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Llama 4 Scout vs Phi-4-multimodal-instruct
Comprehensive side-by-side LLM comparison
Llama 4 Scout leads with 7.8% higher average benchmark score. Llama 4 Scout offers 19.7M more tokens in context window than Phi-4-multimodal-instruct. Both models have similar pricing. Llama 4 Scout is available on 6 providers. Overall, Llama 4 Scout is the stronger choice for coding tasks.
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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 Multimodal was created to handle multiple input modalities including text, images, and potentially other formats. Built to extend Phi-4's efficiency into multimodal applications, it demonstrates that compact models can successfully integrate diverse information types.
2 months newer
Phi-4-multimodal-instruct
Microsoft
2025-02-01
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-multimodal-instruct
Input:$0.05
Output:$0.10($0.23 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 4 common benchmarks
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Llama 4 Scout
Average Score:80.8%(+7.8%)
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Phi-4-multimodal-instruct
Average Score:73.0%
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Knowledge Cutoff
Training data recency comparison
Phi-4-multimodal-instruct
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:80.8%(+7.8%)
Providers:6
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Phi-4-multimodal-instruct
Avg Score:73.0%
Providers:1