Understanding AI Models: A Complete Guide

Understanding AI Models

Choosing the right AI model is one of the most important decisions for your project. This guide covers all major providers, their models, and when to use each.

The AI Model Landscape

┌─────────────────────────────────────────────────────────────┐
│                    PROPRIETARY MODELS                        │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐    │
│  │  OpenAI  │  │Anthropic │  │  Google  │  │ Mistral  │    │
│  │  GPT-4o  │  │  Claude  │  │  Gemini  │  │  Large   │    │
│  └──────────┘  └──────────┘  └──────────┘  └──────────┘    │
├─────────────────────────────────────────────────────────────┤
│                    OPEN SOURCE MODELS                        │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐    │
│  │  Llama   │  │ Mistral  │  │  Qwen    │  │  Gemma   │    │
│  │   3.1    │  │   7B     │  │   2.5    │  │    2     │    │
│  └──────────┘  └──────────┘  └──────────┘  └──────────┘    │
└─────────────────────────────────────────────────────────────┘

Proprietary Model Providers

OpenAI

Company: OpenAI (San Francisco, USA) Founded: 2015 API: api.openai.com

Strengths:

• Industry-leading general intelligence
• Excellent at following instructions
• Strong vision capabilities
• Fastest inference speeds
• Extensive documentation and ecosystem

Weaknesses:

• More expensive than alternatives
• Data may be used for training (opt-out available)
• Rate limits on newer models

Best For: Production applications, general-purpose AI, vision tasks

Anthropic

Company: Anthropic (San Francisco, USA) Founded: 2021 API: api.anthropic.com

Strengths:

• Best-in-class for coding and analysis
• Largest context window (200K tokens)
• Strong safety and alignment
• Excellent at nuanced tasks
• Very honest about limitations

Weaknesses:

• Smaller ecosystem than OpenAI
• Can be overly cautious
• No official vision in all models

Best For: Coding assistance, long document analysis, research

Google (Gemini)

Company: Google DeepMind API: ai.google.dev

Strengths:

• Largest context windows (up to 2M tokens)
• Native multimodal (text, image, video, audio)
• Excellent at code generation
• Deep Google integration

Weaknesses:

• API can be less intuitive
• Availability varies by region
• Less established for production

Best For: Multimodal tasks, massive documents, Google Cloud users

Mistral AI

Company: Mistral AI (Paris, France) Founded: 2023 API: api.mistral.ai

Strengths:

• European company (GDPR-friendly)
• Open weights for some models
• Excellent price/performance
• Strong multilingual support

Weaknesses:

• Smaller model selection
• Less documentation
• Newer ecosystem

Best For: European deployments, cost-conscious projects, multilingual

Open Source Models

Meta Llama

License: Llama 3.1 Community License (commercial use allowed)

Strengths:

• State-of-the-art open source quality
• Full control over your data
• No per-token costs
• Can fine-tune for your use case

Best For: Self-hosting, privacy-sensitive applications, custom training

Mistral (Open Weights)

License: Apache 2.0 (fully open)

Best For: Efficient inference, MoE architecture experiments

Other Notable Open Source

How to Choose

Decision Matrix

By Use Case

Customer Support Bot → Claude Sonnet 4 (nuanced, safe) or GPT-4o mini (cheap, fast)

Code Assistant → Claude Sonnet 4 (best at code) or Codestral

Document Analysis → Gemini 1.5 Pro (massive context) or Claude (200K context)

Privacy-Sensitive Application → Self-hosted Llama 3.1

Startup on a Budget → GPT-4o mini or Claude Haiku

Running Open Source Models

With Ollama (Easiest)

# Install
curl -fsSL https://ollama.com/install.sh | sh

# Run Llama 3.1
ollama run llama3.1

# Run Mistral
ollama run mistral

With vLLM (Production)

pip install vllm

# Serve Llama 3.1 8B
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Meta-Llama-3.1-8B-Instruct

Cloud GPU Options

Summary

The AI model landscape is rich with options. For most projects:

1. Start with a proprietary API (OpenAI or Anthropic) for quick development
2. Evaluate open source once you understand your needs
3. Consider hybrid - use cloud APIs for complex tasks, local models for simple ones

See our Cost Estimation Guide to calculate your expected costs, or our Hosting Comparison to decide where to deploy.