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Llama 4 Scout vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
Llama 4 Scout leads with 17.8% higher average benchmark score. Llama 4 Scout offers 19.7M more tokens in context window than Phi-3.5-mini-instruct. Both models have similar pricing. Llama 4 Scout supports multimodal inputs. 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-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.
7 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
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-3.5-mini-instruct
Input:$0.10
Output:$0.10($0.18 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 6 common benchmarks
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Llama 4 Scout
Average Score:70.0%(+17.8%)
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Phi-3.5-mini-instruct
Average Score:52.1%
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:70.0%(+17.8%)
Providers:6
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Phi-3.5-mini-instruct
Avg Score:52.1%
Providers:1