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DeepSeek R1 Zero vs Llama 4 Scout
Comprehensive side-by-side LLM comparison
Llama 4 Scout leads with 35.8% higher average benchmark score. 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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DeepSeek
DeepSeek R1 Zero is a language model developed by DeepSeek. It achieves strong performance with an average score of 76.5% across 4 benchmarks. It excels particularly in MATH-500 (95.9%), AIME 2024 (86.7%), GPQA (73.3%). It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents DeepSeek's latest advancement in AI technology.
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Meta
Llama 4 Scout is a multimodal language model developed by Meta. It achieves strong performance with an average score of 67.3% across 12 benchmarks. It excels particularly in DocVQA (94.4%), MGSM (90.6%), ChartQA (88.8%). With a 20.0M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 6 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Meta's latest advancement in AI technology.
2 months newer
DeepSeek R1 Zero
DeepSeek
2025-01-20
Llama 4 Scout
Meta
2025-04-05
Performance Metrics
Context window and performance specifications
Average performance across 14 common benchmarks
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DeepSeek R1 Zero
Average Score:21.9%
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Llama 4 Scout
Average Score:57.7%(+35.8%)
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek R1 Zero
0 providers
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Llama 4 Scout
6 providers
DeepInfra
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DeepSeek R1 Zero
Avg Score:21.9%
Providers:0
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Llama 4 Scout
Avg Score:57.7%(+35.8%)
Providers:6