GPT-5.6 Luna vs Microsoft Phi-4 (14B)
Live Unit Economics, TTFT Latency & Reasoning Benchmark Analysis
Which model is better: GPT-5.6 Luna or Microsoft Phi-4 (14B)?
Verified Performance & Unit Economics Delta
Daily synchronized metrics from synthetic latency endpoints and benchmark evaluations.
| Metric / Dimension | GPT-5.6 Luna | Microsoft Phi-4 (14B) | Net Delta / Advantage |
|---|---|---|---|
| Input Cost / 1M Tokens | $0.18 | $0.12 | Microsoft Phi-4 (14B) is 1.5x cheaper |
| Output Cost / 1M Tokens | $0.72 | $0.36 | Microsoft Phi-4 (14B) (100% lower) |
| Avg TTFT Response Latency | 90ms | 110ms | GPT-5.6 Luna (+20ms faster) |
| SWE-bench Verified (Coding) | 48.5% | 42.1% | GPT-5.6 Luna (+6.4% lead) |
| Inference Value Score (IVR) | 99.9 / 100 | 99.9 / 100 | Microsoft Phi-4 (14B) (+0 pts) |
Interactive Monthly Token Economics & ROI Forecaster
Model your expected production workload across prompt (input) and completion (output) tokens.
Estimated Monthly Spend
$0.18/1M in · $0.72/1M out
$0.12/1M in · $0.36/1M out
When to Choose GPT-5.6 Luna
Choose GPT-5.6 Luna when you need Real-time conversational agents, high-volume classification, intent detection, and low-latency interactive apps. and have an infrastructure budget aligned with $0.18/1M tokens.
When to Choose Microsoft Phi-4 (14B)
Choose Microsoft Phi-4 (14B) when you need High-efficiency local math reasoning, embedded device processing, on-device SLM logic, and low-latency classification. and prioritize Microsoft ecosystem integration at $0.12/1M tokens.
Related Questions & Decision Factors
Which is cheaper: GPT-5.6 Luna or Microsoft Phi-4 (14B)?
Microsoft Phi-4 (14B) is cheaper at $0.12/1M input tokens compared to $0.18/1M for GPT-5.6 Luna.
Which model has lower latency: GPT-5.6 Luna or Microsoft Phi-4 (14B)?
GPT-5.6 Luna delivers faster response times with an average TTFT of 90ms vs 110ms for Microsoft Phi-4 (14B).
When should you choose GPT-5.6 Luna?
Choose GPT-5.6 Luna when you need Real-time conversational agents, high-volume classification, intent detection, and low-latency interactive apps. and have an infrastructure budget aligned with $0.18/1M tokens.
When should you choose Microsoft Phi-4 (14B)?
Choose Microsoft Phi-4 (14B) when you need High-efficiency local math reasoning, embedded device processing, on-device SLM logic, and low-latency classification. and prioritize Microsoft ecosystem integration at $0.12/1M tokens.