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DeepSeek-R1 vs Llama 4 Scout
Comprehensive side-by-side LLM comparison
Llama 4 Scout offers 19.7M more tokens in context window than DeepSeek-R1. Llama 4 Scout is $2.36 cheaper per million tokens. Llama 4 Scout supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-R1 was developed as a reasoning-focused language model, designed to combine chain-of-thought reasoning with reinforcement learning techniques. Built to excel at complex problem-solving through trial-and-error learning and deliberate analytical processes, it demonstrates the power of efficient training methods in open-source model development.
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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.
2 months newer
DeepSeek-R1
DeepSeek
2025-01-20
Llama 4 Scout
Meta
2025-04-05
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Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-R1
Input:$0.55
Output:$2.19
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Llama 4 Scout
Input:$0.08
Output:$0.30($2.36 cheaper)
Performance Metrics
Context window and performance specifications
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-R1
5 providers
DeepInfra
Throughput: 0.9 tok/s
Latency: 0.3ms
DeepSeek
Throughput: 9 tok/s
Latency: 0.3ms
Fireworks
Throughput: 2.1 tok/s
Latency: 0.3ms
Together
Throughput: 4 tok/s
Latency: 0.6ms
ZeroEval
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DeepSeek-R1
Avg Score:0.0%
Providers:5
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Llama 4 Scout
Avg Score:0.0%
Providers:6