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GPT-4.1 nano vs Phi-3.5-MoE-instruct
Comprehensive side-by-side LLM comparison
GPT-4.1 nano leads with 3.9% higher average benchmark score. GPT-4.1 nano supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
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OpenAI
GPT-4.1 Nano was developed as the smallest and most efficient variant in the GPT-4.1 family, designed for applications requiring minimal latency and resource usage. Built to enable AI capabilities on edge devices and resource-constrained environments, it distills GPT-4.1 capabilities into an ultra-compact form factor.
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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.
7 months newer
Phi-3.5-MoE-instruct
Microsoft
2024-08-23
GPT-4.1 nano
OpenAI
2025-04-14
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Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
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GPT-4.1 nano
Average Score:65.8%(+3.9%)
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Phi-3.5-MoE-instruct
Average Score:61.9%
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Knowledge Cutoff
Training data recency comparison
GPT-4.1 nano
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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GPT-4.1 nano
1 providers
OpenAI
Throughput: 200 tok/s
Latency: 2ms
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Phi-3.5-MoE-instruct
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GPT-4.1 nano
Avg Score:65.8%(+3.9%)
Providers:1
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Phi-3.5-MoE-instruct
Avg Score:61.9%
Providers:0