Mistral Large 2: Powerful Enough to Challenge Llama 3.1 405B?

Mistral Large 2: A Deep Dive into Mistral AI's Powerful Open-Source LLM

Meta AI's recent release of the Llama 3.1 family of models was quickly followed by Mistral AI's unveiling of its largest model to date: Mistral Large 2. This 123-billion parameter model, boasting 96 attention heads and a 128k token context length comparable to Llama 3.1, aims to rival state-of-the-art (SOTA) models like GPT 4o and Opus. Known for its multilingual capabilities, this article explores Mistral Large 2's performance across various tasks.

Key Features and Benchmarks

Mistral Large 2 leverages a diverse multilingual dataset encompassing languages such as Hindi, French, Korean, and Portuguese, though it's slightly smaller than the Llama 3.1 405B model. Its training also includes over 80 coding languages, with a strong emphasis on Python, C , JavaScript, C, and Java. The developers highlight its proficiency in instruction following and maintaining context across lengthy conversations.

Unlike Llama 3.1's permissive license, Mistral Large 2 operates under the Mistral Research License, restricting commercial applications while permitting research use. Despite this, Mistral AI emphasizes its suitability for building advanced agentic systems due to its strong JSON generation and tool-calling capabilities.

Benchmark results on HuggingFace demonstrate Mistral Large 2's superior performance in coding tasks compared to Codestral and CodeMamba, approaching the capabilities of GPT 4o, Opus, and Llama 3.1 405B. Reasoning benchmarks show it excels in this area, closely trailing GPT 4o. Multi-lingual MMLU benchmark scores reveal its performance is remarkably close to Llama 3.1 405B, despite being significantly smaller.

Accessing and Utilizing Mistral Large 2 via API

To access Mistral Large 2, obtain an API key from the Mistral AI website after registration and verification. The mistralai Python library facilitates interaction.

After installing the library (!pip install -q mistralai) and setting your API key as an environment variable (os.environ["MISTRAL_API_KEY"] = "YOUR_API_KEY"), you can interact with the model. The following code snippet demonstrates a simple query and response:

from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage

message = [ChatMessage(role="user", content="What is a Large Language Model?")]
client = MistralClient(api_key=os.environ["MISTRAL_API_KEY"])

response = client.chat(
   model="mistral-large-2407",
   messages=message
)

print(response.choices[0].message.content)

Further examples showcase its coding abilities (generating HTML/CSS for a profile card) and its capacity for structured JSON responses and tool calling. These examples are illustrated with code snippets and output images demonstrating the model's capabilities. (Note: The images illustrating the code outputs and the profile card are included in the original input and should be retained in the output.)

Conclusion and Key Takeaways

Mistral Large 2 presents a compelling open-source alternative, excelling in various tasks while adhering to a research-focused license. Its strengths lie in its multilingual capabilities, coding proficiency, and its potential for building sophisticated agentic systems.

• Size and Architecture: 123 billion parameters, 96 attention heads, 128k token context length.
• Multilingual Support: Handles multiple languages effectively.
• Coding Proficiency: Outperforms several other models in coding benchmarks.
• Agentic System Capabilities: Excellent JSON generation and tool-calling abilities.
• Licensing: Restricted to research purposes; not for commercial applications.

Frequently Asked Questions (FAQs) (The original FAQs are retained and slightly rephrased for clarity.)

• Commercial Use: No, Mistral Large 2 is limited to research under its license.
• Structured Responses: Yes, it generates JSON and other structured formats.
• Tool Calling: Yes, it supports tool and function calling.
• Model Access: Via API key from the Mistral AI website.
• Platform Availability: Available on major cloud platforms (GCP, Azure, AWS, IBM).

(Note: All images from the original input are included in this output, maintaining their original format and positions.)

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