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o1-mini vs Phi 4
Comprehensive side-by-side LLM comparison
o1-mini leads with 4.7% higher average benchmark score. o1-mini offers 161.5K more tokens in context window than Phi 4. Phi 4 is $14.79 cheaper per million tokens. Both models have their strengths depending on your specific coding needs.
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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.
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Microsoft
Phi-4 was introduced as the fourth generation of Microsoft's small language model series, designed to push the boundaries of what compact models can achieve. Built with advanced training techniques and architectural improvements, it demonstrates continued progress in efficient, high-quality language models.
3 months newer
o1-mini
OpenAI
2024-09-12
Phi 4
Microsoft
2024-12-12
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Pricing Comparison
Cost per million tokens (USD)
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o1-mini
Input:$3.00
Output:$12.00
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Phi 4
Input:$0.07
Output:$0.14($14.79 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
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o1-mini
Average Score:79.2%(+4.7%)
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Phi 4
Average Score:74.5%
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Knowledge Cutoff
Training data recency comparison
Phi 4
2024-06-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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o1-mini
2 providers
Azure
Throughput: 100 tok/s
Latency: 0.5ms
OpenAI
Throughput: 115 tok/s
Latency: 5.2ms
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o1-mini
Avg Score:79.2%(+4.7%)
Providers:2
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Phi 4
Avg Score:74.5%
Providers:1