Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). GLM-4.5V (Z.ai) delivers a 33% cost reduction over Databricks DBRX Instruct.
| Traffic Volume Tier | GLM-4.5V (Z.ai) Monthly | Databricks DBRX Instruct Monthly | Monthly Savings by picking GLM-4.5V (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $7.272 | $10.80 | Save $3.528 / mo |
| 10,000 reqs/mo (Small App) | $72.72 | $108.00 | Save $35.28 / mo |
| 100,000 reqs/mo (Growth Production) | $727.20 | $1,080.00 | Save $352.80 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $7,272.00 | $10,800.00 | Save $3,528.00 / mo |
GLM-4.5V (Z.ai) is 33% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), GLM-4.5V (Z.ai) costs $0.007272 per request compared to $0.0108 on Databricks DBRX Instruct.
GLM-4.5V (Z.ai) offers a context window of 64,000 tokens (max output: 16,384), while Databricks DBRX Instruct offers 32,768 tokens (max output: 4,096).
At 100,000 requests per month, using GLM-4.5V (Z.ai) saves $352.80 every month (or $4,233.60 annually) compared to Databricks DBRX Instruct.
Output is the expensive side — prefer models with cheap output for autocomplete-style calls. Cache repository context between keystrokes; diffs change far less than the full file. Measure acceptance rate: paying for output users delete is pure waste.