Simulating realistic Translation parameters (5,000 in / 5,500 out with 15% cache reuse). Microsoft Phi-4 (14B) delivers a 72% cost reduction over GPT-5.4 nano.
Quick answer
For Translation, Microsoft Phi-4 (14B) is the lower-cost option at $0.00215 per request versus $0.00774 for GPT-5.4 nano, a modeled saving of 72%.
Method & trust
The comparison uses 5,000 input tokens, 5,500 output tokens, and 15% cache reuse for the selected workload. Pricing is applied per model, then scaled to monthly request volumes.
| 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) | $2.15 | $7.74 | Save $5.59 / mo |
| 10,000 reqs/mo (Small App) | $21.50 | $77.40 | Save $55.90 / mo |
| 100,000 reqs/mo (Growth Production) | $215.00 | $774.00 | Save $559.00 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $2,150.00 | $7,740.00 | Save $5,590.00 / mo |
Microsoft Phi-4 (14B) is 72% cheaper for Translation workloads. At standard Translation parameter ratios (5,000 input tokens, 5,500 output tokens, 15% cache hit), Microsoft Phi-4 (14B) costs $0.00215 per request compared to $0.00774 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 $559.00 every month (or $6,708.00 annually) compared to GPT-5.4 nano.
Output length ≈ input length; budget both sides of the request. Japanese and Chinese text typically costs more per word than English due to tokenization. Cache translation memories and glossaries embedded in the prompt.