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Phi-4-multimodal-instruct vs Qwen2.5-Omni-7B
Comprehensive side-by-side LLM comparison
Qwen2.5-Omni-7B leads with 3.3% 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-Omni 7B was created as a multimodal model supporting text, audio, and other modalities, designed to provide integrated understanding across diverse input types. Built with 7 billion parameters for efficient omni-modal processing, it extends AI capabilities beyond traditional text-only or vision-language boundaries.
1 month newer
Phi-4-multimodal-instruct
Microsoft
2025-02-01
Qwen2.5-Omni-7B
Alibaba Cloud / Qwen Team
2025-03-27
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Performance Metrics
Context window and performance specifications
Average performance across 7 common benchmarks
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Phi-4-multimodal-instruct
Average Score:69.8%
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Qwen2.5-Omni-7B
Average Score:73.1%(+3.3%)
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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-Omni-7B
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Phi-4-multimodal-instruct
Avg Score:69.8%
Providers:1
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Qwen2.5-Omni-7B
Avg Score:73.1%(+3.3%)
Providers:0