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Gemma 3 4B vs Phi-3.5-vision-instruct
Comprehensive side-by-side LLM comparison
Gemma 3 4B leads with 23.9% higher average benchmark score. Overall, Gemma 3 4B is the stronger choice for coding tasks.
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Gemma 3 4B is a multimodal language model developed by Google. The model shows competitive results across 26 benchmarks. It excels particularly in IFEval (90.2%), GSM8k (89.2%), DocVQA (75.8%). It supports a 262K 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 Google's latest advancement in AI technology.
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Microsoft
Phi-3.5-vision-instruct is a multimodal language model developed by Microsoft. It achieves strong performance with an average score of 68.3% across 9 benchmarks. It excels particularly in ScienceQA (91.3%), POPE (86.1%), MMBench (81.9%). 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 2024, it represents Microsoft's latest advancement in AI technology.
6 months newer
Phi-3.5-vision-instruct
Microsoft
2024-08-23
Gemma 3 4B
2025-03-12
Performance Metrics
Context window and performance specifications
Average performance across 32 common benchmarks
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Gemma 3 4B
Average Score:43.1%(+23.9%)
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Phi-3.5-vision-instruct
Average Score:19.2%
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Knowledge Cutoff
Training data recency comparison
Gemma 3 4B
2024-08-01
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 3 4B
1 providers
DeepInfra
Throughput: 33 tok/s
Latency: 0.2ms
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Phi-3.5-vision-instruct
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Gemma 3 4B
Avg Score:43.1%(+23.9%)
Providers:1
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Phi-3.5-vision-instruct
Avg Score:19.2%
Providers:0