Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Databricks DBRX Instruct delivers a 52% cost reduction over GLM-5.1 (Z.ai).
| Traffic Volume Tier | GLM-5.1 (Z.ai) Monthly | Databricks DBRX Instruct Monthly | Monthly Savings by picking Databricks DBRX Instruct |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $3.444 | $1.65 | Save $1.794 / mo |
| 10,000 reqs/mo (Small App) | $34.44 | $16.50 | Save $17.94 / mo |
| 100,000 reqs/mo (Growth Production) | $344.40 | $165.00 | Save $179.40 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $3,444.00 | $1,650.00 | Save $1,794.00 / mo |
Databricks DBRX Instruct is 52% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Databricks DBRX Instruct costs $0.00165 per request compared to $0.003444 on GLM-5.1 (Z.ai).
GLM-5.1 (Z.ai) offers a context window of 200,000 tokens (max output: 131,072), while Databricks DBRX Instruct offers 32,768 tokens (max output: 4,096).
At 100,000 requests per month, using Databricks DBRX Instruct saves $179.40 every month (or $2,152.80 annually) compared to GLM-5.1 (Z.ai).
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.