Open Source LLMs Battle: Llama 3.1 vs Mistral Large vs DeepSeek-V3

The open-source large language model (LLM) landscape is more vibrant than ever, with models rapidly approaching and sometimes surpassing the capabilities of their closed-source counterparts. Today, we pit three leading contenders against each other: Meta's Llama 3.1, Mistral AI's Mistral Large, and DeepSeek-V3. This article provides an in-depth comparison across various benchmarks, practical use cases, and other crucial factors for developers and businesses looking to leverage these powerful tools.

Overview

• Llama 3.1: The latest iteration of Meta's Llama series, Llama 3.1, builds upon its predecessors with enhanced training data, improved architecture, and a focus on responsible AI development. It's designed for a wide range of applications, from creative content generation to complex reasoning tasks.
• Mistral Large: Mistral AI has quickly established itself as a key player in the open-source LLM space. Mistral Large is their flagship model, known for its strong performance, especially in multilingual contexts, and its efficiency in terms of computational requirements.
• DeepSeek-V3: DeepSeek-V3 represents a significant leap forward from DeepSeek AI. Trained on a massive dataset with a strong emphasis on code and mathematical reasoning, it excels in tasks requiring precision and analytical skills.

Benchmark Comparisons

Quantitative benchmarks provide a standardized way to evaluate the performance of LLMs. Here's a comparative look at these three models across several key benchmarks.

Note: These scores are estimates based on the current trends and publicly available information. Actual performance may vary depending on the specific implementation and hardware.

Key Observations:

• Overall Performance: DeepSeek-V3 consistently scores at the top across various benchmarks, suggesting superior general capabilities.
• Reasoning: DeepSeek-V3 shows a clear advantage in benchmarks like GSM8K, which tests mathematical reasoning.
• Coding: HumanEval scores indicate that DeepSeek-V3 has a stronger ability to generate correct code.
• Truthfulness: TruthfulQA scores suggest DeepSeek-V3 is slightly better at avoiding the generation of false or misleading information.
• Multilingual: Mistral Large exhibits strong performance in MT-Bench, indicating its strength in multilingual tasks compared to the other two models.

Practical Use Cases

Beyond raw benchmark scores, real-world applications are crucial for evaluating the usefulness of LLMs.

1. Content Generation:

All three models are capable of generating high-quality content, but their strengths lie in different areas.

• Llama 3.1: Excellent for creative writing, drafting marketing copy, and generating engaging social media content. Its versatility makes it suitable for diverse content needs.
• Mistral Large: Shines in multilingual content generation. It can seamlessly translate and adapt content across different languages, making it ideal for global audiences.
• DeepSeek-V3: Best suited for generating technical documentation, reports, and other content that requires precision and accuracy.

2. Chatbots and Conversational AI:

• Llama 3.1: Well-rounded conversational abilities, capable of maintaining engaging and coherent conversations.
• Mistral Large: Its multilingual capabilities make it a strong choice for chatbots that need to interact with users in different languages.
• DeepSeek-V3: Excels in providing accurate and informative responses, making it suitable for chatbots that require in-depth knowledge and reasoning.

3. Code Generation and Debugging:

python

Example: Generate a Python function to calculate the Fibonacci sequence

Llama 3.1 Output:

def fibonacci(n): if n <= 0: return [] elif n == 1: return [0] else: list_fib = [0, 1] while len(list_fib) < n: next_fib = list_fib[-1] + list_fib[-2] list_fib.append(next_fib) return list_fib

Mistral Large Output:

def fibonacci(n): a, b = 0, 1 result = [] while a < n: result.append(a) a, b = b, a+b return result

DeepSeek-V3 Output:

def fibonacci(n): """ This function calculates the Fibonacci sequence up to n terms. """ if n <= 0: return [] elif n == 1: return [0] else: fib_list = [0, 1] while len(fib_list) < n: next_fib = fib_list[-1] + fib_list[-2] fib_list.append(next_fib) return fib_list

• Analysis: All three models produce functional code, but DeepSeek-V3's output includes a docstring explaining the function, showcasing its strength in generating well-documented code.

4. Data Analysis and Insights:

• Llama 3.1: Capable of performing basic data analysis tasks and generating summaries of data.
• Mistral Large: Can be used to extract insights from multilingual data sources.
• DeepSeek-V3: Excels in complex data analysis tasks, such as identifying trends, patterns, and anomalies.

Pricing & Availability

A major advantage of these models is their open-source nature, which allows for free usage and modification. However, costs can arise from the computational resources needed to run them, especially for large-scale deployments.

• Llama 3.1: Available under a community license, allowing for free use and distribution. Meta also provides optimized versions for their cloud infrastructure.
• Mistral Large: Available under a permissive Apache 2.0 license. Mistral AI offers inference endpoints through their API, with pricing based on token usage.
• DeepSeek-V3: Also available under a permissive license. DeepSeek AI provides cloud-based inference services, with pricing structures tailored to different usage levels.

When self-hosting, the costs will depend on your hardware. All 3 models can be quantized to run on less powerful hardware. Quantization may impact the models' performance on specific tasks.

Verdict

Each of these LLMs brings unique strengths to the table.

• Choose Llama 3.1 if: You need a versatile and well-rounded model for various applications, and you value Meta's commitment to responsible AI development.
• Choose Mistral Large if: You prioritize multilingual capabilities and efficient performance, and you want to leverage Mistral AI's API for easy deployment.
• Choose DeepSeek-V3 if: You require exceptional reasoning and coding abilities, and you are willing to invest in the computational resources needed to run it effectively.

Ultimately, the best choice depends on your specific needs and priorities. We recommend experimenting with all three models to determine which one best fits your requirements. The open-source nature of these LLMs allows for customization and fine-tuning, enabling you to tailor them to your unique use cases. The continued advancement of open-source LLMs like Llama 3.1, Mistral Large, and DeepSeek-V3 promises a future where AI is more accessible, customizable, and aligned with the needs of a diverse range of users.