Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). GLM-4.6V (Z.ai) delivers a 82% cost reduction over Kimi K2.6.
| Traffic Volume Tier | Kimi K2.6 Monthly | GLM-4.6V (Z.ai) Monthly | Monthly Savings by picking GLM-4.6V (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $3.96 | $0.725 | Save $3.235 / mo |
| 10,000 reqs/mo (Small App) | $39.60 | $7.25 | Save $32.35 / mo |
| 100,000 reqs/mo (Growth Production) | $396.00 | $72.50 | Save $323.50 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $3,960.00 | $725.00 | Save $3,235.00 / mo |
GLM-4.6V (Z.ai) is 82% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), GLM-4.6V (Z.ai) costs $0.000725 per request compared to $0.00396 on Kimi K2.6.
Kimi K2.6 offers a context window of 1,000,000 tokens (max output: 32,768), while GLM-4.6V (Z.ai) offers 128,000 tokens (max output: 32,768).
At 100,000 requests per month, using GLM-4.6V (Z.ai) saves $323.50 every month (or $3,882.00 annually) compared to Kimi K2.6.
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.