AI Models Explained: GPT vs Claude vs Gemini vs Llama
A clear comparison of the major AI models. What each is good at, where they fall short, and how to choose the right one.
There are hundreds of AI models available, but most users only know ChatGPT. Here is a practical guide to the major model families, what each does well, and when to use which.
GPT (OpenAI)
OpenAI’s flagship model family and the most widely used large language models.
Strengths: Strong general knowledge, good at coding, reliable instruction-following, widely tested and documented, good structured output (JSON, tables).
Weaknesses: Can be verbose, sometimes refuses requests unnecessarily, relatively expensive for simple tasks, 128K context window is smaller than competitors.
Best for: General-purpose questions, coding help, structured data tasks, situations where reliability matters more than creativity.
Claude (Anthropic)
Anthropic’s Claude models are known for nuanced understanding and careful responses.
Strengths: Excellent at writing natural prose, strong instruction-following, 200K token context window (largest among premium models), honest about uncertainty, good at long-document analysis.
Weaknesses: Can be overly cautious with certain requests, not always the best at structured output, premium tiers are expensive.
Best for: Writing tasks, long document analysis, nuanced questions, tasks requiring careful reasoning, coding with explanation.
Gemini (Google)
Google’s Gemini models leverage Google’s infrastructure and data.
Strengths: Up to 2 million token context window, strong multimodal capabilities (text + image), fast response times on lightweight variants, well-integrated with Google services.
Weaknesses: Can produce less polished prose than Claude, reasoning depth sometimes lags behind GPT on complex tasks, evolving rapidly so quality varies between versions.
Best for: Very long documents (books, codebases), multimodal tasks, tasks where speed matters, research tasks.
Llama (Meta)
Meta’s open-source Llama models (available in multiple sizes) are available through various providers.
Strengths: Open source, available through many providers, good performance for the price, customizable, no vendor lock-in.
Weaknesses: Generally behind GPT and Claude on complex reasoning, smaller models have limited capability, requires provider hosting.
Best for: Cost-effective tasks, privacy-sensitive workloads (can be self-hosted), batch processing, tasks that do not require top-tier reasoning.
DeepSeek
DeepSeek models offer strong performance at competitive pricing, particularly for coding tasks.
Strengths: Excellent code generation, very competitive pricing, strong reasoning for the cost, good performance on benchmarks.
Weaknesses: Newer provider with less track record, smaller community, fewer integrations than established providers.
Best for: Coding tasks on a budget, cost-sensitive workloads, situations where GPT quality is overkill.
Mistral
French AI company Mistral produces efficient models that punch above their weight.
Strengths: Good efficiency (strong performance relative to model size), Mistral’s mixture-of-experts approach is fast, competitive pricing, European data governance.
Weaknesses: Smaller model ecosystem, less name recognition, fewer specialized variants.
Best for: European users concerned about data jurisdiction, tasks where speed and cost efficiency matter, general-purpose use at lower cost.
How to Choose
The honest answer: it depends on your task. Here is a quick decision framework:
| Need | Recommended |
|---|---|
| Best overall quality | GPT or Claude Opus |
| Best writing | Claude Sonnet |
| Best coding | GPT or DeepSeek |
| Longest documents | Gemini Pro (1M+ tokens) |
| Cheapest option | Llama or Gemini Flash |
| Best value | Claude Haiku or Gemini Flash |
The practical approach is to start with a good mid-tier model (Claude or GPT) and switch when you hit a limitation. With Chapeta, switching costs you one click.
A Note on Benchmarks
AI benchmarks (MMLU, HumanEval, MATH, etc.) provide rough guidance but do not capture real-world performance on your specific tasks. A model that scores 2% higher on a coding benchmark may not actually write better code for your project. Your own testing, on your own tasks, is the most reliable evaluation. See our model comparison guide for how to test effectively.