MAX’S NOTES

ChatGPT, Claude, Gemini and other AI assistants: what is the difference?

They can all chat, write, and explain. Yet similar chat windows can hide different models, tools, data access, and usage rules. Here is a beginner-friendly guide without a mythical “smartest AI” ranking.

Skillcraft editorial team · Max character · 2026-11-13

You ask ChatGPT a question, then repeat it in Claude and Gemini. All three give plausible answers. Sometimes they sound alike; sometimes they take entirely different approaches. Is this the same technology with different branding?

The short answer: they share important technical ideas, but they are not the same product. Large language models operate on broadly similar principles. The model, training, available tools, and connected data can change the result.

What is an LLM?

LLM stands for *large language model*. During training, it learns patterns from very large collections of text and other data. When responding, it builds an output step by step using units called tokens. Modern AI systems can do much more than complete sentences: they can reason through tasks, handle images, and use external tools.

Think of it this way: the model is the engine; the chat product is the car. Knowing what engine a car has does not tell you whether it includes navigation or a useful dashboard.

This explains a common confusion:

GPT is a family of models from OpenAI; ChatGPT is an app that combines models with search, files, and other tools.
Claude and Gemini name both model families and user-facing assistants from Anthropic and Google.
Perplexity is primarily an AI search-and-answer product that can use several models.
Llama is a model family from Meta that developers can build into their own applications, not the name of one universal chat app.

So “Which LLM is better, ChatGPT or Perplexity?” mixes up a model and a complete product.

What most of these assistants have in common

They work with natural language. You can request explanations, draft emails, translations, summaries, and help with code.

They depend on context. Clear instructions, useful source material, and a defined output format help. Still, a long conversation or an uploaded file does not guarantee that every detail will be used.

They can be confidently wrong. A convincing answer is not necessarily a verified fact. An invented date or source stays wrong even when the writing is polished.

They do not automatically know today's news. Current information requires an actual search or connected source. “AI” alone does not mean continuous internet access.

Where the differences really come from

1. The underlying model. Companies use different data, architectures, and training approaches. Results may differ in reasoning, writing, coding, languages, and reliability. What matters is the exact model version and task, not just the brand.

2. Tools around the model. An app may analyze a spreadsheet, produce an editable document, or work with connected business software. These are capabilities of the whole product, not necessarily of its LLM alone.

3. Information sources. Ask whether a claim comes from training, an uploaded document, a web search, or a connected database. Even a cited link needs checking.

4. Access and limits. Features, speed, request limits, file support, and geographical availability can differ by plan, region, and settings. Privacy terms matter too, especially before submitting workplace information.

Who is who? A quick guide

ChatGPT — OpenAI. A general-purpose assistant with features for text, files, images, spreadsheets, voice, web search, and research. Availability varies by plan and mode.

Claude — Anthropic. An assistant for conversation, document analysis, coding, and workflows. One distinctive product feature is Artifacts: editable outputs such as documents, visualizations, and interactive projects.

Gemini — Google. A multimodal model family and an app. Its ecosystem can connect to Google services such as Gmail, Drive, and Docs where supported and authorized.

Perplexity. A product built around web search and answers that cite sources. It can route requests across different models, so it is not quite the same category as GPT or Llama.

Grok. An assistant from xAI, with chat, file handling, voice, and image/video creation tools. Features vary by mode and access.

DeepSeek and Qwen. Both chat products and model families, with published models and developer resources. Some model versions can be deployed independently, although that requires suitable hardware, configuration, and license checks.

Llama — Meta. A clear example of the model-versus-app distinction. Developers use Llama models in their own products; access to model weights does not remove licensing conditions.

Mistral AI. Develops its own models and offers Vibe (formerly Le Chat), a product for work and coding tasks.

This is a map of the landscape, not a quality ranking. Features change rapidly.

Why does the same prompt produce different answers?

Imagine three editors who received the same assignment but studied different materials, use different references, and have different ideas of how much detail to include. Language models have something similar going on.

The output depends on the model and version, prompt wording, conversation history, selected mode, and tools. Repeating a prompt with one model will not always produce identical wording.

For example, asked to compare two contracts, one assistant might write a smooth summary, another a differences table, while a third asks a clarifying question. But a neat format does not prove it found every difference.

A five-minute experiment

Instead of comparing slogans, compare actual outputs. Paste this fictional example into several services:

> “A fictional coffee shop had 12% more orders this week, but its average order value fell and there is no profit data. Give three conclusions. Separate facts from assumptions. Identify missing information. Do not claim profit increased.”

Assess four things: Did it invent numbers? Did it separate facts and assumptions? Did it note what data is missing? Is the result actually useful?

Use the same wording and record the date and model versions. One example cannot establish an overall winner; another task might change the outcome.

So which assistant should you choose?

For explanations, drafts, and brainstorming, several assistants may do the job well. Differences become more important with files, search, coding, connected data, and recurring workflows.

My rule of thumb: start with your task, then identify the tools you need, and only then consider the brand. For important decisions, independently check source documents and facts, no matter how confident a chatbot sounds.

You do not need one permanent winner. One service may suit research, another document creation. That is normal: different jobs call for different tools.

*Information checked on November 12, 2026. Features vary by region, plan, and settings and can change.*