+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
o1-mini vs Phi 4 Mini Reasoning
Comprehensive side-by-side LLM comparison
o1-mini leads with 1.7% higher average benchmark score. o1-mini is available on 2 providers. Both models have their strengths depending on your specific coding needs.
+
+
+
+
OpenAI
o1-mini was created as a faster, more cost-effective reasoning model, designed to bring extended thinking capabilities to applications with tighter latency and budget constraints. Built to excel particularly in coding and STEM reasoning while maintaining affordability, it provides a more accessible entry point to reasoning-enhanced AI assistance.
+
+
+
+
Microsoft
Phi-4 Mini Reasoning was developed to incorporate extended thinking capabilities into the ultra-compact Phi-4 Mini architecture. Built to demonstrate that reasoning enhancements can be applied even to very small models, it brings analytical depth to resource-constrained environments.
7 months newer
o1-mini
OpenAI
2024-09-12
Phi 4 Mini Reasoning
Microsoft
2025-04-30
+
+
+
+
+
+
+
+
+
+
+
+
Performance Metrics
Context window and performance specifications
Average performance across 2 common benchmarks
+
+
+
+
o1-mini
Average Score:75.0%(+1.7%)
+
+
+
+
Phi 4 Mini Reasoning
Average Score:73.3%
+
+
+
+
Knowledge Cutoff
Training data recency comparison
Phi 4 Mini Reasoning
2025-02-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
+
+
+
+
o1-mini
2 providers
Azure
Throughput: 100 tok/s
Latency: 0.5ms
OpenAI
Throughput: 115 tok/s
Latency: 5.2ms
+
+
+
+
+
+
+
+
o1-mini
Avg Score:75.0%(+1.7%)
Providers:2
+
+
+
+
Phi 4 Mini Reasoning
Avg Score:73.3%
Providers:0