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GPT-4.1 nano vs Qwen2.5 7B Instruct
Comprehensive side-by-side LLM comparison
GPT-4.1 nano leads with 8.6% higher average benchmark score. GPT-4.1 nano offers 941.1K more tokens in context window than Qwen2.5 7B 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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Alibaba Cloud / Qwen Team
Qwen 2.5 7B was created as an efficient instruction-tuned model, designed to provide capable performance with just 7 billion parameters. Built for applications requiring reliable language understanding with minimal computational overhead, it serves as an accessible entry point to the Qwen 2.5 family.
6 months newer
Qwen2.5 7B Instruct
Alibaba Cloud / Qwen Team
2024-09-19
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($0.10 cheaper)
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Qwen2.5 7B Instruct
Input:$0.30
Output:$0.30
Performance Metrics
Context window and performance specifications
Average performance across 2 common benchmarks
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GPT-4.1 nano
Average Score:62.4%(+8.6%)
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Qwen2.5 7B Instruct
Average Score:53.8%
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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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Qwen2.5 7B Instruct
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GPT-4.1 nano
Avg Score:62.4%(+8.6%)
Providers:1
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Qwen2.5 7B Instruct
Avg Score:53.8%
Providers:1