Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Devstral 2 (2512) (OpenRouter) delivers a 13% cost reduction over GLM-4.5V (Z.ai).
| Traffic Volume Tier | GLM-4.5V (Z.ai) Monthly | Devstral 2 (2512) (OpenRouter) Monthly | Monthly Savings by picking Devstral 2 (2512) (OpenRouter) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $1.454 | $1.272 | Save $0.1824 / mo |
| 10,000 reqs/mo (Small App) | $14.54 | $12.716 | Save $1.824 / mo |
| 100,000 reqs/mo (Growth Production) | $145.40 | $127.16 | Save $18.24 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $1,454.00 | $1,271.60 | Save $182.40 / mo |
Devstral 2 (2512) (OpenRouter) is 13% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Devstral 2 (2512) (OpenRouter) costs $0.001272 per request compared to $0.001454 on GLM-4.5V (Z.ai).
GLM-4.5V (Z.ai) offers a context window of 64,000 tokens (max output: 16,384), while Devstral 2 (2512) (OpenRouter) offers 262,144 tokens (max output: 209,715).
At 100,000 requests per month, using Devstral 2 (2512) (OpenRouter) saves $18.24 every month (or $218.88 annually) compared to GLM-4.5V (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.