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Llama 3.2 3B Instruct vs Phi 4 Reasoning
Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. Both models have their strengths depending on your specific coding needs.
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Meta
Llama 3.2 3B Instruct is a language model developed by Meta. The model shows competitive results across 15 benchmarks. It excels particularly in NIH/Multi-needle (84.7%), ARC-C (78.6%), GSM8k (77.7%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. Released in 2024, it represents Meta's latest advancement in AI technology.
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Microsoft
Phi 4 Reasoning is a language model developed by Microsoft. It achieves strong performance with an average score of 75.1% across 11 benchmarks. It excels particularly in FlenQA (97.7%), HumanEval+ (92.9%), IFEval (83.4%). 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
Llama 3.2 3B Instruct
Meta
2024-09-25
Phi 4 Reasoning
Microsoft
2025-04-30
Performance Metrics
Context window and performance specifications
Average performance across 24 common benchmarks
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Llama 3.2 3B Instruct
Average Score:34.8%(+0.3%)
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Phi 4 Reasoning
Average Score:34.4%
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Knowledge Cutoff
Training data recency comparison
Phi 4 Reasoning
2025-03-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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Llama 3.2 3B Instruct
1 providers
DeepInfra
Throughput: 171.5 tok/s
Latency: 0.24ms
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Phi 4 Reasoning
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Llama 3.2 3B Instruct
Avg Score:34.8%(+0.3%)
Providers:1
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Phi 4 Reasoning
Avg Score:34.4%
Providers:0