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o1-mini vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
o1-mini leads with 25.1% higher average benchmark score. Phi-3.5-mini-instruct offers 62.5K more tokens in context window than o1-mini. Phi-3.5-mini-instruct is $14.80 cheaper per million tokens. 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 Mini was developed by Microsoft as a small language model designed to deliver impressive performance despite its compact size. Built with efficiency in mind, it demonstrates that capable language understanding and generation can be achieved with fewer parameters, making AI more accessible for edge and resource-constrained deployments.
20 days newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
o1-mini
OpenAI
2024-09-12
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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-3.5-mini-instruct
Input:$0.10
Output:$0.10($14.80 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
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o1-mini
Average Score:79.2%(+25.1%)
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Phi-3.5-mini-instruct
Average Score:54.1%
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%(+25.1%)
Providers:2
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Phi-3.5-mini-instruct
Avg Score:54.1%
Providers:1