A benchmark is evidence, not your product
HELM's multi-scenario approach highlights why a single score is an incomplete view of a language model. Public benchmarks can inform a shortlist, but their tasks, prompts, and operating conditions may differ from your application.
Start with a workload description. Does the feature extract fields, rewrite copy, interpret screenshots, use tools, or reason across documents? Include the languages, output contract, latency expectations, and consequences of failure. These requirements narrow the candidates more usefully than a vague search for the best model.
Technical foundation: Liang et al.: Holistic Evaluation of Language Models
Set hard constraints before scoring preferences
Some requirements are gates rather than tradeoffs. The model service must support the necessary input types, data-handling arrangement, and deployment environment. A candidate that cannot meet those requirements should not win because it writes more elegant prose.
Then define preferences: accepted-task rate, response time, resource use, integration simplicity, and recoverability. Assign priorities that reflect the actual product. A background analysis job and a mobile autocomplete feature can reasonably choose different models even inside the same application.
Compare candidates on equal terms
Run the same held-out tasks with comparable context and an explicit output contract. Record provider settings and model identifiers. Where candidates require different prompting, document the adaptation rather than pretending the experiment is identical.
Inspect failures, not just averages. One model may excel at ordinary cases but mishandle missing information. Another may abstain too often. Include repeated samples for tasks where variability matters, and have reviewers apply the same rubric. An attractive demonstration is not enough evidence for a production choice.
Account for the cost of changing later
Keep provider-specific details behind a narrow application boundary where practical. Store prompts, schemas, and evaluation fixtures in a portable form. Do not assume that switching an endpoint preserves tool behavior, tokenizer counts, output formatting, or safety characteristics.
Plan a migration test before relying on an automatic fallback. A second model should be evaluated on the same task contract, including authorization and no-answer behavior. The right choice is a configuration with known strengths and limits, not an unqualified claim that one model is universally superior.
A defensible selection record
- Lists workload requirements and non-negotiable constraints.
- Uses a representative held-out evaluation set.
- Compares quality, latency, and resource use together.
- Records exact configurations and important failure modes.
- Includes a tested migration or fallback strategy.
