Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Grok 4.3 delivers a 38% cost reduction over GLM 4.7 (Zhipu).
| Traffic Volume Tier | Grok 4.3 Monthly | GLM 4.7 (Zhipu) Monthly | Monthly Savings by picking Grok 4.3 |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $2.705 | $4.378 | Save $1.672 / mo |
| 10,000 reqs/mo (Small App) | $27.05 | $43.775 | Save $16.725 / mo |
| 100,000 reqs/mo (Growth Production) | $270.50 | $437.75 | Save $167.25 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $2,705.00 | $4,377.50 | Save $1,672.50 / mo |
Grok 4.3 is 38% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Grok 4.3 costs $0.002705 per request compared to $0.004378 on GLM 4.7 (Zhipu).
Grok 4.3 offers a context window of 1,000,000 tokens (max output: 32,768), while GLM 4.7 (Zhipu) offers 128,000 tokens (max output: 16,384).
At 100,000 requests per month, using Grok 4.3 saves $167.25 every month (or $2,007.00 annually) compared to GLM 4.7 (Zhipu).
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.