Key Highlights
Llama 3.3 70B strengths:
Superior performance in general knowledge and reasoning tasks (MMLU: 86)
Higher accuracy in mathematical reasoning (MATH: 76)
Better scientific comprehension (GPQA Diamond: 49)
Faster text processing speed
Cost-effective solution
Strong multilingual support across 8 languages
Mistral Large 2411 strengths:
Excels in programming and code generation (HumanEval: 90)
Advanced context handling for retrieval-augmented generation (RAG)
Native function calling and JSON output capabilities
Broader language support covering 11 languages
Specialized in complex agentic workflows
Robust instruction following capabilities
If you're looking to evaluate the Llama 3.3 70b and on your own use-cases — Upon registration, Novita AI provides a $0.5 credit to get you started!
The field of large language models (LLMs) is constantly evolving, with new models offering improved capabilities and efficiency. This article provides a practical comparison of two notable models: Meta's Llama 3.3 70B and Mistral AI's Mistral Large 2411. We will explore their technical specifications, performance benchmarks, and ideal use cases, aiming to provide a comprehensive guide for developers and researchers.
Basic Introduction of Model
To begin our comparison, we first understand the fundamental characteristics of each model.
Llama 3.3 70b
Release Date: December 6, 2024
Model Scale:
Key Features:
Open source model
Instruction-tuned,text-only model
Supports tool use and function calling
Utilizes Grouped-Query Attention (GQA) for improved efficiency
Supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai
Mistral Large 2411
Release Date: November 18, 2024
Other Model:
mistral/mistral-Large-2-2407
Key Features:
Closed source model
Trained on 80+ coding languages
Supports native function calling and JSON outputting
Designed for robust context adherence, particularly for retrieval-augmented generation (RAG)
Supports English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch and Polish.
Model Comparison
In summary, these technical specifications highlight the differences between the two models in terms of scale, architectural design, and performance optimization. While Mistral Large 2411 features a larger parameter count, Llama 3.3 70b offers more flexible quantization options. Both models maintain parity in terms of context window size.
Speed Comparison
If you want to test it yourself, you can start a free trial on the Novita AI website.
Speed Comparison
source from artificialanalysis
Cost Comparison
source from artificialanalysis
Overall, Llama 3.3 70B outperforms Mistral Large 2411 across all these performance metrics, with particularly notable advantages in output speed and pricing.
Benchmark Comparison
Now that we've established the basic characteristics of each model, let's delve into their performance across various benchmarks. This comparison will help illustrate their strengths in different areas.
| Benchmark Metrics | Llama 3.3 70B | Mistral Large 2411 |
| MMLU | 86 | 85 |
| HumanEval | 86 | 90 |
| MATH | 76 | 72 |
| GPQA Diamond | 49 | 47 |
While Llama 3.3 70B excels in general knowledge and reasoning tasks, Mistral Large 2411 demonstrates superior coding capabilities. Notably, it's reported that Mistral Large 2411 has been trained on over 80 programming languages from Python to Fortran, making it particularly efficient for development tasks.
If you would like to know more about the llama3.3 benchmark knowledge. You can view this article as follows:
If you want to see more comparisons between llama 3.3 and other models, you can check out these articles:
Qwen 2.5 72b vs Llama 3.3 70b: Which Model Suits Your Needs?
Llama 3.1 70b vs. Llama 3.3 70b: Better Performance, Higher Price
Applications and Use Cases
Llama 3.3 70B:
Multilingual chatbots and assistants
Coding support and software development
Synthetic data generation
Multilingual content creation and localization
Research and experimentation
Knowledge-based applications
Mistral Large 2411:
Complex agentic workflows with precise instruction following and JSON outputs
Large context applications requiring strong adherence for RAG
Code generation
Accessibility and Deployment through Novita AI
Step 1: Log In and Access the Model Library
Log in to your account and click on the Model Library button.
Step 2: Choose Your Model
Browse through the available options and select the model that suits your needs.
Step 3: Start Your Free Trial
Begin your free trial to explore the capabilities of the selected model.
Step 4: Get Your API Key
To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.
Step 5: Install the API
Install API using the package manager specific to your programming language.
After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for pthon users.
from openai import OpenAI
client = OpenAI(
base_url="https://api.novita.ai/v3/openai",
# Get the Novita AI API Key by referring to: https://novita.ai/docs/get-started/quickstart.html#_2-manage-api-key.
api_key="<YOUR Novita AI API Key>",
)
model = "meta-llama/llama-3.3-70b-instruct"
stream = True # or False
max_tokens = 512
chat_completion_res = client.chat.completions.create(
model=model,
messages=[
{
"role": "system",
"content": "Act like you are a helpful assistant.",
},
{
"role": "user",
"content": "Hi there!",
}
],
stream=stream,
max_tokens=max_tokens,
)
if stream:
for chunk in chat_completion_res:
print(chunk.choices[0].delta.content or "")
else:
print(chat_completion_res.choices[0].message.content)
Upon registration, Novita AI provides a $0.5 credit to get you started!
If the free credits is used up, you can pay to continue using it.
Both Llama 3.3 70B and Mistral Large 2411 are powerful language models with unique strengths. Llama 3.3 excels in its accessibility and efficiency, making it suitable for a wide range of applications on standard hardware. In contrast, Mistral Large stands out with its advanced reasoning, coding capabilities, and agent-centric functionalities but requires more substantial hardware resources. The choice between the two depends on specific needs and available resources.
Frequently Asked Questions
What are the system requirements for running Mistral Large 2411?
To run Mistral Large 2411 efficiently, it requires over 300 GB of GPU RAM. It is recommended to use the vLLM library for production-ready inference pipelines.
What makes Mistral Large 2411 unique compared to previous models?
Mistral Large 2411 includes enhancements in long context handling, improved function calling capabilities, and better adherence to system prompts compared to its predecessor models.
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