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Gemma 3 4B vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
Gemma 3 4B leads with 4.6% higher average benchmark score. Gemma 3 4B offers 6.1K more tokens in context window than Phi-3.5-mini-instruct. Both models have similar pricing. Gemma 3 4B supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
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Gemma 3 4B was developed as a compact yet capable open-source model, designed to strike a balance between performance and resource efficiency. Built with 4 billion parameters and instruction tuning, it provides a practical option for applications requiring moderate capability with manageable computational costs.
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Microsoft
Phi-3.5 Mini was developed by Microsoft as a small language model designed to deliver impressive performance despite its compact size. Built with efficiency in mind, it demonstrates that capable language understanding and generation can be achieved with fewer parameters, making AI more accessible for edge and resource-constrained deployments.
6 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
Gemma 3 4B
2025-03-12
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Pricing Comparison
Cost per million tokens (USD)
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Gemma 3 4B
Input:$0.02
Output:$0.04($0.14 cheaper)
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Phi-3.5-mini-instruct
Input:$0.10
Output:$0.10
Performance Metrics
Context window and performance specifications
Average performance across 7 common benchmarks
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Gemma 3 4B
Average Score:63.7%(+4.6%)
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Phi-3.5-mini-instruct
Average Score:59.1%
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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-mini-instruct
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Gemma 3 4B
Avg Score:63.7%(+4.6%)
Providers:1
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Phi-3.5-mini-instruct
Avg Score:59.1%
Providers:1