Simulating realistic Data extraction & tagging parameters (2,000 in / 250 out with 20% cache reuse). Microsoft Phi-4 (14B) delivers a 57% cost reduction over GPT-5.4 nano.
| Traffic Volume Tier | Microsoft Phi-4 (14B) Monthly | GPT-5.4 nano Monthly | Monthly Savings by picking Microsoft Phi-4 (14B) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $0.275 | $0.6405 | Save $0.3655 / mo |
| 10,000 reqs/mo (Small App) | $2.75 | $6.405 | Save $3.655 / mo |
| 100,000 reqs/mo (Growth Production) | $27.50 | $64.05 | Save $36.55 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $275.00 | $640.50 | Save $365.50 / mo |
Microsoft Phi-4 (14B) is 57% cheaper for Data extraction & tagging workloads. At standard Data extraction & tagging parameter ratios (2,000 input tokens, 250 output tokens, 20% cache hit), Microsoft Phi-4 (14B) costs $0.000275 per request compared to $0.000641 on GPT-5.4 nano.
Microsoft Phi-4 (14B) offers a context window of 16,384 tokens (max output: 4,096), while GPT-5.4 nano offers 128,000 tokens (max output: 16,384).
At 100,000 requests per month, using Microsoft Phi-4 (14B) saves $36.55 every month (or $438.60 annually) compared to GPT-5.4 nano.
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.