How to Choose an LLM for Your Business: A Practical Framework

New language models are released every few weeks, and each launch arrives with charts showing it at the top of some benchmark. For a business, that noise hides the real question: which model will do our task well enough, at a cost and speed we can live with, under data terms we can accept? The answer is found by testing, and the testing can be simple.

Step 1: Define the task precisely

'We want to use AI for support' is not a task. 'Classify incoming emails into 12 categories and extract the order number' is. Write down, for each AI step in your product, the input, the expected output, how you would judge a good answer, the volume per day and the maximum acceptable response time. Different steps in the same product often need different models.

Step 2: Build a small evaluation set

Collect 50 to 200 real examples with known good answers, including the awkward cases: ambiguous requests, missing information, other languages and attempts to make the system misbehave. Decide how each answer is scored: exact match for extraction, a rubric for writing, a human or model-assisted judgement for open questions. This set becomes the yardstick for every model, prompt and version change from now on.

Step 3: Shortlist with public signals

Public evaluations help narrow the field. Community preference arenas such as LMArena compare models in blind head-to-head chats; task benchmarks such as SWE-bench measure fixing real software issues; and holistic efforts such as Stanford's HELM report many metrics side by side. Treat these as a filter: pick three to five candidates across price tiers, including at least one small model and, if relevant, one open-weight model.

Step 4: Score on six criteria

Weight the criteria for each task. For a customer-facing chat, latency and tone matter; for nightly document processing, cost and accuracy dominate; for anything touching personal data, the data terms can rule a model out entirely.

Step 5: Compare cost per task, not per token

Per-token prices are misleading on their own. A cheaper model that needs longer prompts, more retries or human correction can cost more per completed task. A pricier model that solves a task in one pass can be cheaper overall. Measure the real token usage of each candidate on your evaluation set and multiply by its published prices.

Step 6: Route instead of choosing one

Most mature AI products use more than one model. A routing layer sends routine steps (classification, extraction, short drafts) to a small, fast model and escalates difficult cases to a frontier model, sometimes with a private model for sensitive data. Keeping all models behind one internal interface also protects you from price changes and model retirements. Agent systems benefit most from this pattern; see AI agents vs workflows vs chatbots.

Step 7: Check the risks

• Data handling. Read the provider's business terms on training use and retention, and sign a data processing agreement where personal data is involved.
• Regulation. Some uses, such as hiring, credit and health, carry specific legal obligations. Our overview of the EU AI Act timeline is a starting point.
• Security. Prompt injection and over-permissioned tools are bigger risks than the model itself; see AI agent security and the OWASP agentic top 10.
• Vendor change. Models are updated and retired. Pin versions where possible and re-run your evaluation before upgrading.

The NIST AI Risk Management Framework offers a useful, vendor-neutral structure for documenting these risks.

A one-page selection worksheet

1. 01List each AI step: input, output, volume, latency target, data sensitivity.
2. 02Build or update the evaluation set for that step.
3. 03Shortlist three to five models across price tiers.
4. 04Run the evaluation; record quality, tokens, latency and failures.
5. 05Calculate cost per task at current and projected volume.
6. 06Check data terms and regulatory fit; drop any model that fails.
7. 07Choose a default and a fallback model; put both behind one interface.
8. 08Schedule a re-evaluation every quarter or whenever a major model is released.

If you would like an independent recommendation, our technology consulting team runs model bake-offs on your own data, and our AI agent development practice builds provider-neutral systems that can switch models as the market moves. For the business case around AI more broadly, start with our AI automation guide.

Sources and further reading

• LMArena, community model comparisons
• SWE-bench, real-world software engineering benchmark
• Stanford CRFM, Holistic Evaluation of Language Models (HELM)
• NIST, AI Risk Management Framework
• OpenAI, Model optimization guide