8.80 | $6.40 | Save $2.40 / mo |
| 10,000 reqs/mo (Small App) | $88.00 | $64.00 | Save $24.00 / mo |
| 100,000 reqs/mo (Growth Production) | $880.00 | $640.00 | Save $240.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $8,800.00 | $6,400.00 | Save $2,400.00 / mo |
GLM-5 (Z.ai) is 27% cheaper for RAG / search-augmented answers workloads. At standard RAG / search-augmented answers parameter ratios (8,000 input tokens, 500 output tokens, 50% cache hit), GLM-5 (Z.ai) costs $0.0064 per request compared to $0.0088 on o3-mini.
o3-mini offers a context window of 200,000 tokens (max output: 100,000), while GLM-5 (Z.ai) offers 200,000 tokens (max output: 131,072).
At 100,000 requests per month, using GLM-5 (Z.ai) saves $240.00 every month (or $2,880.00 annually) compared to o3-mini.
retrieved-context dominates cost — tune top-k and chunk size before switching models. Re-rank and drop marginal chunks; halving context roughly halves input cost. Deduplicate repeated chunks across queries to raise the cache hit rate.