All modelsLlama 4 Maverick
Meta Llama 4 Maverick is a cutting-edge, natively multimodal AI model from Meta, part of the new Llama 4 family. Built with a Mixture-of-Experts (MoE) architecture, Maverick is designed for advanced text and image understanding, aiming to provide industry-leading performance for a variety of applications at competitive costs.
Intended Use
Developing multimodal assistant applications (image recognition, visual reasoning, captioning, Q&A about images)
Code generation and technical tasks
General-purpose text generation and chat
Enterprise applications requiring multimodal data processing
Research and development in AI
Performance
Llama 4 Maverick features a Mixture-of-Experts architecture with 17 billion active parameters (from a total of 400 billion), contributing to its efficiency and performance. It is natively multimodal, incorporating early fusion of text and image data during training, which allows for seamless understanding and reasoning across modalities. The model supports a context window of up to 1 million tokens, enabling processing of extensive documents and complex inputs.
Benchmark results highlight Maverick's strong capabilities, particularly in multimodal understanding. It has achieved scores of 59.6 on MMLU Pro, 90.0 on ChartQA, 94.4 on DocVQA, 73.4 on MMMU, and 73.7 on MathVista. In coding, it scored 43.4 on LiveCodeBench (specific timeframe). On the LMSYS Chatbot Arena, an experimental chat version of Maverick reportedly achieved an ELO score of 1417, placing it competitively among top models.
Maverick is designed for efficient deployment, capable of running on a single H100 DGX host, with options for distributed inference for larger-scale needs. Its MoE architecture helps balance powerful performance with inference efficiency.
Limitations
EU restriction on vision: Due to regulatory considerations, the use of the vision capabilities for individuals domiciled in, or companies with a principal place of business in, the European Union is not granted under the Llama 4 Community License.
Dedicated reasoning focus: While capable of reasoning, Llama 4 Maverick is not specifically positioned as a dedicated reasoning model in the same vein as some models optimized solely for complex, multi-step logical deduction.
Long context consistency: Despite a large context window, some initial testing has suggested potential for inconsistent performance or degraded results with exceptionally long prompts.
Image input testing: The model has been primarily tested for image understanding with up to 5 input images; performance with a larger number of images may vary, and developers are advised to perform additional testing for such use cases.
Benchmark interpretation: As with many frontier models, there have been discussions regarding the interpretation and representativeness of benchmark scores compared to real-world performance across all possible tasks.
Memory intensity: The Mixture-of-Experts architecture, while efficient for inference, means the full model still requires significant memory to load.
Citation
Information gathered from Meta's official Llama website, announcements, technical documentation (including the Llama 4 Community License), and third-party analyses and benchmark reports.