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Full Stack edited this page Mar 24, 2025 · 7 revisions

Here's a comprehensive comparison of the latest popular Large Language Models (LLMs), covering their use cases, applications, performance, server requirements, and implementation challenges.

🔥 LLM Comparison Table (Latest Models - 2024-2025)

Model LLaMA 3 (Upcoming) GPT-4 Turbo Claude 3 Mistral 7B Mixtral 8x7B (MoE) Gemini 1.5 Falcon 180B
Organization Meta OpenAI Anthropic Mistral AI Mistral AI Google DeepMind TII
Model Type Transformer Transformer Transformer Transformer Mixture of Experts (MoE) Transformer Transformer
Size (Parameters) 7B - 65B (Expected) Not disclosed (200B+ est.) Not disclosed 7B 8x7B (MoE) Not disclosed 180B
Architecture Dense Dense Dense Dense MoE (2/8 active) Dense Dense
Fine-tuning support Yes (Expected) Limited Limited Yes Yes Limited Yes
Open-source Yes (Expected) No No Yes Yes No Yes
Multi-modal (Text, Images, Code, Audio, Video) Expected ✅ Yes ❌ No ❌ No ❌ No ✅ Yes ❌ No
Training Data Multi-language, high-quality Broad internet-scale dataset Human-curated, safety-focused Optimized for efficiency Optimized for quality & speed Google’s dataset Large-scale web data
Performance vs GPT-4 Expected to be close Best performance Stronger in reasoning Strong in efficiency Stronger than GPT-3.5 Best for multi-modal tasks Good, but lower efficiency

🚀 Final Recommendations

  • Best for Business/Enterprise AI? → GPT-4 Turbo, Claude 3
  • Best Open-Source Alternative? → Mixtral 8x7B
  • Best for Local AI Deployment? → Mistral 7B
  • Best for Image + Video AI? → Gemini 1.5
  • Best for Real-Time AI Apps? → Mistral 7B, Mixtral 8x7B

Running Ollama

Run this in your favorite terminal

To view the list of models installed

ollama list

To run the selected model

ollama run llama3.2

Running the model using Postman

read

To send a Postman request to an Ollama API server, follow these steps:

1️⃣ Start the Ollama Server

First, ensure the Ollama server is running. You can start it with:

ollama serve

By default, it runs on http://localhost:11434.

2️⃣ Postman API Request to Ollama

Use the following POST request in Postman:

🔹 Generate a Chat Completion (Streaming Response)

  • Method: POST
  • URL: http://localhost:11434/api/generate
  • Headers:
    • Content-Type: application/json
  • Body (raw, JSON format):
{
  "model": "mistral",
  "prompt": "What is the capital of France?",
  "stream": false
}
  • Response Example:
{
  "response": "The capital of France is Paris.",
  "model": "mistral",
  "done": true
}

🔹 List Available Models

  • Method: GET
  • URL: http://localhost:11434/api/tags

📌 Response Example:

{
  "models": [
    {
      "name": "mistral",
      "digest": "sha256:abc123...",
      "modified_at": "2024-03-01T12:00:00Z"
    }
  ]
}

🔹 Pull a New Model

To download and use a new model, such as LLaMA 3:

  • Method: POST
  • URL: http://localhost:11434/api/pull
  • Body:
{
  "name": "llama3"
}

📌 Response:

{
  "status": "success"
}

🔹 Run a Model with System Messages

For better control, you can add system messages:

  • Method: POST
  • URL: http://localhost:11434/api/generate
  • Body:
{
  "model": "mistral",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "What are the benefits of AI?"
    }
  ]
}

📌 Response:

{
  "response": "AI offers benefits like automation, efficiency, and scalability.",
  "done": true
}

📌 Notes

  • Replace "mistral" with any model available (e.g., "llama3", "gemma", "mixtral").
  • Set "stream": true for streaming responses.
  • Ensure Ollama is running before sending requests.

References

Tools

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