Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). Databricks DBRX Instruct delivers a 5% cost reduction over Kimi K2.7 Code (OpenRouter).
| Traffic Volume Tier | Databricks DBRX Instruct Monthly | Kimi K2.7 Code (OpenRouter) Monthly | Monthly Savings by picking Databricks DBRX Instruct |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $10.80 | $11.384 | Save $0.584 / mo |
| 10,000 reqs/mo (Small App) | $108.00 | $113.84 | Save $5.84 / mo |
| 100,000 reqs/mo (Growth Production) | $1,080.00 | $1,138.40 | Save $58.40 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $10,800.00 | $11,384.00 | Save $584.00 / mo |
Databricks DBRX Instruct is 5% cheaper for AI coding assistant workloads. At standard AI coding assistant parameter ratios (12,000 input tokens, 2,000 output tokens, 60% cache hit), Databricks DBRX Instruct costs $0.0108 per request compared to $0.0114 on Kimi K2.7 Code (OpenRouter).
Databricks DBRX Instruct offers a context window of 32,768 tokens (max output: 4,096), while Kimi K2.7 Code (OpenRouter) offers 262,144 tokens (max output: 235,929).
At 100,000 requests per month, using Databricks DBRX Instruct saves $58.40 every month (or $700.80 annually) compared to Kimi K2.7 Code (OpenRouter).
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.