Simulating realistic AI coding assistant parameters (12,000 in / 2,000 out with 60% cache reuse). GLM-4.5V (Z.ai) delivers a 85% cost reduction over Cohere Command R+.
| Traffic Volume Tier | Cohere Command R+ Monthly | GLM-4.5V (Z.ai) Monthly | Monthly Savings by picking GLM-4.5V (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $50.00 | $7.272 | Save $42.728 / mo |
| 10,000 reqs/mo (Small App) | $500.00 | $72.72 | Save $427.28 / mo |
| 100,000 reqs/mo (Growth Production) | $5,000.00 | $727.20 | Save $4,272.80 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $50,000.00 | $7,272.00 | Save $42,728.00 / mo |
GLM-4.5V (Z.ai) is 85% 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 $0.05 on Cohere Command R+.
Cohere Command R+ offers a context window of 128,000 tokens (max output: 4,096), 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 $4,272.80 every month (or $51,273.60 annually) compared to Cohere Command R+.
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.