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GPT-4.1 nano vs Phi-4-multimodal-instruct
Comprehensive side-by-side LLM comparison
Phi-4-multimodal-instruct leads with 2.9% higher average benchmark score. GPT-4.1 nano offers 824.3K more tokens in context window than Phi-4-multimodal-instruct. Both models have similar pricing. 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-4 Multimodal was created to handle multiple input modalities including text, images, and potentially other formats. Built to extend Phi-4's efficiency into multimodal applications, it demonstrates that compact models can successfully integrate diverse information types.
2 months newer
Phi-4-multimodal-instruct
Microsoft
2025-02-01
GPT-4.1 nano
OpenAI
2025-04-14
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Pricing Comparison
Cost per million tokens (USD)
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GPT-4.1 nano
Input:$0.10
Output:$0.40
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Phi-4-multimodal-instruct
Input:$0.05
Output:$0.10($0.35 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 2 common benchmarks
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GPT-4.1 nano
Average Score:55.8%
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Phi-4-multimodal-instruct
Average Score:58.8%(+2.9%)
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Knowledge Cutoff
Training data recency comparison
GPT-4.1 nano
2024-05-31
Phi-4-multimodal-instruct
2024-06-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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GPT-4.1 nano
1 providers
OpenAI
Throughput: 200 tok/s
Latency: 2ms
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Phi-4-multimodal-instruct
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GPT-4.1 nano
Avg Score:55.8%
Providers:1
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Phi-4-multimodal-instruct
Avg Score:58.8%(+2.9%)
Providers:1