Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Qwen 2.5 Coder 32B delivers a 85% cost reduction over DeepSeek V4 Pro.
| Traffic Volume Tier | DeepSeek V4 Pro Monthly | Qwen 2.5 Coder 32B Monthly | Monthly Savings by picking Qwen 2.5 Coder 32B |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $3.12 | $0.478 | Save $2.642 / mo |
| 10,000 reqs/mo (Small App) | $31.196 | $4.78 | Save $26.416 / mo |
| 100,000 reqs/mo (Growth Production) | $311.96 | $47.80 | Save $264.16 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $3,119.60 | $478.00 | Save $2,641.60 / mo |
Qwen 2.5 Coder 32B is 85% 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 $0.00312 on DeepSeek V4 Pro.
DeepSeek V4 Pro offers a context window of 1,000,000 tokens (max output: 384,000), while Qwen 2.5 Coder 32B offers 128,000 tokens (max output: 8,192).
At 100,000 requests per month, using Qwen 2.5 Coder 32B saves $264.16 every month (or $3,169.92 annually) compared to DeepSeek V4 Pro.
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.