"Which model should we choose" is the first question people ask and roughly the third or fourth in importance. It still needs answering, so here it is, broken down by the criteria that genuinely affect the outcome.
A note on the numbers. Model versions, context sizes and API prices change every few months — faster than articles get updated. Specific values below are worth re-checking on the provider's own site. What changes slowly is the character of the models and the differences between the ecosystems, and that is what this is about.
ChatGPT (OpenAI)
Strengths. The largest ecosystem: a marketplace of ready-made assistants, broad support in third-party tools, mature documentation. Multimodal — text, images, voice. Built-in code execution and file handling. More specialists on the market know how to work with it than with anything else.
Weaknesses. A tendency towards expansive answers where a short one is needed. Requires firmer configuration to keep it from extrapolating beyond the data provided.
Choose it when: you need a broad ecosystem and off-the-shelf integrations, the task is multimodal, or availability of specialists matters.
Claude (Anthropic)
Strengths. Strict adherence to instructions — critical when an agent must answer only from your knowledge base and honestly say "I don't know". A large context window: a substantial document fits into a single request. Strong handling of long texts and structured data.
Weaknesses. A smaller ecosystem of ready-made integrations than OpenAI's.
Choose it when: customer-facing scenarios where a wrong answer is expensive; work with documents and procedures; tasks where predictable behaviour matters.
We use Claude in most projects for exactly the first reason: an agent that invents answers costs more than one that occasionally says "let me check with a colleague".
Gemini (Google)
Strengths. Native integration with the Google ecosystem — Gmail, Docs, Sheets, Calendar. The largest context window of the three. Generally the lowest price per volume, plus a free tier for small loads.
Weaknesses. Behaviour is less consistent between versions, which becomes noticeable once your prompts are tuned.
Choose it when: the company lives in Google Workspace; you need to process very large volumes of text; the API budget is a limiting factor.
Summary by criteria
| Criterion | ChatGPT | Claude | Gemini |
|---|---|---|---|
| Instruction following | Good | Best of the three | Good |
| Context size | Large | Large | Largest |
| Ready-made integrations | Most | Fewer | Strong within Google |
| Price per volume | Mid | Mid | Usually lower |
| Multimodality | Most complete | Text and images | Complete |
| Russian and Uzbek | Good | Good | Good |
All three handle Russian confidently. Uzbek is acceptable across the board, but quality is noticeably below Russian on any of them. This needs testing against your own real queries rather than against claims in the documentation.
What matters more than the model
Here is the point of the article. The difference in outcome between the three platforms on a typical business task is smaller than the difference between a good and a bad knowledge base on the same model.
Roughly in order of influence on the result:
- Data quality. A complete, current knowledge base decides more than any choice of model.
- How the instructions are written. What the agent does, what it does not do, when it hands over to a human.
- Picking the right process. Automating the wrong task cannot be rescued by any model.
- The model itself.
The practical conclusion: if a project is not delivering, switching models is the last thing to try. Look at the first three items instead. In the overwhelming majority of cases the cause is there.
How your data is handled
The item usually left out of comparisons, and often the decisive one for a business.
All three providers offer modes in which data from API requests is not used to train models. But the terms differ between plans, and consumer versions do not carry the same terms as business plans and the API.
What to verify before starting: whether data is used for training on your plan; how long request logs are retained; in which jurisdiction the data is processed; and what is written in the contract rather than in a blog post.
The same applies to your contractor: ask exactly what goes to the model and what stays with you.
What about open models
The question comes up regularly, so briefly. Open models you can host on your own server are a genuine option, but not for a first project.
For: data never leaves your perimeter at all, there is no dependence on a provider, and at large volumes the cost can work out lower.
Against: you need a server with a GPU and someone to keep it running. On typical business tasks the quality is usually below the leading commercial models, and the gap is especially visible in Russian and more so in Uzbek. The economics only add up at genuinely large volumes — at tens of dollars a month in API costs there is no point discussing your own server.
When it is justified: a hard requirement that data must not leave your systems — working with medical or banking data, for instance. Then it is not a question of saving money but of admissibility, and the calculation works differently.
Frequently asked questions
Can we use several models at once?
Yes, and mature projects do: a cheap fast model on simple steps, a stronger one where the cost of an error is higher. It complicates the system, so it is not where to start — but moving there later is possible without a rewrite.
Is it hard to switch models afterwards?
If the system is built sensibly, the model is a replaceable part. The knowledge base, integrations and logic stay; the API layer changes and the prompts get re-tested. That is days, not weeks. It is worth confirming your contractor has not welded everything to one provider.
Are the free tiers worth using?
For experiments, yes. For a production agent, no: they carry load limits and different data terms. The difference in money is small; the difference in predictability is not.
What does running it cost?
For typical small and mid-sized business volumes — tens of dollars a month on any of the three. The model is rarely a significant line item; development is. Budget breakdown in how much AI automation costs.
What should we choose if we have to decide today?
Document and customer work where accuracy matters — Claude. A company entirely inside Google Workspace — Gemini. Need the widest possible integration ecosystem — ChatGPT. It is hard to get wrong: on a typical task all three will produce a working result.
Where to start
Not with choosing a model. Describe the process and assemble the knowledge base — that takes a few days, and the work is useful regardless of what you pick. Choosing the model takes half an hour once everything else is ready.
Want to break down the task? We run a free audit and will say plainly if there is nothing worth automating yet.