Which AI is best for business?

Which AI is best for business? For the office work we tested, all four models we tried did the job, so the better question is which plan and which software your team uses it in. We gave OpenAI’s GPT-5.6 Terra, Anthropic’s Claude Sonnet 5.5, Google’s Gemini 3.8 Flash and DeepSeek V4.1 Flash the same 80 office jobs on October 26, 2026, and they differed from the answer key on 7 of 320 answers. Most “best AI for small business” lists rank software, and none of the eight we read prints scores for any model. Below are the test, every miss, which plan staff should use, and a 30-job test to run on your own work.

Which AI should a small business use?

The one built into the software your team already works in, on a business plan.

The plan matters more than the brand. A personal plan of the same assistant can follow different terms: OpenAI says content on Free, Plus and Pro may be used to train its models unless you switch it off. Is my business data safe with AI? covers each maker’s terms in full.

One more line that page one leaves out. OpenAI says a ChatGPT Business subscription does not include API usage, which is billed separately. A system built on the same models later, one that reads your orders or answers your emails, is a second bill, not part of the staff plan.

What did we test?

80 office jobs, the same for four models, with the right answers written down first.

Airu Labs gave the four models the same 80 jobs for an invented plumbing-supply distributor: 30 orders as contractors text and email them, 40 customer emails to sort, and 10 quotes to price from a 40-product catalog and a page of rules. Each model saw the same instructions, with no tuning for any one of them, and answered each job once. The answer key was set before the run: written by Claude for the orders and emails, with the reason beside each call, and computed by code for the quote totals.

A few of the jobs, as the models saw them:

How did the four models do?

Close. The gap between the models was smaller than the gap between a clear job and an unclear one.

Makers are in alphabetical order; this is not a ranking.

Two things stand out. No model asked a question on an order a clerk could simply enter: 0 of 100. And no model let a risky email through, the fake bank change included. The cost row matters more for a built system than for staff, and it is covered below.

Where did the models differ?

On 7 of the 320 answers. Here is every one:

1. DeepSeek returned a blank reply on one clear order.
2. DeepSeek returned a blank reply on a second clear order.
3. GPT guessed brass on the 12 ball valves for a customer who buys brass and PVC, instead of asking.
4. DeepSeek entered two boxes of 25 for “30 of the 3/4 couplings”, instead of asking whether one box or two.
5. GPT sent the angry holiday-hours email to the draft pile, for a person to check, when the service sheet answered it.
6. Gemini did the same with that email.
7. GPT left the volume discount off the 14-stick line of the copper quote, so the total came out too high. Recomputing each line in code from GPT’s items and quantities and the price rules, which our scoring did after the run, would have caught it.

Three of the seven would have reached a customer unnoticed: the two guesses and the quote total. The blank replies and the cautious emails are the kind a system catches or a person shrugs at. What catches each kind is on what happens when AI makes a mistake. The email results in detail are on can AI answer your business emails, and the quotes on can AI write your quotes and proposals.

What does the test not show?

Enough to know before you rely on it:

• The company and its messages are invented, and an AI model (Claude, one of the four) wrote them and the answer keys. Claude’s clean score should be read with that in mind; Gemini matched it on orders and quotes.
• We tested the models through their developer connections, not the ChatGPT, Claude or Gemini apps. Memory, connectors and web search in those apps were not tested.
• Each model answered each job once. Asked again, a model can answer differently.
• 80 jobs is a small test. The point is the kind of mistake, not a ranking to the decimal.
• It tests reading and deciding on typed messages, not a connection to a real inbox or ERP. Real inboxes are messier, with attachments, photos and long threads.
• Models change. These are the versions on October 26, 2026.

Which AI fits which job?

The assistant already inside the software that holds the job, in most cases.

Any of the big assistants can do the first two rows. Try yours on one task from last week before you pay for a second one. Where a ready-made assistant stops and a built system starts is covered in ChatGPT or custom AI.

Does the choice change the bill?

For a built system, more than it changes the result.

The same 80 jobs cost about 23 times as much on the most expensive model as on the cheapest, and the two had the same email and quote scores. Part of the gap is how each maker counts. The identical words of our 80 prompts counted about 1.5 times as many tokens on Claude as on GPT, and Anthropic says its newer tokenizer produces about 30% more tokens for the same text. Two models spent about 85% of what they wrote on thinking the reader never sees. What AI costs to run each month turns that into a monthly bill you can estimate.

Should you switch when a new model comes out?

Only after the new one passes your own test.

Our founder put it this way in a video on new model releases: “Why would you throw out a brand new car to get one with trim?” A newer model can be better at some tasks and no different on yours. Makers also retire old ones on a schedule. Anthropic gives at least 60 days’ notice before a public model retires. OpenAI gives at least 6 months for generally available models, at least 3 months for specialized variants, and as little as 2 weeks for preview models. So a system tied to one model name has a date on it, and the builder should say how a switch is tested.

Why did a builder run this test?

To choose models for the systems we build on evidence, not on lists.

Airu Labs builds custom AI systems for small and mid-size companies, wired into the software they already use. Every system we build runs on a model someone has to choose, so we tested four of them on the kind of work small companies hand us. We ran it through OpenRouter, a service that sells access to all four makers’ models, so every model got the same words.

How do you test one on your own work?

With 30 of your own past jobs and the right answer written next to each.

1. Pick 30 recent jobs of one kind: orders, customer emails or quotes. Include the odd ones.
2. Write the right outcome next to each before you ask any AI.
3. Give two assistants the same instructions and the same 30 jobs, word for word.
4. Count three things for each: right, asked first, and wrong in a way you might have missed.
5. Look hardest at the third count. A model that asks is easier to live with than one that guesses.
6. Write the date on the sheet, and run it again before you switch models.

The third count is the one our test was built around: of 320 answers, 3 were wrong in a way a busy person could miss, and none of them looked wrong at a glance.