Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Qwen 2.5 Coder 32B delivers a 92% cost reduction over GLM 5.2 (Zhipu).
| Traffic Volume Tier | Qwen 2.5 Coder 32B Monthly | GLM 5.2 (Zhipu) Monthly | Monthly Savings by picking Qwen 2.5 Coder 32B |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $0.478 | $5.78 | Save $5.302 / mo |
| 10,000 reqs/mo (Small App) | $4.78 | $57.80 | Save $53.02 / mo |
| 100,000 reqs/mo (Growth Production) | $47.80 | $578.00 | Save $530.20 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $478.00 | $5,780.00 | Save $5,302.00 / mo |
Qwen 2.5 Coder 32B is 92% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Qwen 2.5 Coder 32B costs $0.000478 per request compared to $0.00578 on GLM 5.2 (Zhipu).
Qwen 2.5 Coder 32B offers a context window of 128,000 tokens (max output: 8,192), while GLM 5.2 (Zhipu) offers 1,000,000 tokens (max output: 64,000).
At 100,000 requests per month, using Qwen 2.5 Coder 32B saves $530.20 every month (or $6,362.40 annually) compared to GLM 5.2 (Zhipu).
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.