Meta has recently introduced the Llama 3.2 version to the Llama LLM family, which follows the release of Llama 3.1 405B – a model lauded for being one of the most budget-friendly and advanced open-source foundation models. This latest offering by Meta comes in 1B and 3B sizes that are multilingual text-only and 11B and 90B sizes that take both text and image inputs and output text. But how well does 3.2 compare to its predecessor – Llama 3.1 and its competitor GPT-4o? That’s what we discuss in this blog, comparing the three models for tasks such as writing and coding to see which model stands out on top.
But before we get into the comparison, let’s first see what Llama 3.2 is about and how it builds upon its predecessors.
Llama 3.2 Overview
(courtesy: Meta)
Llama 3.2 adds to the solid foundation laid by Llama 3.1 405B and introduces a few new enhancements. These improvements make it a more adaptable tool overall, particularly in edge AI and vision tasks. Here’s a summary of the key features of Llama 3.2:
- Model Variants: Llama 3.2 offers two classes of model options with four different parameters, from lightweight text-only versions with 1B and 3B parameters to larger vision models with 11B and 90B parameters. This variety gives creators and developers the flexibility to choose models that fit their specific computational resources and application needs.
- Multimodal Capabilities: One of the most exciting updates is the introduction of vision models (11B and 90B parameters). These allow Llama 3.2 to handle image-related tasks, such as interpreting charts, graphs, and images alongside text prompts. This is particularly valuable for applications like document analysis and visual grounding.
(courtesy: Meta)
- Local Processing: The lightweight models are designed for edge devices, meaning they can run locally without sending data to the cloud. This is ideal for tasks that require real-time processing and high data privacy.
- Improved Performance: Early tests show that Llama 3.2’s vision models are competitive with top (mini) models like Claude 3 Haiku and GPT-4o-mini in tasks like image recognition. The smaller models have also improved instruction-following and summarization abilities compared to earlier versions. (chart below for reference)
(courtesy: Meta)
Key Improvements from Llama 3.1
(courtesy: Meta)
Llama 3.2 brings numerous upgrades over Llama 3.1 (you can test Llama 3.1 405B with Bind AI Copilot now):
- Enhanced Model Architecture: The vision models have been re-engineered to handle image reasoning more effectively. The new design integrates pre-trained image encoders into the language model, allowing it to manage visual tasks without sacrificing its text-only capabilities.
- Efficiency Boost: Llama 3.2 uses pruning and knowledge distillation techniques to make its smaller models (1B and 3B parameters) more resource-efficient while maintaining high performance. This ensures that even developers with limited computational power can still benefit from its advanced features.
- Greater Accessibility: With models that can run on mobile devices and edge platforms, Llama 3.2 lowers the barrier to entry for developers. This accessibility makes it easier to create cutting-edge applications without needing vast amounts of computing power.
Llama 3.2 vs Llama 3.1 vs GPT-4o
Llama 3.2 shows a decent performance boost over its predecessor Llama 3.1 for some metrics, but compared to GPT-4o, it lags. Perhaps a comparison with GPT-4o mini will be more suitable in the future. Here’s a table showcasing the use cases and capabilities of Llama 3.2 vs 3.1 and GPT-4o:
| Feature | Llama 3.2 | Llama 3.1 | GPT-4o |
| Release Date | September 2024 | July 2024 | March 2024 |
| Parameters | 1B, 3B, 11B, and 90B | 405 billion | Not explicitly stated; estimated at over 200 billion |
| Context Length | Up to 128,000 tokens | Up to 128,000 tokens | Up to 128,000 tokens |
| Multimodal Capabilities | Yes (text + vision) | Text only | Yes (text + audio + image + video) |
| Voice Interaction | Yes | No | Yes |
| Deployment Options | Edge devices (1B and 3B), cloud | Primarily cloud-based | Primarily cloud-based |
| Performance Benchmarks | Competitive with Claude and GPT-4o-mini on various tasks | Strong in text processing | Strong in text generation; excels in real-time interactions |
| Training Data | Enhanced with multimodal data | Extensive multilingual training data | Extensive training across diverse datasets |
| Safety Features | Improved Llama Guard for multimodal tasks | Llama Guard 3, Prompt Guard | Built-in safety features for content moderation |
| Use Cases | Edge computing, image recognition, voice applications | Research, commercial applications | Customer service, content creation, real-time translations |
| Accessibility | Open source | Open source | Proprietary; limited access for some features |
| Speed of Response | Low-latency on edge devices | High | Approximately 232-320 milliseconds |
| Key Innovations | Multimodal functionality and voice integration | Extended context length | Omni-input capabilities (text, audio, image) |
Multimodal Capabilities
One of the standout features of Llama 3.2 is its strong multimodal capabilities. Thanks to the addition of vision models, it can understand and respond to queries that involve both text and images. This flexibility makes it useful in various fields, from document analysis to interactive storytelling.
In contrast, GPT-4o, while excellent in text generation, lags behind Llama 3.2 when it comes to multimodal tasks. Its ability to handle both text and images is not as advanced, limiting its applications in situations where visual content is crucial.
Deployment Flexibility
(courtesy: Meta)
Llama 3.2 is built for flexibility, particularly when it comes to edge computing. It’s designed to run on local devices, making it suitable for applications that need quick, real-time responses without sacrificing data privacy.
GPT-4o, on the other hand, relies heavily on cloud-based infrastructure. While this works for many use cases, it may not be ideal for situations that demand immediate results or involve sensitive data.
Performance Metrics
Initial evaluations suggest that Llama 3.2’s vision models hold their own against top AI models in tasks like image recognition and visual understanding. While GPT-4o remains a powerhouse in text generation across a wide range of topics, Llama 3.2’s ability to integrate visual reasoning gives it an edge in scenarios where images are involved.
Final Thoughts
Llama 3.2 is a decent step forward in Meta’s AI LLM offerings, building on the strengths of Llama 3.1 and expanding its capabilities for both edge devices and complex multimodal tasks. Meta believes that providing these models to the open-source community is just the beginning, and it’s equally important that developers have access to the tools and guidance necessary to build with Llama 3.2 responsibly.
To support this, Meta is offering new resources and tools while continuing to update best practices in the Responsible Use Guide. To try and test Llama 3.1 and other advanced models like GPT-4o and Claude 3.5 Sonnet, head over to Bind AI Copilot and start your 7-day premium free trial today.