Llama 4 Scout
Multimodal
Zero-eval
#2TydiQA
by Meta
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About
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.
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Pricing Range
Input (per 1M)$0.08 -$0.18
Output (per 1M)$0.30 -$0.60
Providers6
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Timeline
AnnouncedApr 5, 2025
ReleasedApr 5, 2025
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Specifications
Training Tokens40.0T
Capabilities
Multimodal
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License & Family
License
Llama 4 Community License Agreement
Performance Overview
Performance metrics and category breakdown
Overall Performance
12 benchmarks
Average Score
67.3%
Best Score
94.4%
High Performers (80%+)
3Performance Metrics
Max Context Window
20.0MAvg Throughput
214.1 tok/sAvg Latency
1ms+
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All Benchmark Results for Llama 4 Scout
Complete list of benchmark scores with detailed information
| DocVQA | multimodal | 0.94 | 94.4% | Self-reported | |
| MGSM | text | 0.91 | 90.6% | Self-reported | |
| ChartQA | multimodal | 0.89 | 88.8% | Self-reported | |
| MMLU | text | 0.80 | 79.6% | Self-reported | |
| MMLU-Pro | text | 0.74 | 74.3% | Self-reported | |
| MathVista | multimodal | 0.71 | 70.7% | Self-reported | |
| MMMU | multimodal | 0.69 | 69.4% | Self-reported | |
| MBPP | text | 0.68 | 67.8% | Self-reported | |
| GPQA | text | 0.57 | 57.2% | Self-reported | |
| MATH | text | 0.50 | 50.3% | Self-reported |
Showing 1 to 10 of 12 benchmarks