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GPT-4.1 nano vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
GPT-4.1 nano leads with 14.2% higher average benchmark score. GPT-4.1 nano offers 824.3K more tokens in context window than Phi-3.5-mini-instruct. Both models have similar pricing. GPT-4.1 nano supports multimodal inputs. Overall, GPT-4.1 nano is the stronger choice for coding tasks.
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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 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
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-3.5-mini-instruct
Input:$0.10
Output:$0.10($0.30 cheaper)
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%(+14.2%)
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Phi-3.5-mini-instruct
Average Score:51.6%
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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-mini-instruct
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GPT-4.1 nano
Avg Score:65.8%(+14.2%)
Providers:1
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Phi-3.5-mini-instruct
Avg Score:51.6%
Providers:1