OpenAI o1 vs Microsoft Phi-4 (14B)
Live Unit Economics, TTFT Latency & Reasoning Benchmark Analysis
Which model is better: OpenAI o1 or Microsoft Phi-4 (14B)?
Verified Performance & Unit Economics Delta
Daily synchronized metrics from synthetic latency endpoints and benchmark evaluations.
| Metric / Dimension | OpenAI o1 | Microsoft Phi-4 (14B) | Net Delta / Advantage |
|---|---|---|---|
| Input Cost / 1M Tokens | $15.00 | $0.12 | Microsoft Phi-4 (14B) is 125.0x cheaper |
| Output Cost / 1M Tokens | $60.00 | $0.36 | Microsoft Phi-4 (14B) (16566.7% lower) |
| Avg TTFT Response Latency | 850ms | 110ms | Microsoft Phi-4 (14B) (+740ms faster) |
| SWE-bench Verified (Coding) | 48.9% | 42.1% | OpenAI o1 (+6.8% lead) |
| Inference Value Score (IVR) | 21 / 100 | 99.9 / 100 | Microsoft Phi-4 (14B) (+78.9 pts) |
Interactive Monthly Token Economics & ROI Forecaster
Model your expected production workload across prompt (input) and completion (output) tokens.
Estimated Monthly Spend
$15/1M in · $60/1M out
$0.12/1M in · $0.36/1M out
When to Choose OpenAI o1
Choose OpenAI o1 when you need Complex PhD-level multi-step science research, formal algorithm synthesis, and high-stakes reasoning pipelines. and have an infrastructure budget aligned with $15.00/1M 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: OpenAI o1 or Microsoft Phi-4 (14B)?
Microsoft Phi-4 (14B) is cheaper at $0.12/1M input tokens compared to $15.00/1M for OpenAI o1.
Which model has lower latency: OpenAI o1 or Microsoft Phi-4 (14B)?
Microsoft Phi-4 (14B) delivers faster response times with an average TTFT of 110ms vs 850ms for OpenAI o1.
When should you choose OpenAI o1?
Choose OpenAI o1 when you need Complex PhD-level multi-step science research, formal algorithm synthesis, and high-stakes reasoning pipelines. and have an infrastructure budget aligned with $15.00/1M 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.