Claude, GPT or Gemini: which AI model to choose
How Claude, GPT and Gemini differ: twelve criteria, three tiers in every family, which model for which task, how to choose on your own examples and common mistakes.
In short
Claude, GPT and Gemini are the three leading families of language models, and for most business tasks all three are good enough — the difference is in the details. Claude is strongest in long documents, careful following of instructions, code and a calm, precise tone. GPT has the widest ecosystem: voice, images, ready integrations and the most familiar interface. Gemini handles the longest context and video and fits naturally into Google products. The right choice is made not by rankings but on 30–50 of your own examples: the cheapest model that passes them wins, and different tasks of one product can use different models.
In short: which one to choose
If the task is working with documents — contracts, instructions, a knowledge base, long letters — start with Claude: it reads carefully, keeps to the rules it was given and rarely adds what was not asked. If the product needs voice, image generation or a ready ecosystem of plug-ins, start with GPT. If the main material is video, very long files or data in Google Workspace, start with Gemini.
Start is not the end: the final word belongs to a test on your own examples. Quite often a fast, inexpensive model of any family passes it — and then a flagship would only multiply the bill.
- Documents and instructions — Claude
- Voice, images, ecosystem — GPT
- Video and very long context — Gemini
Claude, GPT and Gemini: a detailed comparison
Twelve criteria side by side. The positions are relative: each family updates several times a year, and the leader in a narrow area changes.
| Criterion | Claude | GPT | Gemini |
|---|---|---|---|
| Company | Anthropic | OpenAI | |
| Strongest at | documents, code, instructions | versatility and ecosystem | long context and video |
| Tone of answers | calm, precise | lively, eager | concise, factual |
| Long documents | very good | good | the longest window |
| Following instructions | very strict | good | good |
| Code | one of the strongest | strong | strong |
| Pictures as input | yes | yes | yes, plus video and audio |
| Generating images | no | yes | yes |
| Voice in real time | in the apps | yes, also through the API | yes, also through the API |
| Tools and agents | MCP, computer use | function calls, agents | function calls, Google search |
| Data through the API | not used for training | not used for training | not used on the paid tier |
| Through cloud platforms | AWS, Google Cloud, Azure | Azure | Google Cloud |
Three tiers in every family
Each company sells a flagship, a balanced model and a fast one. The fast tier is many times cheaper and is enough for most routine tasks.
| Tier | Claude | GPT | Gemini | When to take |
|---|---|---|---|---|
| Flagship | Opus | the main model | Pro | complex analysis, agents, code |
| Balanced | Sonnet | mini | Flash | assistants, documents, most products |
| Fast | Haiku | nano | Flash-Lite | sorting, extraction, large volumes |
Which model for which task
Twelve typical tasks with a starting point. The final choice is made by a test on your own examples.
| Task | Start with | Why |
|---|---|---|
| Assistant on a knowledge base | Claude, balanced | answers strictly from the sources |
| Analysis of contracts | Claude, flagship | long documents and careful reading |
| Sorting incoming requests | any, fast tier | a simple task, large volume |
| Extracting data from invoices | any, fast or balanced | structured output is enough |
| Product descriptions | Claude or GPT, balanced | tone and accuracy to the specifications |
| Voice assistant | GPT | mature real-time voice |
| Images for content | GPT or Gemini | they generate images |
| Analysis of video | Gemini | video as input |
| A whole archive in one request | Gemini | the longest context window |
| Programming assistant | Claude | one of the strongest in code |
| Agent working with tools | Claude or GPT, flagship | reliable multi-step work |
| Data that cannot leave | a local model | nothing leaves the server |
How to choose a model: 6 rules
Rankings measure general abilities. Your product needs one thing — correct answers on your data at a sensible price.
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01
A set of your own examples
30–50 real requests with good answers — every candidate model is run on them.
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02
From cheap to expensive
Start with the fast tier and go up only if it fails the test.
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03
Different tasks, different models
Sorting on a fast model, complex answers on a strong one — within one product.
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04
Code without lock-in
The model is a setting: switching to a new version or another company takes a day, not a rewrite.
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05
The price of one answer
Counted on real requests, not on the price list per million tokens.
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06
Terms for data
Read how the provider stores requests and where the servers are, before the first real data goes in.
Common mistakes when choosing a model
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Choosing by rankings
A leader in olympiad maths may lose on your price lists and letters.
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The flagship for everything
The most expensive model sorting requests multiplies the bill without improving anything.
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A test on three questions
Three successful answers say nothing about the hundredth.
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Code tied to one company
When a better or cheaper model appears, the switch becomes a project.
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A consumer subscription for a business
Employees’ personal chat accounts have other terms for data than business access.
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Choosing once and for all
New versions come out several times a year; the test set is rerun on each.
Questions about Claude, GPT and Gemini
Which AI model is the best?
There is no single best one: each leads in its own area, and for most tasks all three are good enough.
Is Claude better than ChatGPT?
In work with documents, instructions and code it is often more precise; GPT has more ready features around the model.
Which is the cheapest?
The fast tiers of all three cost about the same order; what matters is the price of one answer on your requests.
Are my data used for training?
Through the business API of Claude and GPT — no; Gemini — not on the paid tier. Free consumer services have other terms.
Can one product use several models?
Yes, and it is often the cheapest way: each task goes to the model that does it best for the money.
What about open models?
They run on your own server — for data that cannot leave and for large volumes of simple work.
How often should the choice be reviewed?
With every major release: the test set takes an hour, and the saving can be significant.
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