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DeepSeek-V3 vs Phi 4 Mini Reasoning
Comprehensive side-by-side LLM comparison
DeepSeek-V3 leads with 1.3% higher average benchmark score. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-V3 was introduced as a major architectural advancement, developed with 671B mixture-of-experts parameters and trained on 14.8 trillion tokens. Built to be three times faster than V2 while maintaining open-source availability, it demonstrates competitive performance against frontier closed-source models and represents a significant leap in efficient large-scale model design.
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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.
4 months newer
DeepSeek-V3
DeepSeek
2024-12-25
Phi 4 Mini Reasoning
Microsoft
2025-04-30
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Performance Metrics
Context window and performance specifications
Average performance across 2 common benchmarks
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DeepSeek-V3
Average Score:74.6%(+1.3%)
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Phi 4 Mini Reasoning
Average Score:73.3%
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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
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DeepSeek-V3
1 providers
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
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Phi 4 Mini Reasoning
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DeepSeek-V3
Avg Score:74.6%(+1.3%)
Providers:1
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Phi 4 Mini Reasoning
Avg Score:73.3%
Providers:0