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Llama 4 Scout vs Phi 4
Comprehensive side-by-side LLM comparison
Phi 4 leads with 4.1% higher average benchmark score. Llama 4 Scout offers 20.0M more tokens in context window than Phi 4. Both models have similar pricing. Llama 4 Scout supports multimodal inputs. Llama 4 Scout is available on 6 providers. Both models have their strengths depending on your specific coding needs.
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Meta
Llama 4 Scout was created as an exploratory variant in the Llama 4 family, designed to investigate new architectures and optimization strategies. Built as part of Meta's commitment to advancing open-source AI, it serves as a testbed for innovations that may inform future model releases.
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Microsoft
Phi-4 was introduced as the fourth generation of Microsoft's small language model series, designed to push the boundaries of what compact models can achieve. Built with advanced training techniques and architectural improvements, it demonstrates continued progress in efficient, high-quality language models.
3 months newer
Phi 4
Microsoft
2024-12-12
Llama 4 Scout
Meta
2025-04-05
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Pricing Comparison
Cost per million tokens (USD)
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Llama 4 Scout
Input:$0.08
Output:$0.30
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Phi 4
Input:$0.07
Output:$0.14($0.17 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 5 common benchmarks
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Llama 4 Scout
Average Score:70.4%
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Phi 4
Average Score:74.5%(+4.1%)
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Knowledge Cutoff
Training data recency comparison
Phi 4
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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Llama 4 Scout
6 providers
DeepInfra
Throughput: 76.1 tok/s
Latency: 0.31ms
Fireworks
Throughput: 116.1 tok/s
Latency: 0.53ms
Groq
Throughput: 776.1 tok/s
Latency: 1.08ms
Lambda
Throughput: 139.7 tok/s
Latency: 0.43ms
Novita
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Llama 4 Scout
Avg Score:70.4%
Providers:6
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Phi 4
Avg Score:74.5%(+4.1%)
Providers:1