Which is the Better Model?

The AI landscape is rapidly evolving, with smaller, lightweight models gaining prominence for their efficiency and scalability. After Google DeepMind launched its 27B model Gemma 3, Mistral AI has now released the Mistral 3.1 lightweight model of 24B parameters. This new, fast, and customizable model is redefining what lightweight models can do. It operates efficiently on a single processor, enhancing speed and accessibility for smaller teams and organizations. In this Mistral 3.1 vs. Gemma 3 comparison, we’ll explore their features, evaluate their performance on benchmark tests, and conduct some hands-on trials to find out the better model.

What is Mistral 3.1?

Mistral 3.1 is the latest large language model (LLM) from Mistral AI, designed to deliver high performance with lower computational requirements. It represents a shift toward compact yet powerful AI models, making advanced AI capabilities more accessible and cost-efficient. Unlike massive models requiring extensive resources, Mistral 3.1 balances scalability, speed, and affordability, making it ideal for real-world applications.

Key Features of Mistral 3.1

• Lightweight & Efficient: Runs smoothly on a single RTX 4090 or a Mac with 32GB RAM, making it ideal for on-device AI solutions.
• Fast-Response Conversational AI: Optimized for virtual assistants and chatbots that need quick, accurate responses.
• Low-Latency Function Calling: Supports automated workflows and agentic systems, executing functions with minimal delay.
• Fine-Tuning Capability: Can be specialized for legal AI, medical diagnostics, and technical support, allowing domain-specific expertise.
• Multimodal Understanding: Excels in image processing, document verification, diagnostics, and object detection, making it versatile across industries.
• Open-Source & Customizable: Available with both base and instruct checkpoints, enabling further downstream customization for advanced applications.

How to Access Mistral 3.1

Mistral 3.1 is available through multiple platforms. You can either download and run it locally via Hugging Face or access it using the Mistral AI API.

1. Accessing Mistral 3.1 via Hugging Face

You can download Mistral 3.1 Base and Mistral 3.1 Instruct for direct use from Hugging Face. Here’s how to do it:

Step 1: Install vLLM Nightly

Open your terminal and run this command to install vLLM (this also installs the required mistral_common package):

pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly --upgrade

You can verify the installation by running:

python -c "import mistral_common; print(mistral_common.__version__)"

Step 2: Prepare Your Python Script

Create a new Python file (e.g., offline_inference.py) and add the following code. Make sure to set the model_name variable to the correct model ID (for example, “mistralai/Mistral-Small-3.1-24B-Instruct-2503“):

from vllm import LLM
from vllm.sampling_params import SamplingParams

# Define a system prompt (you can modify it as needed)
SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."

# Define the user prompt
user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."

# Set up the messages for the conversation
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": user_prompt},
]

# Define the model name (make sure you have enough GPU memory or use quantization if needed)
model_name = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"

# Initialize the LLM from vLLM with the specified model and tokenizer mode
llm = LLM(model=model_name, tokenizer_mode="mistral")

# Set sampling parameters (adjust max_tokens and temperature as desired)
sampling_params = SamplingParams(max_tokens=512, temperature=0.15)

# Run the model offline and get the response
outputs = llm.chat(messages, sampling_params=sampling_params)

# Print the generated text from the model's response
print(outputs[0].outputs[0].text)

Step 3: Run the Script Offline

1. Save the script.
2. Open a terminal in the directory where your script is saved.
3. Run the script with:
   python offline_inference.py

The model will load locally and generate a response based on your prompts.

Important Considerations

• Hardware Requirements: Running the full 24B model in full precision on GPU typically requires over 60 GB of GPU RAM. If your hardware doesn’t meet this, consider:
  • Using a smaller or quantized version of the model.
  • Using a GPU with sufficient memory.
• Offline vs. Server Mode: This code uses the vLLM Python API to run the model offline (i.e., entirely on your local machine without needing to set up a server).
• Modifying Prompts: You can change the SYSTEM_PROMPT and user_prompt to suit your needs. For production or more advanced usage, you might want to add a system prompt that helps guide the model’s behavior.

2. Accessing Mistral 3.1 via API

You can also access Mistral 3.1 via API. Here are the steps to follow for that.

1. Visit the Website: Go to Mistral AI sign in or log in with all the necessary details.

2. Access the API Section: Click on “Try the API” to explore the available options.

3. Navigate to API: Once logged in, click on “API” to manage or generate new keys.

4. Choose a Plan: When asked to generate an API, click on “Choose a Plan” to proceed with API access.

5. Select the Free Experiment Plan: Click on “Experiment for Free” to try the API without cost.

6. Sign Up for Free Access: Complete the sign-up process to create an account and gain access to the API.

7. Create a New API Key: Click on “Create New Key” to generate a new API key for your projects.

8. Configure Your API Key: Provide a key name to easily identify it. You may even choose to set an expiry date for added security.

9. Finalize and Retrieve Your API Key: Click on “Create New Key” to generate the key. Your API key is now created and ready for use in your projects.

You can integrate this API key into your applications to interact with Mistral 3.1.

What is Gemma 3?

Gemma 3 is a state-of-the-art, lightweight open model, designed by Google DeepMind, to deliver high performance with efficient resource usage. Built on the same research and technology that powers Gemini 2.0, it offers advanced AI capabilities in a compact form, making it ideal for on-device applications across various hardware. Available in 1B, 4B, 12B, and 27B parameter sizes, Gemma 3 enables developers to build AI-powered solutions that are fast, scalable, and accessible.

Key Features of Gemma 3

• High Performance on a Single Accelerator: It outperforms Llama 3-405B, DeepSeek-V3, and o3-mini in LMArena’s evaluations, making it one of the best models per size.
• Multilingual Capabilities: Supports over 140 languages, enabling AI-driven global communication.
• Advanced Text & Visual Reasoning: Processes images, text, and short videos, expanding interactive AI applications.
• Expanded Context Window: Handles up to 128k tokens, allowing deeper insights and long-form content generation.
• Function Calling for AI Workflows: Supports structured outputs for automation and agentic experiences.
• Optimized for Efficiency: Official quantized versions reduce computational needs without sacrificing accuracy.
• Built-in Safety with ShieldGemma 2: Provides image safety checking, detecting dangerous, explicit, and violent content.

How to Access Gemma 3

Gemma 3 is readily accessible across multiple platforms such as Google AI Studio, Hugging Face, Kaggle, and more.

1. Accessing Gemma 3 on Google AI Studio

This option lets you interact with Gemma 3 in a pre-configured environment without installing anything on your own machine.

Step 1: Open your web browser and go to Google AI Studio.

Step 2: Log in with your Google account. If you don’t have one, create a Google account.

Step 3: Once logged in, use the search bar in AI Studio to look for a notebook or demo project that uses “Gemma 3”.

Tip: Look for projects titled with “Gemma 3” or check the “Community Notebooks” section where pre-configured demos are often shared.

Step 4: Launch the demo by following the below steps.

• Click on the notebook to open it.
• Click the “Run” or “Launch” button to start the interactive session.
• The notebook should automatically load the Gemma 3 model and provide example cells that demonstrate its capabilities.

Step 5: Follow the instructions in the notebook to start using the model. You can modify the input text, run cells, and see the model’s responses in real-time all without any local setup.

2. Accessing Gemma 3 on Hugging Face, Kaggle, and Ollama

If you prefer to work with Gemma 3 on your own machine or integrate it into your projects, you can download it from several sources.

A. Hugging Face

Step 1: Visit Hugging Face.

Step 2: Use the search bar to type “Gemma 3” and click on the model card that corresponds to Gemma 3.

Step 3: Download the model using the “Download” button or clone the repository via Git.

If you are using Python, install the Transformers library:

pip install transformers

Step 4: Load and use the model in your code. For this, you can create a new Python script (e.g., gemma3_demo.py) and add code similar to the snippet below:

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "your-gemma3-model-id"  # replace with the actual model ID from Hugging Face
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = "What is the best way to enjoy a cup of coffee?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Run your script locally to interact with Gemma 3.

B. Kaggle

Step 1: Open Kaggle in your browser.

Step 2: Use the search bar on Kaggle to search for “Gemma 3.” Look for notebooks or datasets where the model is used.

Step 3: Click on a relevant notebook to see how Gemma 3 is integrated. You can run the notebook in Kaggle’s environment or download the notebook to study and modify it on your local machine.

C. Ollama

Step 1: Visit Ollama and download the Ollama app.

Step 2: Launch the Ollama application on your system and use the built-in search feature to look for “Gemma 3” in the model catalog.

Step 3: Click on the Gemma 3 model and follow the prompts to download and install it. Once installed, use the Ollama interface to test the model by entering prompts and viewing responses.

By following these detailed steps, you can either try Gemma 3 instantly on Google AI Studio or download it for development through Hugging Face, Kaggle, or Ollama. Choose the method that best fits your workflow and hardware setup.

Mistral Small 3.1 vs Gemma 3: Features Comparison

Now let’s begin our comparison, starting with their features. Here’s a detailed comparison of the features of Gemma 3 and Mistral Small 3.1, based on available data:

Mistral Small 3.1 vs Gemma 3: Performance Comparison

In this section, we will be comparing Gemma 3 and Mistral 3.1 on some hands-on tasks and see how they perform. For a fair comparison, we’ll be trying out the same prompts on both the models, Here’s a list of tasks we’ll be trying out:

1. Object detection
2. Visual data extraction
3. Visual data extraction – printed text

Let’s get started.

Task 1: Object Detection

In this task, we will give the models an image of candies having animal symbols, and ask them to identify the animal on the candies.

Prompt: “Identity the animal in the image”

Input image:

Output 

Comparative Analysis

Both the models correctly identified the animal in the image as a turtle. However, Mistral 3.1 provided additional context by identifying the candies as M&Ms and mentioning the colours of the candies. Meanwhile, Gemma 3 described the turtle’s appearance in a more general manner.

Verdict: Both models successfully identified the object, but Mistral 3.1 provided slightly more detailed contextual information. Hence, Mistral 3.1 wins this round.

Score: Mistral 3.1: 1 | Gemma 3: 0

To test the models on their visual data extraction capabilities, we’ll provide them with an image of a plane and ask them to extract the vehicle code from it.

Prompt: “What is the code on the vehicle?”

Input Image:

Output:

Comparative Analysis:

Mistral 3.1 and Gemma 3 successfully identified the registration code from the airplane. But, Gemma 3 not only identified the code, but also added extra details, such as the aircraft model.

Verdict: Both models accurately extracted the code, but Gemma 3 offered more background details about the vehicle. So, Gemma 3 gets the point for this one.

Score: Mistral 3.1: 1 | Gemma 3: 1

For the last task, we’ll provide them with an image of a wifi receipt and ask them to extract the wifi password from it.

Prompt: “What is the password?”

Input Image:

Output:

Comparative Analysis:

Both models accurately extracted the password from the image. Additionally, Mistral 3.1 provided extra details about the username, traffic, price, validity, and other related information, making its output more comprehensive.

Verdict: Mistral 3.1 performed better in extracting and presenting structured data, offering more useful context. So, Mistral 3.1 gets another point for this task.

Score: Mistral 3.1: 2 | Gemma 3: 1

Final Score: Mistral 3.1: 2 | Gemma 3: 1

Performance Comparison Summary

Here’s a summary of the performance of both the models across the tasks we’ve tried out.

From this comparison, we’ve seen that Mistral 3.1 excels in structured data extraction and providing concise yet informative responses. Meanwhile, Gemma 3 performs well in object recognition and offers richer contextual details in some cases.

For tasks requiring fast, structured, and precise data extraction, Mistral 3.1 is the better choice. For tasks where context and additional descriptive information are important, Gemma 3 has an edge. Therefore, the best model depends on the specific use case.

Mistral Small 3.1 vs Gemma 3: Benchmark Comparison

Now let’s see how these two models have performed across various standard benchmark tests. For this comparison, we’ll be looking at benchmarks that test the models’ capabilities in handling text, multilingual content, multimodal content, and long-contexts. We will also be looking at the results on pretrained performance benchmarks.

Both Gemma 3 and Mistral Small 3.1 are notable AI models that have been evaluated across various benchmarks.

Text instruct benchmarks

From the graph we can see that:

• Mistral 3.1 consistently outperforms Gemma 3 in most benchmarks, particularly in GPQA Main, GPQA Diamond, and MMLU.
• HumanEval and MATH show near-identical performance for both models.
• SimpleQA shows minimal difference, indicating both models struggle in this category.
• Mistral 3.1  leads in reasoning-heavy and general knowledge tasks (MMLU, GPQA), whereas Gemma 3 closely competes in code-related benchmarks (HumanEval, MATH).

Multimodal Instruct Benchmarks

The graph visually illustrates that:

• Mistral 3.1 consistently outperforms Gemma 3 in most benchmarks.
• The largest performance gaps favoring Mistral appear in ChartQA and DocVQA.
• MathVista is the closest competition, where both models perform almost equally.
• Gemma 3  lags behind in document-based QA tasks but is relatively close in general multimodal tasks.

Multilingual and Long-context Benchmarks

From the graph we can see that:

For Multilingual Performance:

• Mistral 3.1 leads in European and East Asian languages.
• Both models are close in Middle Eastern and average multilingual performance.

For Long Context Handling:

• Mistral outperforms Gemma 3 significantly in long-context tasks, particularly in RULER 32k and RULER 128k.
• Gemma 3 lags more in LongBench v2 but remains competitive in RULER 32k.

Pretrained Performance Benchmarks

From this graph, we can see that:

• Mistral 3.1 consistently performs better in general knowledge, factual recall, and reasoning tasks.
• Gemma 3 struggles significantly in GPQA, where its performance is much lower compared to Mistral 3.1.
• TriviaQA is the most balanced benchmark, with both models performing nearly the same.

Conclusion

Both Mistral 3.1 and Gemma 3 are powerful lightweight AI models, each excelling in different areas. Mistral 3.1 is optimized for speed, low latency, and strong reasoning capabilities, making it the preferred choice for real-time applications like chatbots, coding, and text generation. Its efficiency and task specialization further enhance its appeal for performance-driven AI tasks.

On the other hand, Gemma 3 offers extensive multilingual support, multimodal capabilities, and a competitive context window, making it well-suited for global AI applications, document summarization, and content generation in diverse languages. However, it trades off some speed and efficiency compared to Mistral 3.1.

Ultimately, the choice between Mistral 3.1 and Gemma 3 depends on specific needs. Mistral 3.1 excels in performance-driven and real-time applications, while Gemma 3 is ideal for multilingual and multimodal AI solutions.

Frequently Asked Questions