How to choose an AI model for your business
Why benchmarks should not decide for you, what the price gaps mean in real money, and how we pick a model for a client's product.
If you tried to follow AI news over the last month, you probably gave up. Google released Gemini 3 Pro on 18 November. Anthropic answered with Claude Opus 4.5 on the 24th. OpenAI shipped GPT-5.2 on 11 December, and yesterday Google made Gemini 3 Flash the default model in its Gemini app. In between came strong open models from DeepSeek and Mistral that anyone can download and run.
Every launch came with charts showing it ahead on something. People ask us which one they should use, and our answer is that the question is slightly wrong.
Why the leaderboard does not decide
Benchmarks measure what the model makers choose to measure: maths olympiad problems, coding tests, graduate-level science questions. Those tell you something about raw ability. They tell you very little about whether a model will answer your customers' questions about delivery times correctly, in the tone you want, in Arabic as well as English.
The top models are now close enough that the differences on your task can go either way. One model may be better at following your formatting rules; another may be better at saying "I don't know" when your documents do not cover a question. You only find out by testing.
The price gap is real
Where models differ sharply is cost. Prices are quoted per million tokens, roughly 750,000 words, with input (what you send) and output (what the model writes) priced separately.
Gemini 3 Flash costs $0.50 per million input tokens and $3 per million output. Claude Opus 4.5 costs $5 and $25. That is ten times the price on input and more than eight times on output.
In practice: a typical support conversation might send about 3,000 tokens to the model (your instructions, the relevant help articles and the chat so far) and get 500 back. On Gemini 3 Flash that is about $0.003 per conversation. On Opus 4.5 it is about $0.03. For 10,000 conversations a month, that is around $30 against around $275.
Neither number will break a business. But it shows why using the most powerful model for everything is a habit worth breaking, especially once AI is doing many small jobs a day.
How we choose for a client
We think about it in three tiers, matched to the job rather than the brand.
- Small, fast, cheap models for high-volume background work: sorting enquiries, pulling fields out of documents, tagging, routing. Gemini 3 Flash and Claude Haiku 4.5 are good examples.
- Mid-sized models for most customer-facing answers, where quality and tone matter and conversations are short.
- The top models for the hard parts: multi-step agents, complex reasoning over long documents, and writing software. This is where Opus 4.5, Gemini 3 Pro and GPT-5.2 earn their price.
Then we test. We take fifty to a hundred real examples from the client's business, real customer messages and real documents, run them through two or three candidate models, and have a person grade the answers without knowing which model wrote which. An afternoon of this beats any leaderboard.
Build so you can switch
Given how fast this is moving, being able to change your mind cheaply matters more than your first pick.
That means keeping your instructions and test examples in your own repository, not buried in one provider's dashboard. It means a thin layer in your code that talks to models, so swapping one for another is a configuration change. And it means rerunning your test set whenever you consider a switch, so you know whether the new model is actually better for you.
Teams that build this way can move to a newer or cheaper model in days rather than months.
A note on data
Two questions come up again and again with businesses in the Gulf and India: where does our data go, and is it used for training?
The paid API services from OpenAI, Anthropic and Google do not train on your data by default. Free tiers and consumer chat apps can work differently, so a team pasting customer records into a free chatbot is a bigger risk than your product's API calls. Read the terms for the exact plan you are on.
If data cannot leave your own servers, open models like DeepSeek V3.2 and Mistral Large 3 can run on hardware you control. That brings its own costs and engineering, and it is usually worth it only for specific, sensitive workloads rather than everything.
