The AI Model Landscape
There are hundreds of AI models and new ones launch every month. This page cuts through the noise and gives you a mental map: who the major players are, what the confusing names mean, and how to pick a model for everyday use.
Model vs Product - Know the Difference
The most common confusion for newcomers: mixing up the model (the AI engine) with the product (the thing you log into).
| Product (what you use) | Underlying Model | Made by |
|---|---|---|
| ChatGPT | GPT-5 (Instant, Thinking, Pro) | OpenAI |
| Claude.ai | Claude Sonnet, Claude Opus, Claude Haiku | Anthropic |
| Gemini (gemini.google.com) | Gemini 3.1 Pro, Gemini Flash | |
| Meta AI (on WhatsApp, Instagram) | Llama 4 | Meta |
| Grok (on X/Twitter) | Grok 4.3, Grok 4.7 | xAI |
The same underlying model can power multiple products. And the product you use may offer different models on different pricing tiers - for example, ChatGPT Free uses a lighter model while ChatGPT Plus gives you access to more powerful ones.
The two main model categories - open-weight models you can run locally; closed models you access via APIs with per-use pricing
The Big Four Labs
OpenAI
Made ChatGPT, the most widely used AI product. Model families: GPT (general purpose) and o-series (reasoning - see below). Known for broad capability, a huge developer ecosystem, and being the first to go mainstream.
Anthropic
Makes the Claude family. Strong on safety, long documents, and coding. The Claude name comes with tiers: Haiku (fast/cheap), Sonnet (balanced),Opus (most powerful). Popular with developers and professionals.
Google DeepMind
Makes the Gemini family. Stands out for multimodal ability (images, audio, video), very long context windows (up to 1 million tokens), and deep integration with Google Search and Workspace. Tiers: Flash (fast), Pro, Ultra.
Meta AI
Makes Llama - the world's most popular open-weight model family. Unlike the others, Meta releases Llama for free download. Anyone can run it locally, fine-tune it, or build products with it. Llama 4 matches frontier proprietary performance in many tasks.
Other notable players
Mistral AI (France) - efficient open-weight models, strong in coding and European languages. DeepSeek (China) - open-weight reasoning models that compete with frontier closed models at a fraction of the cost. Microsoft - Phi family of small language models (SLMs) designed to run on devices. xAI - Grok models, integrated with X/Twitter real-time data.
Frontier vs Open-Weight Models
This is the most important split in the model landscape right now:
Frontier (Proprietary) Models
- You access them via API or web interface
- The weights are never released - you can't download them
- Examples: GPT-5, Claude Opus, Gemini Ultra
- Best performance on hard tasks
- Your data is sent to the company's servers
- Pay per use (tokens) or subscription
Open-Weight Models
- You download the model and run it yourself
- No data leaves your computer or server
- Examples: Llama 4, Mistral, DeepSeek-R1, Phi-3
- Free to run (you pay for hardware/cloud compute)
- Can be customised and fine-tuned
- Usually slightly behind frontier models on hardest tasks
Note: "Open-weight" is not the same as "open-source." Open-weight means the trained model weights are released. Open-source would mean the training code and data are also released. Most open models (including Llama) only release the weights.
Model Tiers: Speed vs Capability
Every major AI lab offers multiple tiers of model - smaller/faster/cheaper and larger/smarter/more expensive. Knowing the tier naming conventions saves a lot of confusion:
| Tier | OpenAI | Anthropic (Claude) | Google (Gemini) | Best for |
|---|---|---|---|---|
| Fast / Cheap | GPT-6 Luna | Claude Haiku | Gemini Flash | High-volume, simple tasks, cost-sensitive apps |
| Balanced | GPT-5.6 Terra | Claude Sonnet | Gemini Pro | Most everyday tasks - best value per token |
| Most Capable | GPT-6 Astra | Claude Opus | Gemini Ultra | Hard problems, complex reasoning, highest quality |
For most tasks, the balanced tier is the right default. Use the fast tier for bulk/simple work, and the top tier only when the balanced tier fails.
Reasoning Models: A New Type
Since late 2024, a new category of model has emerged: reasoning models. Instead of immediately generating a response, they spend extra time "thinking" - producing a long internal chain-of-thought - before giving you an answer.
These are dramatically better at maths, logic puzzles, complex coding, and planning, but they're slower and more expensive. Key reasoning models:
- GPT-6 Astra - OpenAI's flagship, with adaptive thinking built in rather than a separate model family
- DeepSeek-R1 - Open-weight reasoning model matching frontier performance
- Claude Fable 5.1 with adaptive thinking - Anthropic's implementation, on by default
See the page Reasoning Models Explained in this section for the full picture.
Multimodal Models
Modern frontier models are multimodal - they can process more than text. Today's leading models understand:
- Images - analyse photos, diagrams, charts, screenshots
- Audio - transcribe speech, answer questions about audio recordings
- Documents/PDFs - read and reason over uploaded files
- Video - some models (Gemini) can process video frames
All of GPT-5.6 Terra, Claude (Sonnet, Opus), and Gemini Pro/Ultra handle text and images. Audio and video support varies - check the provider's current docs.
Model Versions: Why the Name Keeps Changing
You'll see version numbers like GPT-4, GPT-4o, GPT-5, GPT-6 Astra - from just one company. Here's how to read them:
- GPT-4 → GPT-4o - The "o" stood for "omni" (multimodal). A major capability upgrade using the same generation number.
- GPT-5 → GPT-5.6 → GPT-6 - Generational jumps, each substantially more capable than the last. Within GPT-6, tiers are now named rather than numbered: Astra (flagship), Luna (cheap/fast), and GPT-5.6's Sol/Terra/Luna tiers before it.
- o1, o3, o4 (retired) - A separate reasoning-focused model series that existed alongside GPT-4/GPT-5. The numbers didn't follow normal versioning (there was no o2 - skipped to avoid confusion with a UK telecom brand).
- Claude 3 Sonnet → Claude 3.5 Sonnet → Claude 4.6 Sonnet → Claude Sonnet 5 - Minor and major version bumps. Always check the release date if you need the latest.
Practical tip
Model names get stale fast. When choosing a model, always check current benchmarks rather than relying on name recognition alone. A "new" model can be significantly better than a well-known older one.
A Simple Decision Guide
For everyday tasks, this covers most situations:
| Task | Good starting point |
|---|---|
| Writing, editing, summarising | Claude Sonnet 5 or GPT-5.6 Terra |
| Coding help, debugging | Claude Sonnet 5 or GPT-5.6 Terra - try GPT-6 Astra for hard bugs |
| Hard maths, logic, planning | GPT-6 Astra, Claude Fable 5.1, or DeepSeek-R1 |
| Long documents (100+ pages) | Gemini 3.1 Pro (1M token context) |
| Image analysis | GPT-5.6 Terra, Claude Sonnet, Gemini Pro |
| Privacy-sensitive / offline | Llama 4 via Ollama (runs locally) |
| High-volume / cost-sensitive | Claude Haiku or Gemini Flash |
Where to Compare Models
- Chatbot Arena (lmarena.ai) - humans rate head-to-head model battles blind. The most human-feedback-based leaderboard. Great for general usefulness.
- Artificial Analysis (artificialanalysis.ai) - tracks speed, price, and quality across all major models. Best for choosing based on value per token.
- Hugging Face Open LLM Leaderboard - focuses on open-weight models and academic benchmarks. Good for comparing local/self-hosted options.
- Provider documentation - always check the latest model pages from OpenAI, Anthropic, and Google as benchmarks go out of date fast.