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Gemma 3 12B vs Phi 4 Mini
Comprehensive side-by-side LLM comparison
Gemma 3 12B leads with 13.5% higher average benchmark score. Gemma 3 12B supports multimodal inputs. Overall, Gemma 3 12B is the stronger choice for coding tasks.
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Gemma 3 12B is a multimodal language model developed by Google. It achieves strong performance with an average score of 62.5% across 26 benchmarks. It excels particularly in GSM8k (94.4%), IFEval (88.9%), DocVQA (87.1%). 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 4 Mini is a language model developed by Microsoft. It achieves strong performance with an average score of 65.4% across 17 benchmarks. It excels particularly in GSM8k (88.6%), ARC-C (83.7%), BoolQ (81.2%). It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Microsoft's latest advancement in AI technology.
1 month newer
Phi 4 Mini
Microsoft
2025-02-01
Gemma 3 12B
2025-03-12
Performance Metrics
Context window and performance specifications
Average performance across 38 common benchmarks
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Gemma 3 12B
Average Score:42.7%(+13.5%)
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Phi 4 Mini
Average Score:29.2%
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Knowledge Cutoff
Training data recency comparison
Phi 4 Mini
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 3 12B
1 providers
DeepInfra
Throughput: 33 tok/s
Latency: 0.2ms
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Phi 4 Mini
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Gemma 3 12B
Avg Score:42.7%(+13.5%)
Providers:1
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Phi 4 Mini
Avg Score:29.2%
Providers:0