A public benchmark is a useful discovery tool, but it does not tell you whether a model can perform your workflow. The decision needs evidence from representative inputs, the intended application, and the hardware you expect to operate.
This guide proposes an evaluation method. It does not rank models or present measured performance for a Falcon deployment.
Define success before comparing models
Choose a bounded task. For document extraction, identify each required field, acceptable normalization, and the cost of an incorrect value. For question answering, require source support and an appropriate response when information is missing.
Separate cosmetic preferences from consequential errors. A different writing style may be acceptable; an invented account number may not be. Avoid averaging a critical failure into a reassuring overall score.
Write a rubric that two reviewers can apply consistently. Include examples of a correct result, a correct refusal, an incomplete result, and a serious error.
Build a representative dataset
Use appropriately authorized real examples or synthetic examples that capture the same structure. Include short and long inputs, common and rare cases, ambiguous wording, missing fields, and inputs outside the intended scope.
Keep development examples separate from held-out evaluation examples. Otherwise, repeated prompt tuning can improve the measured score without improving performance on new work.
For a RAG workflow, preserve the source collection and retrieval configuration used in each run. You need to distinguish a model change from an index or document change.
Freeze the comparison conditions
Record the model identifier and exact revision, quantization, serving software version, hardware, prompt, sampling settings, context limit, retrieval settings, and output schema.
Use the same acceptance criteria for every candidate. If one candidate needs a different prompt format or server configuration, document the adaptation. Do not describe the comparison as like-for-like if the workload or output budget changed.
An illustrative result record can be simple:
{
"case_id": "extraction-023",
"model_revision": "record-exact-revision-here",
"configuration_id": "evaluation-config-04",
"schema_valid": true,
"required_fields_correct": 5,
"required_fields_total": 6,
"critical_error": false,
"review_seconds": 42
}The values are invented to show a record shape. Replace them with measured results and keep the original output for review.
Measure application quality
For extraction, score field accuracy and invalid or missing values. For an assistant, score correctness, source support, and handling of insufficient evidence. For a drafting workflow, measure reviewer corrections and the time needed to produce an acceptable final artifact.
A second model can assist with review, but it should not be the sole authority for the decision. Calibrate automated judgments against human reviewers and inspect disagreements. If the evaluation uses sensitive inputs, the evaluator belongs within the approved processing boundary.
Measure realistic performance
Record time to first token for interactive experiences, total response time for completed work, and throughput at the expected concurrency. Include queueing and failures. Report latency percentiles so a fast average does not hide slow requests.
Use representative prompt and output lengths. A short chat benchmark is a weak proxy for a long document workflow. Separate cold startup from warmed operation and state which condition you measured.
Test the application as a whole. Retrieval, authorization, tool execution, and user-interface behavior can dominate the experience even when model serving is fast.
Make the selection inspectable
Summarize each candidate against required quality, critical failures, review effort, capacity, total cost, and operating complexity. Identify the reason for the recommendation and the cases that remain unresolved.
The result may favor a smaller model, a better retriever, or conventional software for part of the task. Retain the evaluation set for regression checks when dependencies or models change. The cost guide adds the economic comparison without assuming that self-hosting is always cheaper.