The Core AI Models Explained (Without the Jargon)
GPT, Claude, Gemini, Llama, Mistral, Sonar — what they are, what makes them different, and how to choose between them.
The AI model landscape changes fast. The fundamentals don’t. Here’s a current-state reference you can return to.
The four labs that matter
OpenAI (ChatGPT family)
The most well-known. GPT-4o is the workhorse model — fast, reliable, multimodal. GPT-5 is the flagship — slower, smarter, better at complex reasoning. The default for most users.
Anthropic (Claude family)
The operator’s favorite for serious work. Claude Sonnet is the workhorse. Claude Opus is the flagship — slower, dramatically better at long-form writing and complex code. Best long-context handling of any major model.
Google (Gemini family)
Strongest multimodal capabilities. Native to Google Workspace. Massive context windows. Underrated for analysis tasks involving images, video, or audio alongside text.
Meta (Llama family)
Open weights. You can run it yourself. Increasingly competitive on quality. The right choice if data sovereignty or fine-tuning is critical to your use case.
The next tier
Mistral
French. Open and proprietary models. Strong European data privacy posture. Underrated for non-English work.
Sonar (Perplexity)
Built for citation-grounded search. Used inside Perplexity’s products. Not a general-purpose chatbot — built for one specific job.
DeepSeek, Qwen, Grok
Worth watching. Not yet default choices for most operators, but improving rapidly. Strong on specific benchmarks.
What “context window” actually means
The context window is everything the model can “see” at once: your prompt, attachments, and its own output so far.
- GPT-4o: 128K tokens (~100K words)
- Claude Opus: 200K tokens (~150K words)
- Gemini 2.5 Pro: 1M+ tokens (~750K words)
For most use cases, 100K is plenty. For document synthesis, 200K+ matters. For analyzing entire codebases or video files, 1M+ becomes relevant.
Speed vs quality tradeoffs
Every lab offers a “fast” model and a “smart” model:
| Tier | OpenAI | Anthropic | |
|---|---|---|---|
| Fast | GPT-4o mini | Haiku | Flash |
| Workhorse | GPT-4o | Sonnet | Pro |
| Flagship | GPT-5 | Opus | Ultra |
For 80% of tasks, the workhorse tier is right. The fast tier is for high-volume, simple tasks (classifications, simple rewrites). The flagship tier is for complex reasoning, long-form, or when stakes are high.
How to actually pick
Stop reading benchmark comparisons. Pick a daily driver, use it for a month, and form your own opinion. The benchmarks lie about real-world feel. Your hands and eyes are the right judges.
If you’re starting fresh in 2026:
- Default: Claude Sonnet for daily work + Perplexity for research
- If you need multimodal: Add Gemini 2.5 Pro
- If you need image generation: Add ChatGPT (DALL·E built in) or Midjourney
- If you have a privacy requirement: Look at self-hosted Llama or Mistral
What’s changing
Model capabilities double roughly every 6-9 months. Pricing per token drops roughly 4x per year. Context windows are growing. Multimodal is becoming default. The gap between top-tier labs is narrowing.
Stay loyal to a workflow, not a model. Models change. Workflows compound.