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GPT-5 nano vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
GPT-5 nano leads with 40.8% higher average benchmark score. GPT-5 nano offers 272.0K more tokens in context window than Phi-3.5-mini-instruct. Both models have similar pricing. GPT-5 nano supports multimodal inputs. Overall, GPT-5 nano is the stronger choice for coding tasks.
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OpenAI
GPT-5 Nano was developed as the most compact variant in the GPT-5 family, designed for deployment in resource-constrained environments and edge computing scenarios. Built to bring next-generation AI capabilities to devices and applications where latency and efficiency are paramount, it distills GPT-5 innovations into a minimal footprint.
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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.
11 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
GPT-5 nano
OpenAI
2025-08-07
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Pricing Comparison
Cost per million tokens (USD)
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GPT-5 nano
Input:$0.05
Output:$0.40
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Phi-3.5-mini-instruct
Input:$0.10
Output:$0.10($0.25 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
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GPT-5 nano
Average Score:71.2%(+40.8%)
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Phi-3.5-mini-instruct
Average Score:30.4%
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Knowledge Cutoff
Training data recency comparison
GPT-5 nano
2024-05-30
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-5 nano
2 providers
OpenAI
Throughput: 500 tok/s
Latency: 0.3ms
ZeroEval
Throughput: 500 tok/s
Latency: 0.3ms
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GPT-5 nano
Avg Score:71.2%(+40.8%)
Providers:2
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Phi-3.5-mini-instruct
Avg Score:30.4%
Providers:1