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Gemma 2 27B vs Phi-4-multimodal-instruct
Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. Phi-4-multimodal-instruct supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
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Gemma 2 27B is a language model developed by Google. It achieves strong performance with an average score of 69.1% across 16 benchmarks. It excels particularly in ARC-E (88.6%), HellaSwag (86.4%), BoolQ (84.8%). It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Google's latest advancement in AI technology.
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Microsoft
Phi-4-multimodal-instruct is a multimodal language model developed by Microsoft. It achieves strong performance with an average score of 72.0% across 15 benchmarks. It excels particularly in ScienceQA Visual (97.5%), DocVQA (93.2%), MMBench (86.7%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Microsoft's latest advancement in AI technology.
7 months newer
Gemma 2 27B
2024-06-27
Phi-4-multimodal-instruct
Microsoft
2025-02-01
Performance Metrics
Context window and performance specifications
Average performance across 31 common benchmarks
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Gemma 2 27B
Average Score:35.7%(+0.8%)
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Phi-4-multimodal-instruct
Average Score:34.8%
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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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Gemma 2 27B
0 providers
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Phi-4-multimodal-instruct
1 providers
DeepInfra
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Gemma 2 27B
Avg Score:35.7%(+0.8%)
Providers:0
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Phi-4-multimodal-instruct
Avg Score:34.8%
Providers:1