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Phi-4-multimodal-instruct vs Qwen2.5 VL 7B Instruct
Comprehensive side-by-side LLM comparison
Qwen2.5 VL 7B Instruct leads with 3.8% higher average benchmark score. Both models have their strengths depending on your specific coding needs.
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Microsoft
Phi-4 Multimodal was created to handle multiple input modalities including text, images, and potentially other formats. Built to extend Phi-4's efficiency into multimodal applications, it demonstrates that compact models can successfully integrate diverse information types.
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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.
6 days newer
Qwen2.5 VL 7B Instruct
Alibaba Cloud / Qwen Team
2025-01-26
Phi-4-multimodal-instruct
Microsoft
2025-02-01
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Performance Metrics
Context window and performance specifications
Average performance across 8 common benchmarks
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Phi-4-multimodal-instruct
Average Score:73.5%
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Qwen2.5 VL 7B Instruct
Average Score:77.3%(+3.8%)
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Knowledge Cutoff
Training data recency comparison
Phi-4-multimodal-instruct
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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Phi-4-multimodal-instruct
1 providers
DeepInfra
Throughput: 25 tok/s
Latency: 0.5ms
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Qwen2.5 VL 7B Instruct
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Phi-4-multimodal-instruct
Avg Score:73.5%
Providers:1
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Qwen2.5 VL 7B Instruct
Avg Score:77.3%(+3.8%)
Providers:0