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Llama 3.1 vs. Mistral Large 2: What Does This Battle of AI Giants Mean? What is the Future of Open-Source AI?

Table of Contents

1. Introduction

The world of artificial intelligence is more dynamic than ever in the scorching summer of 2024. Just as the dust from Meta’s open-source model, Llama 3, had barely settled, the company made a surprise move in July by announcing Llama 3.1. This move excited the AI community, and shortly afterward, Mistral AI counterattacked with Mistral Large 2. Like a fierce chess match between two grandmasters, these two giant models have taken the stage with game-changing moves that will shape the future of AI.

According to Meta, Llama 3.1 holds the title of “the world’s largest and most capable open-source foundation model.” With 405 billion parameters, this massive model promises groundbreaking potential in natural language processing (NLP) tasks. However, Mistral Large 2 did not stay idle. Announced just one day after Llama 3.1, this model, despite having fewer parameters (128 billion), claims to deliver higher performance. Particularly excelling in code generation and mathematical operations, Mistral Large 2 signals a new era in the open-source AI race.

So, what are the key differences between these two AI giants? Which model performs better in which tasks? What do they mean for the future of open-source artificial intelligence? In this post, we will delve deep into Llama 3.1 and Mistral Large 2, provide comparative analyses, and offer AI enthusiasts a comprehensive guide to these two models.

If you’re ready, let’s take a closer look at this thrilling AI showdown.

2. Llama 3.1: Meta’s Open-Source AI Revolution and Its Potential to Shape the Future

Meta has taken its ambitions in artificial intelligence a step further with the announcement of Llama 3.1, a 405-billion-parameter model that supports context lengths of up to 128k and is available in eight different languages. This model, a significant upgrade over its predecessor, Llama 3, is built on Transformer architecture and demonstrates exceptional proficiency in NLP tasks.

One of Llama 3.1’s most striking features is its extraordinary capability in natural language understanding and generation. It performs exceptionally well in text analysis, summarization, and classification tasks, almost like a linguistic genius. Whether you need to summarize a product review or translate a news article, Llama 3.1 delivers impressive results. Moreover, it excels in creative text generation, producing original content across various formats, from poetry to screenplays.

The open-source nature of Llama 3.1 is one of its biggest advantages. This allows researchers, developers, and AI enthusiasts to access, examine, and utilize the model’s code in their own projects. This not only enables continuous improvement and refinement of Llama 3.1 but also significantly contributes to the democratization of AI technologies. Being open-source also enhances the model’s transparency, fostering trust and ethical AI usage.

Llama 3.1 is not just powerful in natural language processing; it also performs exceptionally well in coding, mathematical operations, and even protein folding. Its versatility increases its potential applications across various industries and fields. Education, healthcare, finance, and technology are some of the sectors where Llama 3.1 is expected to make groundbreaking impacts. 

Meta believes that Llama 3.1 will pave the way for new applications like synthetic data generation and will be the first open-source model at this scale to enable model distillation, which involves improving and training smaller models. By providing its growing community with more tools and resources, Meta is developing new utilities that help developers create custom AI agents, while new security tools like Llama Guard 3 and Prompt Guard encourage responsible AI development.

More than 25 major partners, including Amazon Web Services (AWS), NVIDIA, Databricks, Groq, Dell Technologies, Microsoft Azure, Google Cloud, and Snowflake, are supporting the Llama 3.1 ecosystem from day one, accelerating its adoption.

Currently, Llama 3.1 405B can be tested in the United States via WhatsApp and meta.ai, where users can ask complex math or coding questions.

2.1. Model Evaluation

In this version, performance evaluations were conducted across more than 150 benchmark datasets covering various languages. Additionally, comprehensive human evaluations were carried out, comparing Llama 3.1 with competing models in real-world scenarios. Experimental assessments indicate that Llama 3.1 is competitive with leading foundation models like GPT-4, GPT-4o, and Claude 3.5 Sonnet. Smaller models are also capable of competing with both closed and open models with similar parameter counts.

Comparison of Llama 3.1 405B and Other Models

Comparison of Llama 3.1 8B, Llama 3.1 70B, and Other Models

Human Evaluation for Llama 3.1 405B

2.2. Training and Optimization

The 405-billion-parameter Llama 3.1 was trained on 16,000 H100 GPUs. Compared to previous versions of Llama, Meta increased both the quantity and quality of training data, applying the same meticulous approach to the processes of pre-selection, filtering, and selection. In line with scaling laws based on model size, this new flagship model surpasses smaller models trained with the same procedure.

2.3. Instruction and Chat Fine-Tuning

With Llama 3.1 405B, the goal was to enhance the model’s helpfulness, response quality, and ability to follow detailed instructions based on user prompts. The greatest challenges lay in supporting more capabilities, managing the 128K context window, and ensuring model scalability and security.

During Llama 3.1’s development, Meta used a method involving several rounds of alignment on a pre-trained model to produce the final chat models. Each round included Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Synthetic data generation was employed to produce the vast majority of SFT samples, iterating repeatedly to generate higher-quality synthetic data across capabilities. Additionally, significant investments were made in data processing techniques to filter this synthetic data at the highest quality, enabling a more efficient and intelligent model by scaling the amount of fine-tuning data across capabilities.

2.4. Building with Llama 3.1 405B

Utilizing a 405B-sized model can be challenging for the average developer. Running this incredibly powerful model requires significant computational resources and expertise. Listening to the Llama community, Meta acknowledged that there is much more to generative AI development than merely writing prompts for the model. They aim to ensure everyone can make the most of the advanced capabilities offered by 405B.

Some examples of Llama 405B’s key capabilities:

  • Real-time batch inference
  • Supervised fine-tuning (SFT)
  • Model evaluation for your specific application
  • Continuous pre-training
  • Retrieval-Augmented Generation (RAG)
  • Function calling
  • Synthetic data generation

This is an area where the Llama ecosystem can be highly beneficial. From day one, developers can leverage all the advanced capabilities of the 405B model and start developing immediately. Developers can also explore advanced workflows like easy-to-use synthetic data generation, step-by-step instructions for model distillation, and seamless RAG enabled by solutions from partners like AWS, NVIDIA, and Databricks. Additionally, optimizations for low-latency inference and similar enhancements for cloud deployments have been made with Groq and Dell.

Meta expressed confidence in their leadership in the open-source generative AI space as follows:

“Today, we are taking the next steps towards establishing open-source AI as the industry standard. We present Llama 3.1 405B as the first frontier-level open-source AI model, along with the new and improved Llama 3.1 70B and 8B models. With a significantly better cost/performance ratio compared to closed models, the openness of the 405B model makes it the optimal choice for fine-tuning and distillation of smaller models.”

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3. Mistral Large 2: The Small Giant with Big Performance

In this AI duel, a young and ambitious contender rises against Llama 3.1: Mistral Large 2. Developed by the still relatively new Mistral AI, this model, with its 128 billion parameters, may seem modest compared to its competitor, but its performance is set to make a significant impact.

One of Mistral Large 2’s most remarkable features is its claim of achieving higher efficiency with fewer parameters. Its developers argue that the model outperforms Llama 3.1 in tasks such as code generation and mathematical operations. While these claims have yet to be fully confirmed by independent tests, Mistral Large 2 has already sparked excitement within the AI community.

Mistral Large 2 also boasts a wide range of applications. The model is expected to accelerate and optimize software development processes, making tasks like automatic code completion, debugging, and even suggesting new software features easier for developers. Additionally, it could become a powerful tool in fields such as mathematical computations, data analysis, and scientific research.

The potential impact of Mistral Large 2 is not limited to technical fields. The model is also expected to contribute to the development of more accessible and user-friendly AI applications. In particular, its adoption could expand in areas such as education, healthcare, and customer service, reaching a broader audience.

However, Mistral Large 2 is still a very new model and is continuously evolving. Future tests and comparisons will provide a clearer picture of its true potential. Nevertheless, it is already evident that Mistral Large 2 brings fresh innovation to the AI world and could achieve great success in the future.

3.1. Mistral Large 2: A New Era of Performance and Accessibility

In July 2024, Mistral AI introduced a breath of fresh air into the AI landscape with the release of Mistral Large 2, a model with 123 billion parameters. This model stands out not only for its technical capabilities but also for its accessibility and ease of use.

3.1.1. Extended Context Window up to 128K Tokens

One of Mistral Large 2’s most significant features is its 128,000-token context window. This allows the model to process larger text or code segments at once, which is a major advantage when working with long documents or complex tasks.

3.1.2. Multilingual Support

Mistral Large 2 supports not only English but also French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean. This enables it to perform tasks like content creation, translation, and language analysis in multiple languages. Additionally, with support for over 80 programming languages, Mistral Large 2 proves to be a powerful tool for software development.

3.1.3. Single-Node Inference

Mistral Large 2 is designed to operate efficiently on a single powerful processor. This enhances the model’s speed and efficiency, making large-scale AI applications more accessible to smaller teams and organizations.

3.1.4. Flexible Licensing Options

Mistral Large 2 offers a flexible licensing model for both research and commercial purposes.

  • The Mistral Research License allows researchers and developers to use the model for free and experiment with it.
  • The Mistral Commercial License provides the necessary permissions for commercial applications.

This licensing approach supports AI research advancements while also encouraging commercial innovation.

Mistral Large 2 is not just about technical excellence; its ease of use and accessibility are set to usher in a new era in AI. Its extended context window, multilingual capabilities, single-node inference, and flexible licensing make it an attractive choice for both researchers and businesses.

3.2. Performance Evaluation

Mistral Large 2 is setting a new benchmark in performance-to-cost ratio in evaluation metrics like MMLU. The pre-trained version achieved 84.0% accuracy in MMLU, marking a new point on the performance-to-cost Pareto frontier among open models.

To better understand Mistral Large 2’s performance, let’s examine its benchmarks. The above graph illustrates Mistral Large 2’s performance compared to other models in evaluations like Human Eval, Human Eval Plus, MBPP Base, and MBPP Plus.

  • In Human Eval and Human Eval Plus, Mistral Large 2 delivers competitive results.
  • In MBPP Base and MBPP Plus, however, it lags behind in certain cases.

Overall, considering its lower parameter count, Mistral Large 2 presents highly competitive performance, indicating substantial improvements in efficiency and optimization.

3.3. Coding and Logical Reasoning Capabilities

Building on the experiences of Codestral 22B and Codestral Mamba, Mistral AI has extensively trained Mistral Large 2 on code. As a result, the model performs at the same level as leading models like GPT-4o, Claude 3 Opus, and Llama 3.1 405B.

Additionally, Mistral Large 2’s logical reasoning abilities have been significantly improved. The model has been trained to minimize hallucinations, providing more cautious and selective responses. In mathematical benchmarks, it demonstrates enhanced logical reasoning and problem-solving skills.

Mistral Large 2’s coding performance, particularly in Python and C++, is impressive.

  • In Python, it achieves 92.1% accuracy.
  • In C++, it scores 84.5% accuracy.

These numbers are comparable to, or in some cases even better than, larger models like Llama 3.1 405B.

However, its performance in Bash and C# is lower, indicating areas for further improvement. Nevertheless, Mistral Large 2 proves to be a highly capable model, especially for code generation.

3.4. Instruction Following and Alignment

Mistral Large 2 has made significant progress in instruction following and conversational abilities. The model now performs better in following precise instructions and managing long, multi-turn conversations. Its performance has been benchmarked in MT-Bench, Wild Bench, and Arena Hard.

3.5. Tool Usage and Function Calling

Mistral Large 2 is equipped with enhanced function calling and retrieval capabilities, trained to efficiently handle both parallel and sequential function calls. This allows it to serve as a powerful engine for complex business applications.

3.6. Accessibility and Future Prospects

Mistral Large 2 is available for free under the name “mistral-large-2407” on la Plateforme and Le Chat. Additionally, its weights are accessible on Hugging Face.

Mistral AI aims to expand Mistral Large 2’s global reach through partnerships with Google Cloud Platform, Azure AI Studio, Amazon Bedrock, and IBM watsonx.ai.

With its open-source model approach, Mistral AI is pioneering a new chapter in AI. Through Mistral Large 2, the company is making groundbreaking advancements in both performance and accessibility.

As AI technology progresses, the future looks increasingly exciting, with Mistral AI driving innovation forward.

4. Comparative Analysis: Llama 3.1 vs. Mistral Large 2

The two rising stars of the AI world, Llama 3.1 and Mistral Large 2, are engaged in fierce competition, showcasing their abilities in different domains. Let’s compare their performances and examine which model excels in which tasks.

4.1. Text Generation

Thanks to its massive size and extensive training dataset, Llama 3.1 is highly skilled in text generation. It delivers impressive results in creative writing, storytelling, and poetry. However, it sometimes struggles to maintain controlled and highly specific text outputs, occasionally including unnecessary details or deviating from the topic.

On the other hand, Mistral Large 2 is better at generating concise and precise texts. It excels in following instructions and producing structured content in the desired format. However, when it comes to creativity-driven tasks, it may not be as impressive as Llama 3.1.

4.2. Coding

In the field of code generation, Mistral Large 2 poses a serious challenge to Llama 3.1. It performs exceptionally well in generating code, particularly in popular languages like Python, and is highly effective in debugging tasks.

Llama 3.1, being a more general-purpose model, lags slightly behind in direct code writing. However, thanks to its vast knowledge base, it can provide more comprehensive and detailed explanations for coding-related queries.

4.3. Mathematical Operations

Mistral Large 2 is also highly competitive in mathematical operations. The model can perform complex calculations quickly and accurately.

In contrast, Llama 3.1 is somewhat more limited in raw mathematical capabilities. However, due to its strong natural language processing (NLP) abilities, it is better at understanding mathematical problems and suggesting solution approaches.

4.4. Benchmark Results

Benchmark tests like MMLU are crucial for evaluating the performance of both models across different tasks. Despite having fewer parameters, Mistral Large 2 achieves results comparable to—or in some cases even better than—Llama 3.1. This indicates that Mistral Large 2 is a more efficient and optimized model.

In multilingual MMLU comparisons, Mistral Large 2’s ability to match the performance of Llama 3.1’s massive 405-billion-parameter version and even outperform the 70-billion-parameter version is particularly noteworthy. This highlights Mistral Large 2’s ability to achieve high performance with fewer resources.

5. Open-Source AI: Democratization, Innovation, and Future Potential

Large language models (LLMs) like Llama 3.1 and Mistral Large 2 are revolutionizing the field of artificial intelligence. These models are paving the way for AI democratization, making cutting-edge technology accessible beyond big tech monopolies and available to a wider audience.

5.1. Democratization

Unlike closed-source models, open-source LLMs like Llama 3.1 allow developers to download model weights and customize them according to their needs. This enables users to train models on new datasets, fine-tune them, and even run them on personal laptops.

This flexibility and accessibility lower the barriers to AI technology, fostering equal opportunities in the field. Developers, researchers, students, and even AI enthusiasts can freely use, explore, and modify these models. This, in turn, encourages more people to participate in AI development and create innovative applications.

For developing countries and small businesses, this accessibility provides a valuable opportunity to benefit from AI technologies, which were once reserved for major corporations.

5.2. Accelerating Innovation

Open-source LLMs accelerate AI research and development. Thousands of independent researchers and developers can analyze, improve, and expand these models by identifying errors, adding new features, and exploring diverse applications.

This collaborative approach allows AI technologies to progress faster and be applied across various domains. Additionally, open-source models serve as a foundation for commercial AI advancements, increasing competition and innovation in the industry.

5.3. Future Potential

The future potential of open-source AI is immense. These models could revolutionize industries including healthcare, education, finance, and the arts.

For example, they could:

  • Enable personalized learning experiences
  • Assist in disease diagnosis and treatment
  • Make accurate financial predictions
  • Generate creative content

Moreover, open-source LLMs contribute to the development of more transparent and trustworthy AI systems. As Meta CEO Mark Zuckerberg stated, open-source AI has the potential to “enhance human productivity, creativity, and quality of life” while also “accelerating economic growth and advancing medical and scientific research.”

5.4. Potential Challenges

Despite their advantages, open-source AI models also present challenges. Some risks include:

  • Misuse of models
  • Spread of misinformation
  • Bias in generated content

For this reason, ethical and security considerations must be carefully addressed in the development and use of open-source AI.

Additionally, ongoing updates and improvements require strong community support and collaboration.

5.5. Cost Efficiency

Another major advantage of open-source models is their lower cost compared to proprietary alternatives. Artificial Analysis reports indicate that Llama models offer one of the lowest cost-per-token rates in the industry.

This cost efficiency makes open-source models more appealing to a broader user base, further enhancing AI accessibility.

5.6. The Impact of Open-Source AI

Models like Llama 3.1 and Mistral Large 2 hold tremendous potential for:

  • Democratizing AI
  • Accelerating innovation
  • Reducing costs
  • Driving AI-powered solutions across various industries

However, for this potential to be fully realized, issues such as ethics, security, and collaboration must be carefully managed. If properly governed, open-source AI could lead to groundbreaking innovations for humanity.

6. Conclusion: Two Sides of the AI Revolution

Llama 3.1 and Mistral Large 2 are two powerful models ushering in a new era of AI development. Both models stand out for their open-source nature, advanced capabilities, and diverse applications.

  • Llama 3.1, with its 405 billion parameters, serves as a massive knowledge repository and excels in natural language processing tasks. It is an ideal choice for those looking to generate creative texts, translate content, and summarize information.
  • Mistral Large 2, on the other hand, focuses on efficiency, offering high performance with fewer parameters. It particularly shines in coding, mathematical operations, and multilingual tasks, making it a compelling choice for technical users and developers.

Which Model is Better?

The answer depends on user needs and expectations:

  • If creative and original text generation is your priority, Llama 3.1 may be the better option.
  • If you are more focused on coding, mathematical operations, or multilingual tasks, Mistral Large 2 might be the superior choice.

One important point to keep in mind is that both models are still evolving and will likely continue to improve over time. Therefore, it is essential to keep track of their developments and choose the model that best fits your needs.

What Do You Think?

How do you think the competition between Llama 3.1 and Mistral Large 2 will shape the future of AI?
Which model do you believe will excel in different areas?

Share your thoughts in the comments!

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