The GenAI Landscape

The GenAI space is moving fast. New models drop every few weeks. This tutorial helps you understand the major players, compare their offerings, and make informed decisions about which models to use for your projects.

Major Model Providers

OpenAI

The company that started the current AI boom with ChatGPT. They offer the GPT family of models.

• Flagship: GPT-4o (multimodal — text, image, audio)
• Budget: GPT-4o mini (fast and cheap)
• API: api.openai.com
• Chat product: ChatGPT
• Strengths: Broad general knowledge, strong coding, massive ecosystem
• Pricing model: Pay per token

Anthropic

Founded by former OpenAI researchers, focused on AI safety. They build Claude.

• Flagship: Claude 3.5 Sonnet (exceptional at coding and analysis)
• Budget: Claude 3.5 Haiku (fast, affordable)
• API: api.anthropic.com
• Chat product: claude.ai
• Strengths: Code generation, long-context understanding, safety, structured output
• Pricing model: Pay per token

Google

Google's DeepMind division builds the Gemini family of models.

• Flagship: Gemini 1.5 Pro (massive 1M+ token context)
• Budget: Gemini 1.5 Flash (fast, cheap)
• API: Google AI Studio / Vertex AI
• Chat product: Gemini (gemini.google.com)
• Strengths: Huge context window, multimodal, Google integration
• Pricing model: Pay per token (generous free tier)

Meta

Meta (Facebook) leads the open-source AI movement with Llama.

• Flagship: Llama 3.1 405B
• Popular: Llama 3 70B, Llama 3 8B
• API: Via third-party providers (Together AI, Groq, Fireworks, etc.)
• Chat product: meta.ai
• Strengths: Open source, can self-host, no API costs if you run it yourself
• Pricing model: Free to download; pay hosting providers if not self-hosting

Mistral AI

A French AI company with strong open-source and commercial offerings.

• Flagship: Mistral Large
• Open source: Mistral 7B, Mixtral 8x7B
• API: api.mistral.ai
• Strengths: Efficient architectures, European data sovereignty, strong multilingual
• Pricing model: Pay per token

Model Comparison Table

*Free to download; compute costs apply if using cloud hosting.

Open Source vs Closed Source

Closed Source (Proprietary)

Models like GPT-4o and Claude 3.5 Sonnet are closed source — you can only access them through APIs.

Advantages:

• Highest capability models (currently)
• No infrastructure management
• Always up-to-date
• Easy to get started

Disadvantages:

• Per-token costs add up
• Data sent to third-party servers
• Vendor lock-in
• Rate limits
• Model could change without notice

Open Source

Models like Llama 3, Mistral, and Mixtral can be downloaded and run yourself.

Advantages:

• No per-token costs (only compute)
• Full data privacy — runs on your servers
• Customize and fine-tune for your use case
• No rate limits
• Reproducible — the model doesn't change

Disadvantages:

• Lower capability than top closed models (though the gap is closing)
• Need GPU infrastructure (expensive)
• You handle updates, security, scaling
• More technical setup

The Middle Ground: Open Models via APIs

Services like Together AI, Groq, Fireworks AI, and Replicate host open-source models for you — so you get the benefits of open models without managing infrastructure.

// Using Llama 3 via Together AI (same OpenAI-compatible format)
const response = await fetch("https://api.together.xyz/v1/chat/completions", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_TOGETHER_API_KEY",
  },
  body: JSON.stringify({
    model: "meta-llama/Llama-3-70b-chat-hf",
    messages: [{ role: "user", content: "Explain Docker in simple terms" }],
    temperature: 0.7,
    max_tokens: 1024,
  }),
});

When to Use Which Model

Decision Framework

What are you building?
│
├─ Production app with high quality needs?
│  ├─ Heavy coding/analysis → Claude 3.5 Sonnet
│  ├─ General purpose → GPT-4o
│  └─ Very long documents → Gemini 1.5 Pro
│
├─ Budget-conscious or high volume?
│  ├─ Simple tasks → GPT-4o mini or Claude 3.5 Haiku
│  ├─ Batch processing → Gemini 1.5 Flash
│  └─ Self-hosting option → Llama 3 70B
│
├─ Data privacy is critical?
│  ├─ Can manage infrastructure → Llama 3 (self-hosted)
│  └─ Need managed service → Azure OpenAI or GCP Vertex AI
│
└─ Experimenting / learning?
   └─ Start with → GPT-4o mini (cheapest with good quality)

Quick Recommendations

Pricing Overview

Costs are per million tokens (as of 2024-2025). Prices change frequently — always check the provider's pricing page.

Input Token Pricing (per 1M tokens)

What Does This Mean in Practice?

A chatbot handling 1,000 conversations/day:
  - Average 500 tokens input + 500 tokens output per conversation
  - Monthly: 1,000 × 30 × 1,000 = 30M tokens

  GPT-4o mini: ~$5-20/month
  GPT-4o:      ~$75-375/month
  Claude 3.5 Sonnet: ~$90-540/month

For most startups and side projects, costs are very manageable.

Multi-Model Strategy

Smart teams don't use just one model. They route different tasks to different models:

// Route to the best model for each task
function selectModel(task: string): string {
  switch (task) {
    case "code-generation":
      return "claude-3-5-sonnet-20241022";
    case "simple-classification":
      return "gpt-4o-mini";
    case "long-document-analysis":
      return "gemini-1.5-pro";
    case "creative-writing":
      return "gpt-4o";
    default:
      return "gpt-4o-mini"; // Default to cheapest
  }
}

Key Takeaways

• The "best" model depends on your specific use case, budget, and requirements
• Closed-source models (GPT-4o, Claude) offer the highest quality but cost per token
• Open-source models (Llama, Mistral) offer privacy and cost control at scale
• Start cheap (GPT-4o mini), upgrade when you need more capability
• Consider a multi-model strategy — route tasks to the best model for each job
• Prices are dropping rapidly — what's expensive today may be cheap in 6 months

What's Next?

Let's go deeper into how LLMs actually work — understanding attention, embeddings, and next-token prediction will help you reason about their capabilities and limitations.

What to ask your AI: "I'm building [describe your app]. Which AI model should I use and why? My budget is [amount] per month."

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