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<\/a>, 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\u2019ll explore their features, evaluate their performance on benchmark tests, and conduct some hands-on trials to find out the better model.<\/p>\n Mistral 3.1 is the latest large language model<\/a> (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.<\/p>\n Mistral 3.1 is available through multiple platforms. You can either download and run it locally via Hugging Face<\/a> or access it using the Mistral AI API.<\/p>\n You can download Mistral 3.1 Base and Mistral 3.1 Instruct for direct use from Hugging Face. Here\u2019s how to do it:<\/p>\n Step 1: Install vLLM Nightly<\/strong><\/p>\n Open your terminal and run this command to install vLLM (this also installs the required mistral_common package):<\/p>\n You can verify the installation by running:<\/p>\n Step 2: Prepare Your Python Script<\/strong><\/p>\n Create a new Python file (e.g., Step 3: Run the Script Offline<\/strong><\/p>\n The model will load locally and generate a response based on your prompts.<\/p>\n You can also access Mistral 3.1 via API. Here are the steps to follow for that.<\/p>\n You can integrate this API key into your applications to interact with Mistral 3.1.<\/p>\n 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.<\/p>\n Gemma 3 is readily accessible across multiple platforms such as Google AI Studio, Hugging Face, Kaggle, and more.<\/p>\n This option lets you interact with Gemma 3 in a pre-configured environment without installing anything on your own machine.<\/p>\n Step 1:<\/strong> Open your web browser and go to Google AI Studio<\/a>.<\/p>\n Step 2:<\/strong> Log in with your Google account. If you don\u2019t have one, create a Google account.<\/p>\n Step 3:<\/strong> Once logged in, use the search bar in AI Studio to look for a notebook or demo project that uses \u201cGemma 3\u201d.<\/p>\n Tip: Look for projects titled with \u201cGemma 3\u201d or check the \u201cCommunity Notebooks\u201d section where pre-configured demos are often shared.<\/p>\n Step 4: <\/strong>Launch the demo by following the below steps.<\/p>\n Step 5:<\/strong> Follow the instructions in the notebook to start using the model. You can modify the input text, run cells, and see the model\u2019s responses in real-time all without any local setup.<\/p>\n 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.<\/p>\n Step 1: <\/strong>Visit Hugging Face<\/a>.<\/p>\n Step 2: <\/strong>Use the search bar to type \u201cGemma 3\u201d and click on the model card that corresponds to Gemma 3.<\/p>\n Step 3: <\/strong>Download the model using the \u201cDownload\u201d<\/strong> button or clone the repository via Git.<\/p>\n If you are using Python, install the Transformers library:<\/p>\n Step 4: Load and use the model in your code. For this, you can create a new Python script (e.g., Run your script locally to interact with Gemma 3.<\/p>\n Step 1:<\/strong> Open Kaggle<\/a> in your browser.<\/p>\nWhat is Mistral 3.1?<\/h2>\n
Key Features of Mistral 3.1<\/h3>\n
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How to Access Mistral 3.1<\/h2>\n
1. Accessing Mistral 3.1 via Hugging Face<\/h3>\n
pip install vllm --pre --extra-index-url https:\/\/wheels.vllm.ai\/nightly --upgrade<\/code><\/pre>\npython -c \"import mistral_common; print(mistral_common.__version__)\"<\/code><\/pre>\noffline_inference.py<\/code>) and add the following code. Make sure to set the model_name variable to the correct model ID (for example, \u201cmistralai\/Mistral-Small-3.1-24B-Instruct-2503<\/code>\u201c):<\/p>\nfrom vllm import LLM\nfrom vllm.sampling_params import SamplingParams\n\n# Define a system prompt (you can modify it as needed)\nSYSTEM_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.\"\n\n# Define the user prompt\nuser_prompt = \"Give me 5 non-formal ways to say 'See you later' in French.\"\n\n# Set up the messages for the conversation\nmessages = [\n {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n {\"role\": \"user\", \"content\": user_prompt},\n]\n\n# Define the model name (make sure you have enough GPU memory or use quantization if needed)\nmodel_name = \"mistralai\/Mistral-Small-3.1-24B-Instruct-2503\"\n\n# Initialize the LLM from vLLM with the specified model and tokenizer mode\nllm = LLM(model=model_name, tokenizer_mode=\"mistral\")\n\n# Set sampling parameters (adjust max_tokens and temperature as desired)\nsampling_params = SamplingParams(max_tokens=512, temperature=0.15)\n\n# Run the model offline and get the response\noutputs = llm.chat(messages, sampling_params=sampling_params)\n\n# Print the generated text from the model's response\nprint(outputs[0].outputs[0].text)<\/code><\/pre>\n\n
python offline_inference.py<\/code><\/li>\n<\/ol>\nImportant Considerations<\/h4>\n
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2. Accessing Mistral 3.1 via API<\/h3>\n
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<\/figure>\nWhat is Gemma 3?<\/h2>\n
Key Features of Gemma 3<\/h3>\n
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How to Access Gemma 3<\/h2>\n
1. Accessing Gemma 3 on Google AI Studio<\/h3>\n
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2. Accessing Gemma 3 on Hugging Face, Kaggle, and Ollama<\/h3>\n
A. Hugging Face<\/h4>\n
pip install transformers<\/code><\/pre>\ngemma3_demo.py<\/code>) and add code similar to the snippet below:<\/p>\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nmodel_id = \"your-gemma3-model-id\" # replace with the actual model ID from Hugging Face\nmodel = AutoModelForCausalLM.from_pretrained(model_id)\ntokenizer = AutoTokenizer.from_pretrained(model_id)\n\nprompt = \"What is the best way to enjoy a cup of coffee?\"\ninputs = tokenizer(prompt, return_tensors=\"pt\")\noutputs = model.generate(**inputs, max_new_tokens=50)\nprint(tokenizer.decode(outputs[0], skip_special_tokens=True))<\/code><\/pre>\nB. Kaggle<\/h4>\n
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