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o3-mini vs Phi 4
Comprehensive side-by-side LLM comparison
o3-mini leads with 18.9% higher average benchmark score. o3-mini offers 268.0K more tokens in context window than Phi 4. Phi 4 is $5.29 cheaper per million tokens. Overall, o3-mini is the stronger choice for coding tasks.
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OpenAI
o3-mini was created as an efficient variant of the o3 reasoning model, designed to provide advanced thinking capabilities with reduced computational requirements. Built to make next-generation reasoning accessible to a broader range of applications, it balances analytical depth with practical speed and cost considerations.
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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.
1 month newer
Phi 4
Microsoft
2024-12-12
o3-mini
OpenAI
2025-01-30
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Pricing Comparison
Cost per million tokens (USD)
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o3-mini
Input:$1.10
Output:$4.40
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Phi 4
Input:$0.07
Output:$0.14($5.29 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 7 common benchmarks
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o3-mini
Average Score:78.2%(+18.9%)
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Phi 4
Average Score:59.4%
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Knowledge Cutoff
Training data recency comparison
o3-mini
2023-09-30
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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o3-mini
2 providers
Azure
Throughput: 115 tok/s
Latency: 5.2ms
OpenAI
Throughput: 115 tok/s
Latency: 5.2ms
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o3-mini
Avg Score:78.2%(+18.9%)
Providers:2
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Phi 4
Avg Score:59.4%
Providers:1