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o1-mini vs Phi-3.5-MoE-instruct
Comprehensive side-by-side LLM comparison
o1-mini leads with 17.1% higher average benchmark score. o1-mini is available on 2 providers. Overall, o1-mini is the stronger choice for coding tasks.
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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-3.5 MoE was created using a mixture-of-experts architecture, designed to provide enhanced capabilities while maintaining efficiency through sparse activation. Built to combine the benefits of larger models with practical computational requirements, it represents Microsoft's exploration of efficient scaling techniques.
20 days newer
Phi-3.5-MoE-instruct
Microsoft
2024-08-23
o1-mini
OpenAI
2024-09-12
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Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
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o1-mini
Average Score:79.2%(+17.1%)
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Phi-3.5-MoE-instruct
Average Score:62.1%
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%(+17.1%)
Providers:2
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Phi-3.5-MoE-instruct
Avg Score:62.1%
Providers:0