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Which LLM is best for business? How to choose one
No model is best for every business. Here is how to shortlist GPT, Gemini, Claude or an open-weight model on data, cost, context, tools and languages, using each vendor's own documentation.
There is no single best LLM for business. The right model depends on where your data may go, how you want to pay, how much text each task needs, which tools it must call and which languages your customers write in. Shortlist two or three models on those points, test them on 50 to 100 of your own examples, and pick the cheapest one that passes.
Everything below was checked against each vendor's own documentation on 10 November 2026.
Where will your data go?
Start here, because it rules options out before you look at quality.
| Option | Business data used for training by default? | Location options that matter in India |
|---|---|---|
| OpenAI API | No | India region stores data at rest, but inference runs elsewhere. In-region processing is offered in the US, Europe and the UAE, with sales approval |
| Gemini API (Google AI Studio) | Free tier: yes, and people may review it. Paid tier: no | Through Vertex AI, data at rest stays in the region you choose; Mumbai offers in-region processing for some listed models only |
| Claude API | No, for commercial products | Inference "US only" or "global"; stored data in the US |
| Open-weight model on your server | Your choice | Wherever your server is |
Three details catch people out. OpenAI keeps abuse monitoring logs for up to 30 days by default, and zero data retention needs its approval. Google's Gemini API terms tell you not to send sensitive, confidential or personal information to the unpaid service. And a "global" endpoint, on any vendor, means the vendor picks where your request runs.
If a client contract says data must stay in India, your realistic choices are a regional cloud deployment of a model that is processed in-region, or an open-weight model on hardware you control. Our on-premise LLM versus cloud API comparison covers that trade-off.
How will you pay for it?
Hosted APIs bill per token: what you send, what comes back, and on reasoning models the "thinking" too, which Anthropic bills as output tokens. Location controls can cost extra: Anthropic charges 1.1 times the standard rate for US-only inference on its newer models. Prices change often, so take them from each vendor's pricing page on the day you decide.
A self-hosted open-weight model swaps the token bill for a fixed server cost. It wins when usage is steady and high, and loses when ten people ask a few questions a week. We worked through the numbers in pay per token or run your own GPU server.
How much text does it need to read, and which tools must it call?
Context. Anthropic lists a 1M token context window for its current models, and Google's top listed model also shows 1M. A small open-weight model such as Qwen3-8B handles 32,768 tokens natively and 131,072 with a scaling method called YaRN, per its model card. Bigger is not automatically better: Anthropic's own documentation warns that accuracy and recall degrade as the token count grows. For most company documents, retrieving the right passages beats pasting in everything.
Tools. OpenAI, Google and Anthropic all support function calling, where the model returns a structured request that your code runs, such as "look up order 4512". OpenAI and Anthropic both offer a strict mode that makes the call match your schema. If your assistant must create records or send messages, test tool calling on your real schemas, not a demo.
Will it work in Tamil, Hindi or Arabic?
Language lists on model cards are a starting point, not proof. Qwen3's card claims more than 100 languages and dialects, for example, but that tells you nothing about your customers' WhatsApp messages written in Tamil script, in English letters, or in both in one sentence.
Build a test set from real messages in each language your customers use, including mixed ones, and score every shortlisted model on it. A model that is strong in English can still be weak in Tamil, so test each language you need.
Open weights, licences and lock-in
An open-weight model gives you the weights to run anywhere, but read the licence before anything else. Qwen3-8B is Apache 2.0. Meta's Llama 4 licence requires a separate licence from Meta above 700 million monthly active users and a "Built with Llama" notice when you distribute it. Neither is a problem for most firms; both are worth knowing.
Lock-in with hosted APIs is mostly about your own code. Three habits keep you free to switch:
- Keep your own test set of real inputs and good answers, so you can score a new model in a day.
- Put a thin layer between your code and the vendor's SDK. Some vendors ease this: Anthropic, for example, offers an endpoint that accepts the OpenAI SDK.
- Keep prompts and tool definitions in version control, written plainly rather than tuned to one model's quirks.
How to make the decision
- Rule out options on data location and contract terms.
- Estimate monthly volume and decide between per-token and a fixed server.
- Shortlist two or three models that fit, including one open-weight model if privacy matters.
- Score them on your own examples for accuracy, language handling, tool calls, speed and cost.
- Pick the cheapest that passes, and retest every six months.
When we build custom AI systems, we usually try a hosted model and an open-weight one and score both on your examples for quality, speed and running cost. If privacy decides it, our private AI deployment team runs the model on hardware you control, and we will tell you in the first call if a hosted API is the better fit. Book a consultation.
