Meta’s Llama Challenges GPT4


In the ever-evolving landscape of artificial intelligence, Meta has unveiled its latest entrant, Code Llama 70B, a formidable contender in the AI coding domain. With advancements in code-generation capabilities, Code Llama 70B is geared to challenge OpenAI’s GPT-4, promising a thrilling competition in the realm of AI coding.

SEE MORE : Nyt Explores AI Newsroom

The Power of Code Llama 70B

Code Llama 70B boasts an impressive training regimen, having been exposed to a staggering 500 billion tokens of code and code-related data. This extensive training has equipped the model with a substantial context window of 100, enabling it to handle a greater number of queries and, consequently, enhancing accuracy in code generation.

Performance Metrics

In terms of performance, Code Llama 70B has set a new benchmark, achieving a remarkable 53% accuracy score on the HumanEval benchmark. This surpasses the previous GPT-3.5’s score of 48.1% and is closing in on the reported 67% accuracy of GPT-4. These results indicate a significant stride in Meta’s pursuit of AI excellence.

However, despite these achievements, GPT-4 maintains its lead in certain aspects.

GPT-4’s Dominance

GPT-4 exhibits superior performance in handling complex programming tasks with higher accuracy than its predecessors, including Code Llama 70B. In the HumanEval benchmark, which evaluates language models’ coding abilities, GPT-4 leads with an impressive 67.0% compared to Code Llama 70B at 29.9%.

Math Reasoning Abilities

The prowess of GPT-4 extends beyond coding as it outshines Code Llama 70B in math reasoning tasks. With GSM8K (8-shot) scores indicating a commanding 92.0% for GPT-4 and 56.8% for Code Llama 70B, GPT-4 establishes its supremacy in logical reasoning and mathematical problem-solving.

Strengths of Code Llama 70B

Despite GPT-4’s dominance, Code Llama 70B brings its own strengths to the table. Notably, in common sense reasoning tasks, such as the Winogrande challenge, Code Llama 70B showcases improvements where GPT-3 falters. Its ability to grasp contextual cues positions it as a reliable choice for a diverse range of applications.

Moreover, Code Llama 70B excels in programming abilities, surpassing GPT-3. Its prowess in generating high-quality code underscores its potential for applications in software development and related domains.

MUST READ : Scite AI Login

Accessibility: Open-Source vs. Closed-Source Models

An essential aspect of evaluating these AI models lies in their accessibility. Code Llama 70B, being open-source, beckons the global community of developers to contribute to its refinement. This inclusivity enables collaborative efforts for diverse applications, ranging from academic research to content creation, machine translation, and sentiment analysis.

On the contrary, GPT-4 follows a closed-source model, limiting access to developers with a commendable track record. While this approach ensures control over the model’s usage, it also restricts the broader developer community from actively participating in its evolution.


In the ongoing AI coding race, GPT-4 stands as the current frontrunner, excelling in task complexity, coding, math reasoning, and multilingual support. However, Meta’s Code Llama 70B emerges as a robust competitor, marking a significant leap forward in AI coding capabilities.

The strengths of Code Llama 70B, including improvements in common sense reasoning tasks and superior programming abilities, position it as a viable alternative. Its open-source nature further encourages collaborative development, fostering innovation in various domains.

As the AI landscape continues to evolve, the competition between Code Llama 70B and GPT-4 promises to drive advancements, benefiting developers, researchers, and industries alike. Whether open or closed source, these models signal a future where AI becomes an indispensable tool, continuously pushing the boundaries of what is achievable in the realm of coding and beyond.

Leave a Comment