Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). GLM-4.5V (Z.ai) delivers a 77% cost reduction over GPT-5.3 Codex.
| Traffic Volume Tier | GPT-5.3 Codex Monthly | GLM-4.5V (Z.ai) Monthly | Monthly Savings by picking GLM-4.5V (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $6.37 | $1.454 | Save $4.916 / mo |
| 10,000 reqs/mo (Small App) | $63.70 | $14.54 | Save $49.16 / mo |
| 100,000 reqs/mo (Growth Production) | $637.00 | $145.40 | Save $491.60 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $6,370.00 | $1,454.00 | Save $4,916.00 / mo |
GLM-4.5V (Z.ai) is 77% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), GLM-4.5V (Z.ai) costs $0.001454 per request compared to $0.00637 on GPT-5.3 Codex.
GPT-5.3 Codex offers a context window of 256,000 tokens (max output: 64,000), while GLM-4.5V (Z.ai) offers 64,000 tokens (max output: 16,384).
At 100,000 requests per month, using GLM-4.5V (Z.ai) saves $491.60 every month (or $5,899.20 annually) compared to GPT-5.3 Codex.
Volume makes nano-tier models attractive — extraction rarely needs frontier reasoning. Constrain output with JSON schema modes to avoid retry loops on malformed responses. Cache shared schema instructions and few-shot examples across calls.