Explainers

AI Jargon, Decoded: Tokens, Context Windows, Open Weights and More

The plain-English glossary you need to understand AI news, model launches and our weekly rankings.

By The AI Standings Desk · · 2 min read

In this guide
  1. Model
  2. Token
  3. Input vs. output tokens
  4. Context window
  5. Benchmark
  6. Arena / Elo score
  7. Open weights
  8. Reasoning / effort levels
  9. Agent
  10. Multimodal / Omni
  11. Diffusion model
  12. Hallucination
  13. Flash / Mini / Luna / Haiku
  14. Pro / Opus / Astra / Fable

AI news is full of words nobody explains. This is the glossary we wish we'd had, and it covers every term you'll see in our weekly standings.

Model

The "brain" behind an AI app. ChatGPT is an app, GPT-6 Sol is a model. One app can switch between several models, and the same model can power many different apps.

Token

The unit AI models read and write in, roughly three-quarters of a word. "Hamburger" might be two or three tokens. AI companies charge developers per million tokens, which is why you'll see prices like "$2 in / $10 out".

Input vs. output tokens

Input is what you send (your question plus any documents). Output is what the AI writes back. Output usually costs more, often 4–5x more. Try it in our cost calculator.

Context window

How much the model can "hold in mind" at once, measured in tokens. A bigger context window means you can give it a whole book or a big codebase in one go.

Benchmark

A standardised test for AI. Examples include Terminal-Bench (can it get real tasks done in a computer terminal?) and AutomationBench (can it automate multi-step work?). Useful, but labs pick which ones to publish, so read them with a pinch of salt.

Arena / Elo score

LMArena shows people two anonymous AI answers side by side and asks which is better. Millions of votes produce an Elo score, the same kind of rating chess uses. A difference of a few points is basically a tie.

Open weights

A model whose "brain file" you can download and run yourself, on your own computer or server. That's good for privacy and customisation. It isn't always "open source" in the strict sense, because the license can still restrict how you use it. See our Open-Weight Watch.

Reasoning / effort levels

Many 2026 models can "think longer" before answering. Higher effort gives better results on hard problems, but it's slower and uses more tokens, so it costs more.

Agent

An AI that doesn't just answer, but takes actions: browsing, clicking, running code, sending emails, often over many steps. Agentic ability is one of the biggest differences between the top models this year.

Multimodal / Omni

A model that handles more than text, such as images, audio and video, as input, output or both. Google's Gemini Omni models currently top the video leaderboard.

Diffusion model

A different way of generating content. Instead of writing one word after another, it refines everything in parallel. It's traditionally used for images and video, and now also for fast text models like Mercury 2.5.

Hallucination

When an AI confidently states something false. Newer models hallucinate less (OpenAI says GPT-6 Sol makes about half as many mistakes as GPT-5.6 Sol), but it still happens. Always check important facts.

Flash / Mini / Luna / Haiku

Different labs' names for their smaller, faster, cheaper models. They're often good enough for everyday tasks at a fraction of the price.

Pro / Opus / Astra / Fable

The labs' names for their biggest, smartest, most expensive models.

Now you speak fluent AI. Go see who's on top this week →

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