There is no "best" LLM in the abstract. There is the model that solves your task at the quality you need, at a cost and latency your use case can bear, with data handling your security and compliance teams can accept. To choose between Claude, GPT, Gemini or an open-weight model, define your criteria, compare deployment options and, above all, run a small evaluation on your own cases before committing. This guide gives you the method.
The options available today
A large language model (LLM) is the engine that understands and generates text inside a chatbot, an agent or a search system. For a business, the options fall into two groups:
- Proprietary models via API: Claude (Anthropic), GPT (OpenAI) and Gemini (Google). You consume them as a service, pay per token and the provider runs the infrastructure. Each family offers large models (more capable, more expensive) and small ones (fast and cheap).
- Open-weight models: for example Llama (Meta) and several Mistral models. You can download and run them on your own infrastructure or use them through a cloud provider.
Versions change every few months, so decide on criteria rather than on whichever model is trending. Today's best model for your task may not be in six months, and your architecture should let you switch.
The criteria that actually matter
- Quality on your task: public leaderboards measure generic tasks. What matters is whether the model extracts your invoice fields correctly or answers accurately about your policies.
- Cost per token: providers charge per million input and output tokens, and a large and small model from the same family can differ several-fold. See the breakdown in our guide to the cost of implementing AI.
- Latency: customer chat needs answers in seconds; an overnight document job can wait hours and use discounted batch processing.
- Context window: how much text the model accepts in one call. A large window does not replace good retrieval, and sending more text costs more.
- Data privacy and retention: whether the provider trains on your data, how long it keeps it and where it is processed.
- Deployment options: direct API, through your existing cloud, or self-hosted.
- Licensing: for open-weight models, commercial terms vary.
Privacy and retention: what the providers say
This is the question security reviewers ask first, and the answers are in each provider's official documentation:
- Anthropic: states that by default it does not use inputs or outputs from its commercial products (such as the Claude API) to train models, unless the customer opts in or submits feedback.
- OpenAI: states that data sent to its API is not used for training unless you opt in. It retains abuse-monitoring logs for up to 30 days by default and offers Zero Data Retention to eligible customers, subject to approval.
- Amazon Bedrock: AWS states that Bedrock does not store or use your data to train models.
If processing must stay in a given geography, several providers offer regional or data residency endpoints, usually at a premium over standard pricing. If you handle protected health information, confirm that the specific service is covered by a business associate agreement before sending PHI. For highly sensitive data, a self-hosted open-weight model keeps everything inside your environment, at a higher operating cost.
Deployment options: direct API, cloud marketplace or self-hosted
Many companies can use these models through the cloud they already pay for:
- Amazon Bedrock (AWS): models from several providers, including Claude.
- Microsoft Foundry (Azure): OpenAI models and Claude.
- Google Cloud: its AI platform, formerly Vertex AI, offers Gemini and Claude.
Going through your cloud means the same billing, IAM permissions and private networking as the rest of your stack, which simplifies vendor and SOC 2 reviews and can draw down existing cloud commitments. The trade-off: new features sometimes reach the model provider's own API first.
| Criterion | Direct API (Claude, GPT, Gemini) | Cloud marketplace (Bedrock, Foundry, Google Cloud) | Self-hosted open-weight (Llama, Mistral) |
|---|---|---|---|
| Time to start | Immediate | Fast if you already use that cloud | Weeks: infrastructure and GPUs |
| Top available quality | Each provider's most advanced models | The same models, sometimes with a lag | Depends on the model; usually needs more tuning |
| Cost | Per token, no fixed cost | Per token, on your cloud bill | Fixed GPU and ops cost; pays off only at high, steady volume |
| Data control | Per provider terms | Inside your cloud account and region | Full: data never leaves your infrastructure |
| Operational burden | Minimal | Minimal | High: updates, scaling, monitoring |
| Lock-in risk | Medium | Medium (cloud and model) | Low on the model, high on your team |
Open-weight model licensing
"Open weights" does not always mean open source in the classic sense. Read the license before you build on one:
- Llama: released under Meta's community license, which allows commercial use but requires displaying "Built with Llama" when you distribute products that include it, and requires companies with more than 700 million monthly active users to request a separate license.
- Mistral: publishes several models under Apache 2.0, one of the most permissive licenses; others carry different terms. Check the specific model.
For most companies the user threshold is irrelevant, but attribution duties and acceptable use policies still apply, and your legal team will want them on file.
Routing: more than one model in the same product
You do not have to pick just one. Multi-model routing sends each request to the right model for its difficulty:
- A small model classifies intent, extracts fields or answers FAQs.
- A large model handles complex reasoning, sensitive drafting or decisions that need more context.
- A fallback model from another provider takes over if the primary fails or is rate-limited.
Done well, routing cuts cost without lowering perceived quality. It is standard practice in AI agents for business, where each step has different demands.
How to avoid vendor lock-in
Lock-in happens when your product depends on one model's quirks. To avoid it:
- An abstraction layer: your code calls your own interface ("generate answer", "extract fields"), never a vendor SDK directly.
- Versioned prompts outside the code: so you can adapt them per model without rewriting the app.
- Your data in standard formats: documents, embeddings and logs that can be regenerated with another provider.
- Decoupled tools: standards like MCP let different applications and models share the same integrations.
- An evaluation set: without it, switching models is a leap of faith; with it, it is a measurable decision.
That is how our AI development services design solutions: you can change models without rewriting the product.
Run a small evaluation before you decide
A model evaluation is a test where candidate models answer the same real cases and you score the results against defined criteria. You need method, not a lab:
- Collect 30 to 100 real, representative cases, including hard ones and ones that should be refused.
- Define the correct answer for each, or a scoring rubric.
- Test 2 or 3 models (for example, one large and one small from different families) with the same adapted prompt.
- Measure quality, cost per case and latency, not just quality.
- Pick the cheapest model that clears your quality bar, not the one with the top score.
The same case set later becomes your production quality monitor, as we explain in evaluating AI assistants in production. If you are weighing tuning a model against connecting it to your documents, read RAG vs fine-tuning.
Frequently asked questions
Is Claude or ChatGPT better for business?
It depends on the task. Claude (Anthropic) and GPT (OpenAI) are both top-tier model families with large and small versions, and each can perform better on different tasks. The reliable way to decide is to test both on 30 to 100 real cases from your business and compare quality, cost per case and latency. Data handling, your existing cloud and available integrations also weigh in.
Is an open-source LLM cheaper than using an API?
Not necessarily. The weights are free to download, but running them takes GPU servers, monitoring, updates and skilled staff, a fixed cost whether you use it or not. Self-hosting tends to pay off at high, steady volume or when data cannot leave your infrastructure. For low or variable volume, paying per token through an API is usually cheaper.
Do AI providers train their models on my data?
For their business API services, Anthropic and OpenAI state that by default they do not train on your data, and AWS says the same for Bedrock. Always check the current policy for the exact service you contract, how long logs are retained and where data is processed. Free consumer apps can have different rules, so keep company data out of them.
Can I switch models after launch?
Yes, if the solution was built with an abstraction layer, versioned prompts and an evaluation set. Then switching is a bounded task: adapt the prompts, run the tests and compare results. If your code calls one vendor's SDK directly throughout, switching can mean rewriting significant parts of the product.
Which LLM is best for Spanish-language customers?
Models from the major families handle Spanish well, but quality varies with the task, your industry vocabulary and the register you need. Include real Spanish-language cases in your evaluation, with regional expressions and common typos, and check that the model keeps your brand voice. A bilingual nearshore team can help build and score that test set.
Conclusion
Choosing an LLM is an engineering and business decision, not a brand choice: define criteria, compare deployment options, review privacy and licensing, and decide with an evaluation on your own cases. Design for switching, because the market will keep moving.
If you want to compare models on your real data, explore our AI development services or book a free consultation; we will build the evaluation with you and recommend the mix of quality, cost and privacy your use case needs.