Which LLM is best for your business? A comparison worksheet

There is no useful universal answer to “which LLM is best?” without a task, a version and an agreed test. A model that produces a convincing draft may still miss a document detail, cite the wrong source or take an action it should have left for review.

This guide combines the useful questions from our tool-comparison articles into a repeatable worksheet. Use the same inputs and acceptance criteria, and keep a record of the configuration and checks behind the decision.

Distinguish the model from the product

ChatGPT, Claude, Gemini, Perplexity and Grok are products or services a business may evaluate; the model, enabled tools, account plan and interface can affect what a user experiences. An API test and a consumer chat interface are not necessarily testing the same configuration.

Start from the current official pages for OpenAI, Claude, Gemini, Perplexity and Grok. Confirm features, availability, limits and terms for your actual account. Do not rely on a comparison table tied to obsolete model names.

Choose tasks that reflect your work

A useful small test set might include a factual summary, a draft in your house style, a table extraction, a source-backed research answer and a task requiring the system to say that information is missing. Use safe representative inputs and keep the expected result separate from the prompt.

Include awkward cases: conflicting documents, an old source, a misleading question or an instruction to make a claim the evidence does not support. The goal is to learn where review is needed, not to obtain a flattering demonstration.

A practical scorecard

• Accuracy: are facts, figures and extracted fields correct?
• Evidence: do citations support the claim and point to an accessible source?
• Instruction following: does the output respect scope, format and exclusions?
• Uncertainty: does it preserve missing or conflicting information?
• Review effort: what must a competent person correct before use?
• Latency and cost: how long does the whole task take and what does it cost?
• Controls: can the organisation apply its data, permission and retention requirements?

Writing: compare the edited result

Give each candidate the same brief, source facts and style example. Check whether it invents benefits, changes meaning or ignores required wording. Record the editing needed, not just the first impression.

The relevant question is whether your team can produce an accurate final piece efficiently. A preference for one tone is useful evidence for that team, not proof that a provider is universally better at writing.

Documents: test recall and source location

Use a document containing several similar names or figures, an exception and a detail near the end. Ask for the source passage alongside each answer. A large advertised context window does not prove that every relevant detail will be used correctly.

Include a question that the document cannot answer. Reject invented clauses, dates or conclusions. For confidential or regulated material, decide the allowed processing and review requirements before uploading it.

Research: check the sources yourself

Record whether browsing or a research tool was enabled and what it actually searched. Open the cited source and check the publication date, scope and quoted evidence. A citation marker does not establish support.

Keep search discovery separate from a verified fact. If a source is inaccessible, the answer should state that limitation rather than imply it was read. Current-information features and provider coverage change; check the exact setup you are testing.

Coding and actions need their own checks

Code generation must be assessed against the repository, tests and application behaviour. For a detailed method, use our Claude Code versus Codex comparison worksheet. Choosing an LLM for prose does not automatically choose the right coding tool or agent permissions.

Where a system can update records, send messages or use external tools, test denied actions, partial completion and duplicate events. A correct explanation of an action is not evidence that the destination accepted it.

Images, charts and other formats need separate evidence

If the workflow needs images, charts, slides or audio, check that the chosen product and plan support the required input and output. Test a representative file rather than assuming that a good text answer proves the whole capability.

For chart interpretation, include axis labels, units and a deliberately ambiguous figure. For generated media, review factual representation, brand requirements and rights. Keep creative preference separate from whether the result is accurate and permitted for the intended use.

Compare cost per usable outcome

Add model or subscription usage to preparation, review and rework. A cheaper run can be more expensive if it requires repeated correction. A more capable model may be unnecessary for a fixed, rules-based task.

Record the plan, model identifier, settings, date and test set. If a product changes its model or tools, repeat the checks that matter to your workflow. OpenAI’s evaluation documentation explains evaluation tooling; the general principle is to test expected behaviour rather than assume improvement.

A fictional decision record

Suppose two tools produce customer-policy summaries. One is faster, but repeatedly misses an exception about damaged goods. The other requires less correction on that test. Record the result narrowly: the second configuration performed better on this checked sample. Do not turn it into a claim that it is best for every business task.

Choose the simplest arrangement that meets the evidence and control requirements. Keeping several subscriptions only makes sense if each has a clear purpose and owner.

For help scoping the work, see AI consulting for small businesses or book a free consultation. Agree the scope, responsibilities and written quote before work starts.