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GPT-4.1 nano vs Qwen2.5 VL 7B Instruct
Comprehensive side-by-side LLM comparison
Qwen2.5 VL 7B Instruct leads with 3.2% higher average benchmark score. 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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Alibaba Cloud / Qwen Team
Qwen2.5-VL 7B was developed as an efficient vision-language model, designed to provide multimodal understanding with minimal computational requirements. Built with 7 billion parameters for integrated visual and textual processing, it serves applications requiring practical vision-language capabilities with constrained resources.
2 months newer
Qwen2.5 VL 7B Instruct
Alibaba Cloud / Qwen Team
2025-01-26
GPT-4.1 nano
OpenAI
2025-04-14
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Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
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GPT-4.1 nano
Average Score:55.4%
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Qwen2.5 VL 7B Instruct
Average Score:58.6%(+3.2%)
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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 VL 7B Instruct
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GPT-4.1 nano
Avg Score:55.4%
Providers:1
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Qwen2.5 VL 7B Instruct
Avg Score:58.6%(+3.2%)
Providers:0