# How to choose an LLM for customer support

- URL: https://www.chaterimo.com/en/blog/how-to-choose-llm-for-customer-support/
- Language: en
- Type: Article
- Author: David Langr
- Published: 2024-05-03
- Updated: 2026-10-19

> Accuracy on your data, speed, cost and tone. How to compare ChatGPT, Claude, Gemini and Grok for an online store and test them on your own questions.

## Key findings

- Choose a language model for customer support by **four criteria**: accuracy on your own data, response time, cost per conversation and tone.
- In our blind benchmark, fast and affordable models often score **close to or above the flagship models** of the same provider.
- Every model is weakest on **accuracy**, so test candidates on real questions from your store and check the facts in their answers.
- Model versions change every few months; **re-test** when a provider releases a new one instead of fixing a model name for years.

Choose a language model (LLM) for customer support by how accurately it answers from your store's data, how fast it answers, what a conversation costs and whether its tone fits your brand. No model is permanently the best: providers release new versions every few months. The reliable way is to test two or three current models on real questions from your store.

## What matters when you choose a model?

| Criterion | What to check | How to measure it |
|---|---|---|
| Accuracy | Are products, prices, stock and policies in the answer correct? | Check the facts in answers to 20–30 real questions |
| Response time | How long does a customer wait for a complete answer? | Average time per answer in your test |
| Cost | What does a typical conversation cost? | Tokens per conversation × the provider's price |
| Tone | Does the answer sound like your store? | Read the answers as a customer would |
| Languages | Does it answer well in your customers' languages? | Test questions in each language you serve |

Accuracy comes first. A friendly, fluent answer with a wrong delivery date costs more than a slower correct one.

## ChatGPT or Claude: which is better for customer support?

Neither is better for every store. In round 4 of the [Chaterimo CX-LLM Benchmark](https://www.chaterimo.com/en/blog/best-ai-for-customer-support/), OpenAI's newest models took the first three places, and the best Claude model was fifth. The difference came mostly from accuracy; Claude and ChatGPT models scored similarly high on tone.

Gemini and Grok belong in the comparison too. In the same round, a fast Grok model was fourth and answered in about two seconds, while the fastest model of the round, a light Gemini model, answered in about one second but scored lowest.

Benchmark results apply to specific versions on a specific date. Use them to choose which models to test, not as the final answer.

## Do you need the biggest model?

Usually not. In our benchmark, fast and affordable models of a provider often scored close to or above its flagship models, answered faster and cost a fraction per conversation. Stores use them a lot: in May 2026, “mini” and “Flash” models produced 46.3% of AI replies on Chaterimo, although the single most used model was a flagship (see [State of AI customer support 2026](https://www.chaterimo.com/en/blog/state-of-ai-customer-support/)).

Start with a fast, affordable model and move to a larger one only for the questions where the smaller one fails.

## Why does accuracy depend on your data?

A general model does not know your catalogue, stock, delivery times or return policy. It answers correctly only when it gets this information with the question, from your product catalogue and knowledge base. In the benchmark, every model scored lowest on accuracy, even with the same knowledge-base context.

So the quality of your data matters as much as the model:

- keep products, variants and their parameters complete;
- describe delivery, payment and returns in the knowledge base;
- read a sample of real conversations and fill in what the chatbot did not know.

## How do you test models on your own store?

1. Pick 20–30 real customer questions: products, compatibility, delivery, returns, order status.
2. Choose two or three current models from different providers, including a fast, affordable one.
3. Ask every model the same questions with the same knowledge base.
4. Check the facts in each answer and note the response time.
5. Choose the model with the most correct answers within your response-time target and budget.

In Chaterimo you switch the model in the chatbot settings and try it on the test page, without changing your knowledge base. With your own API key (BYOK) you pay the provider directly; plans are on the [pricing page](https://www.chaterimo.com/en/pricing/).

## When should you switch models?

Re-test when a provider releases a new version or retires the one you use, and when your customers' questions change, for example before a peak season. A short test with the same question set takes an hour and shows whether a newer model is worth switching to.

## Summary

Choose an LLM for customer support by accuracy on your data, response time, cost and tone, in that order. Fast, affordable models are often good enough, and the quality of your catalogue and knowledge base decides as much as the model. Test two or three current models on real questions from your store and repeat the test when providers release new versions.

**Which is the best AI model for customer support?**

It changes with every new model version. In round 4 of the Chaterimo CX-LLM Benchmark, OpenAI's newest models scored highest; test current models on your own questions before you choose.

**Is a cheaper model good enough for customer support?**

Often yes. In our benchmark, fast and affordable models scored close to the flagship models of the same provider and answered faster.

**Can I change the model later?**

Yes. In Chaterimo you switch the model in the chatbot settings; the knowledge base and integrations stay the same.