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GPT-4.1 nano vs Phi 4
Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. GPT-4.1 nano offers 1.0M more tokens in context window than Phi 4. Both models have similar pricing. 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-4 was introduced as the fourth generation of Microsoft's small language model series, designed to push the boundaries of what compact models can achieve. Built with advanced training techniques and architectural improvements, it demonstrates continued progress in efficient, high-quality language models.
4 months newer
Phi 4
Microsoft
2024-12-12
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
Input:$0.07
Output:$0.14($0.29 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
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GPT-4.1 nano
Average Score:68.3%(+0.3%)
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Phi 4
Average Score:68.0%
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Knowledge Cutoff
Training data recency comparison
GPT-4.1 nano
2024-05-31
Phi 4
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
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GPT-4.1 nano
Avg Score:68.3%(+0.3%)
Providers:1
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Phi 4
Avg Score:68.0%
Providers:1