Meta has recently introduced the Llama 3.1 405B, which they believe is the world’s most capable open-source foundation model, trained on 15 trillion tokens. Displaying impressive capabilities in code generation, synthetic data generation, and model distillation, 405B offers developers powerful tools for accelerating development processes. But is it better than Claude 3.5 Sonnet or GPT-4o for AI code generation? In many technical aspects, it is. However, raw technical performance isn’t everything when discussing AI code generation models. There’s more to it.
This article compares Llama 3.1 405B with Claude 3.5 Sonnet and GPT-4o across performance metrics, feature sets, availability, and pricing. We will share the latest community test results and developers’ thoughts on each model. Read till the end, and you’ll know everything about Llama 3.1 405B.
Overview of Llama 3.1 405B
Credit: Meta AI
Building upon the successes of its predecessor Llama 3, the Llama 3.1 family advances AI code generation technology. One of its standout features is the expanded context length of 128K tokens. It allows a deeper understanding of complex prompts and long-form text summarization. The 405B variant of Llama 3.1 is particularly noteworthy, showcasing flexibility and performance that rival many proprietary models like GPT-4 and Claude 3/3.5 family. As part of this latest release they are also introducing upgraded versions of the 8B and 70B models.
Meta has also updated its license to allow developers to use outputs from Llama models to enhance other models.
Llama 3.1 Architecture and Features
Llama 3.1 405B is Meta’s largest model to date. It uses a standard decoder-only transformer architecture with scalability and stability in mind. This model has undergone iterative post-training procedures, enhancing its performance across various tasks, including code generation.
It supports multiple languages and can handle complex tasks like synthetic data generation and model distillation, making it a versatile tool for developers. To support large-scale production inference, Meta has quantized its models from 16-bit (BF16) to 8-bit (FP8) numerics.
Meta’s partnerships with major cloud providers such as AWS, NVIDIA, and Google Cloud ensure Llama 3.1 is accessible across various platforms.
Llama 3.1 405B Model Evaluation and Comparison
Direct comparisons with multiple models in preliminary assessments suggest promising results for Llama 3.1 in some metrics. For example, Llama 3.1 405B has shown competitive performance at question answering, summarization, and translation. Its ability to handle complex reasoning problems and generate creative code formats is also noteworthy.
While it may not yet match the overall performance of GPT-4o or Claude 3.5 Sonnet in every single aspect, its open-source nature offers significant advantages in accessibility, customization, and potential for further development.
Source: Meta AI
Source: Meta AI
Code Generation: Llama 3.1 vs GPT 4 vs Claude 3.5 Sonnet
Llama 3.1 405b demonstrates notable coding capabilities, achieving a high completion percentage on the evaluated tasks, as illustrated in the provided graph. Compared to Claude 3.5 Sonnet, which stands out at the top with the highest completion rate, Llama 3.1 405b is slightly lower but still performs robustly. When compared to GPT-4o, Llama 3.1 405b also shows competitive performance, although GPT-4o ranks just below Claude 3.5 Sonnet. This indicates that while Llama 3.1 405b is proficient in coding tasks, both Claude 3.5 Sonnet and GPT-4o slightly surpass it, suggesting their superior performance in completing coding tasks correctly.
For a comprehensive comparison between GPT-4o and Claude 3.5 Sonnet, you can read this article.
In a recent side-by-side comparison by Aider, all three models (along with many others) were tested across various commands. Llama 3.1 405b is the most powerful code generation open weight model. It is very close to GPT-4 in terms of performance, however it is not as powerful as GPT-40 and Claude 3.5 Sonnet. Surprising enough, DeepSeek Coder models are performing better than Llama 3.1 and GPT-4o as per Aider’s leaderboard for specific coding tasks.
There are two benchmarks by Aider, Code editing and Code refactoring. Aider’s code editing benchmark evaluates the LLM’s proficiency by tasking it with editing Python source files to complete 133 coding exercises from Exercism. This benchmark measures the LLM’s ability to integrate new code seamlessly into existing codebases and apply all changes autonomously, without human intervention.
Their refactoring benchmark challenges the LLM to refactor 89 extensive methods from large Python classes. This more demanding test evaluates the model’s capability to produce long segments of code accurately, without omissions or errors. It was specifically designed to test and measure GPT-4 Turbo’s propensity for “lazy coding.”
Note that both these benchmarks use Python as the language. For a comprehensive coding ability assessment for multiple languages, HumanEval benchmark is more relevant.
Practical Examples
To better understand the capabilities of Llama 3.1 405B, GPT-4o, and Claude 3.5 Sonnet, consider experimenting with the following prompts. Each model has its strengths, and these examples will help illustrate their unique features and performance in various tasks. Based on your needs, you can assess what works best for you.
| Test Case | Task | Evaluation Criteria |
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Test Case 1: Python Code Generation |
Write a script to generate email address from name and domain |
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Test Case 2: Web Page creation |
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Test Case 3: API Query Generation |
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Accuracy and User Feedback
User feedback from platforms like Reddit and X (formerly Twitter) highlights varying experiences with these models. Many users noted that while Llama 3.1 excels in straightforward tasks, it sometimes falters in complex reasoning compared to Claude 3.5.
For instance, a Twitter user @aiexplainedyt reported that Claude 3.5 Sonnet provided the best simple bench results compared to other models, including Llama 3.1 405B.
But some users find Llama more impressive, as highlighted in the below screenshot:
Llama 3.1 Pricing Comparison
Llama 3.1, being an open-source model, is available on various platforms. Though pricing per million output tokens varies a lot across these providers:
- Fireworks: $3
- Octo AI: $9
- Together AI: $15
- Snowflake: $15
- Azure: $16
- Databricks: $30
- IBM: $35
Best platforms to access Llama models
Users can access these models through various platforms. Some of the best “bang-for-your-buck” ones inlcude:
- Bind AI: Bind AI offers a user-friendly interface for interacting with AI models, including Llama, GPT-4o, and Claude 3.5 Sonnet. The platform provides a range of pricing options to suit different needs, and many features that aren’t available on other platforms.
- AWS: AWS hosts Llama 3.1 405B, allowing users to deploy the model within their existing AWS infrastructure. Pricing may vary depending on usage and specific requirements.
- Google Cloud: Google Cloud is another platform that offers Llama 3.1 405B, providing users with the flexibility to integrate the model into their cloud-based applications.
Summary
To sum it up, the choice between Llama 3.1, GPT-4o, and Claude 3.5 Sonnet largely depends on user needs and specific use cases. As cliché as that might sound, that’s what test results show. Llama 3.1 is good for developers looking for an open-source solution with extensive customization options and flexibility in deployment. GPT-4o and Claude 3.5 Sonnet are good for multimodal learning and precision-coding respectively.
For further exploration and hands-on experience, users can visit the respective platforms hosting these models.
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