Choosing a model for your workload

A public leaderboard can help build a shortlist. It cannot tell you how a model will handle an unfamiliar part number, a damaged scan or an ambiguous request from your own workflow. That decision needs a separate evaluation with a clear definition of an acceptable result.

Start with the decision

Write down what the model must produce and what happens when it is wrong. For document extraction, distinguish a missing field from an incorrect value and a valid JSON object from a correct answer. Set thresholds for critical fields before running the comparison.

Build a useful test set

Collect representative examples, including uncommon layouts, incomplete inputs and cases that should be referred for review. Keep test examples separate from training and prompt development. Split related documents together so near-duplicates do not appear on both sides of the evaluation.

Compare complete systems

Use the same task definition, input preparation and scoring rules for every candidate. Record model versions, prompts, decoding settings and any adaptation. If one candidate uses retrieval or a different parser, describe that difference: the comparison is then between pipelines, not just model weights.

Measure accepted outputs alongside latency, memory use and cost per completed task. Count retries and human corrections. Inspect disagreements manually, particularly where an automated scorer may miss a plausible but incorrect value.

Make the result reproducible

A useful report includes the test-set composition, scoring rubric, configuration and failure examples. Report results by document type or language as well as an overall score. Choose a system that meets the workload's requirements, then repeat the evaluation when the model or input distribution changes.

This engineering note describes an approach, not a new experimental result. Performance claims require a documented test setup and reproducible measurements.