How to choose an AI model for research

Image: "A view of the server room at The National Archives" by The National Archives (UK) (CC BY 3.0) via Wikimedia Commons, recoloured.

Updated 6 November 2026. Choose a model by testing the work you need it to do. A convincing answer in a chat window is only the starting point: research also needs traceable evidence, appropriate data handling and a record of what happened.

Start with a small, realistic task

Write down the input, expected output and an error that would make the result unusable. For a literature review, that might be extracting the sample, method and limitations from a paper with page references. For text classification, it might be assigning a code while retaining the passage that supports it.

Build a pilot set that includes routine cases, ambiguous cases and cases where the correct answer is “insufficient evidence.” Keep some examples separate while developing your prompt. Use the same material and instructions when comparing tools.

Check the account and deployment

Record the provider, product, account type, enabled features and agreement that apply to your project. Check training use, retention, storage location, deletion, access controls and subprocessors separately. A paid subscription or a training opt-out alone does not establish that a workflow meets your institution’s requirements.

For example, Anthropic distinguishes its commercial products from consumer accounts. Its API retention documentation also describes feature-specific storage and exceptions. Read the policy for the exact service you will use: commercial retention, API retention.

Use approved sample data for the pilot. Where data must remain within your institution, ask its IT or research support team which deployment is approved. Local inference still requires checking connected services, logs and backups. Hardware requirements depend on the particular model, quantization and workload.

Score the outputs against your research needs

• Evidence accuracy: can each extracted fact be located in the source?

• Coverage: which important fields or difficult cases were missed?

• Uncertainty: does the tool recognize missing information?

• Consistency: do repeated runs change consequential answers?

• Effort: how much human correction does each output require?

• Resources: what are the actual cost and elapsed time for your pilot?

Treat context-window size as an input limit. Test whether the tool finds evidence throughout your documents. When processing a collection, preserve document identifiers and inspect omissions before combining results.

Keep a reproducible record

Save the model identifier shown by the service, date, prompts, source versions, settings, outputs and review decisions. Pin versions where supported, and record changes when a version becomes unavailable. A low temperature can still produce different outputs; rerun important checks.

Choose the least costly workflow that meets your quality and governance requirements. Document the decision and revisit the pilot after a meaningful tool change.

Next: build a verified evidence matrix, check an AI-generated analysis, or browse the research toolkit.