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o4-mini vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
o4-mini leads with 51.0% higher average benchmark score. o4-mini offers 44.0K more tokens in context window than Phi-3.5-mini-instruct. Phi-3.5-mini-instruct is $5.30 cheaper per million tokens. o4-mini supports multimodal inputs. Overall, o4-mini is the stronger choice for coding tasks.
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OpenAI
o4-mini was created as part of the next generation of OpenAI's reasoning models, designed to continue advancing the balance between analytical capability and operational efficiency. Built to bring cutting-edge reasoning techniques to applications requiring quick turnaround, it represents the evolution of compact reasoning-focused models.
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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.
7 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
o4-mini
OpenAI
2025-04-16
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Pricing Comparison
Cost per million tokens (USD)
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o4-mini
Input:$1.10
Output:$4.40
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Phi-3.5-mini-instruct
Input:$0.10
Output:$0.10($5.30 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
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o4-mini
Average Score:81.4%(+51.0%)
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Phi-3.5-mini-instruct
Average Score:30.4%
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Knowledge Cutoff
Training data recency comparison
o4-mini
2024-05-31
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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o4-mini
1 providers
OpenAI
Throughput: 115 tok/s
Latency: 5.2ms
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Phi-3.5-mini-instruct
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o4-mini
Avg Score:81.4%(+51.0%)
Providers:1
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Phi-3.5-mini-instruct
Avg Score:30.4%
Providers:1