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To compare language models for an application, run them on the same representative tasks, grade their outputs against criteria that match the workflow, and repeat trials when responses can vary. A useful harness records not just scores but also prompts, settings, traces, errors, latency, and token use. The result tells you which system performed better under the tested setup—not which model is universally best.
What an LLM evaluation harness measures
An evaluation combines a task, its input, grading logic, and a measured outcome. A harness runs the tasks, records model interactions, grades the outputs, and aggregates the results. For an agent that uses tools or takes multiple steps, the record may need the full interaction transcript and the final state of the environment, not only the last answer. Anthropic’s evaluation guidance distinguishes these parts and describes why both transcript and outcome matter.
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Start by defining a decision, such as whether a model can reliably classify support tickets, extract fields from invoices, or answer questions from a knowledge base. Public benchmarks can provide context, but they do not replace testing on representative inputs and conditions from your own workflow.
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Set success criteria before running models
List the outcomes that matter and decide which are non-negotiable. A workflow might require a valid JSON object and correct extraction of specified fields; a writing assistant might be judged on factual accuracy, instruction following, and usefulness. Add safety, latency, reliability, or cost only when they affect the decision you need to make.
#1 Best Overall
- Hard gates: conditions a candidate must meet, such as passing schema validation or avoiding a high-severity error.
- Quality dimensions: rubric-scored traits that may involve trade-offs.
- Operational measures: latency, failures, token consumption, or cost under the tested settings.
- Failure cases: boundary conditions and rare but costly mistakes that an average score could conceal.
Involve people who understand the workflow when writing criteria and examples. OpenAI recommends contextual evaluations based on real workflow conditions, representative examples, and edge cases in its evaluation guidance for businesses.
Build and version the task set
Give every case a stable ID and store its input, any reference answer or expected outcome, and the category it represents. Include routine cases, boundary conditions, and known failure modes. Keep the task data and rubric versioned so you can identify what changed between runs. Where feasible, keep cases used to tune prompts or rubrics separate from a held-out set for final comparison; otherwise, repeated tuning against the same cases can make the final score less informative. Protect sensitive input data and record its provenance.
Make the comparison controlled and reproducible
For a controlled comparison, hold the task data, prompt, tools, model settings, and token or time budget constant wherever the providers allow it. OpenAI notes that scores can change with harness and budget choices in its guidance on third-party evaluations. If one model needs a distinct tool interface or another model-specific feature, record that difference and limit your conclusion to the systems as tested.
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Place a small adapter around each provider’s API so the rest of the harness sends a consistent request and receives a normalized response. The adapter should not silently change prompts or settings. Log enough information to rerun or interpret each result:
- Provider and exact model identifier; prompt, task-set, and rubric versions.
- Parameters and available tool definitions, plus the request timestamp.
- Request or response IDs when the API supplies them; errors and retries.
- Output and, for multi-turn or tool-using systems, the full trace and resulting environment state.
- Latency, token use, and cost when available. Record the pricing basis and calculation method used for cost rather than treating it as a property of the response alone.
A standardized harness makes differences easier to attribute, but it can also understate a model’s capability if it excludes a feature needed for the task. Report results as conditional on the named harness, tools, and budget—not as a context-free ranking.
A minimal harness shape
The following Python-style scaffold shows the responsibilities without assuming a particular provider SDK. Implement generate in each adapter using the provider’s current API, and make it return a normalized response with output, trace, usage, latency, and any error. The grader should return structured measures rather than one opaque score.
from dataclasses import dataclass
from typing import Any, Protocol
@dataclass
class Case:
id: str
input: Any
expected: Any
category: str
@dataclass
class Result:
case_id: str
model: str
trial: int
output: Any
trace: Any
usage: Any
latency_ms: float | None
error: str | None
scores: dict[str, Any]
class Adapter(Protocol):
name: str
def generate(self, case: Case, prompt: str, settings: dict) -> dict: ...
def run_case(adapter, case, prompt, settings, trial, grader):
response = adapter.generate(case, prompt, settings)
scores = grader(case, response.get("output")) if not response.get("error") else {}
return Result(
case_id=case.id, model=adapter.name, trial=trial,
output=response.get("output"), trace=response.get("trace"),
usage=response.get("usage"), latency_ms=response.get("latency_ms"),
error=response.get("error"), scores=scores,
)
for case in cases:
for trial in range(trials):
for adapter in adapters:
result = run_case(adapter, case, prompt, settings, trial, grader)
append_jsonl(result) # one durable record per model/case/trialThis is a starting structure, not a complete provider client or statistical analysis. Save the input and configuration versions alongside each record, and decide explicitly how errors, retries, and missing measurements enter summaries. Do not silently count a failed request as an ordinary low-quality answer.
Choose graders that fit the output
Use deterministic checks where possible
For objectively verifiable outcomes, use exact match, schema validation, required-field checks, executable tests, or task-specific invariants. These checks are repeatable and make failures easier to inspect. If several fields matter, retain the field-level results instead of collapsing them immediately into a single pass/fail number.
Use rubrics and pairwise review for open-ended quality
Write a rubric with observable criteria and examples of different score levels. For pairwise grading, ask a reviewer to choose between two anonymized outputs on specified criteria, and allow a tie. Hide model identities and vary which response appears first to reduce identity and position effects. Track ties rather than forcing a winner. OpenAI’s evaluation best practices discuss judge risks such as position and verbosity bias and recommend structured grading formats.
A model judge can speed up grading, but its judgments are not automatically valid for your task. Have people rate a representative sample, then compare judge scores or choices with those human ratings. Review disagreements, and keep human review for ambiguous or consequential cases. Google’s judge evaluation documentation describes checking model-based pointwise scores and pairwise choices against human ratings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Repeat trials and analyze the differences
Model outputs can vary, so one run is weak evidence of a stable difference when the task or settings are stochastic. Anthropic states, “Each attempt at a task is a trial,” and explains that multiple trials help produce more consistent results in its agent evaluation guidance. Choose the number of trials based on the decision, outcome type, and uncertainty you can tolerate; there is no universal sample size established here.
When comparing models on the same cases, preserve the pairing in your analysis: compare results for the same case and trial where the design supports it. Report the number of cases and trials, the scoring rules, the aggregation method, and uncertainty summaries appropriate to the outcome. Select a statistical method that fits whether the outcomes are paired or unpaired and what is being measured; do not claim statistical significance just because one aggregate score is higher.
Best Value
Report the dimensions that can change the decision
Show the overall result alongside category-level results and the underlying operational measures. Useful dimensions, when relevant to the workflow, include:
- Task success or correctness and the types and severity of errors.
- Rubric dimensions, safety or policy adherence, and format validity.
- Latency and reliability under the same load conditions.
- Token consumption and cost under the tested settings.
- Results by meaningful category, language, or difficulty, plus variation across trials.
A blended score can be useful for a pre-defined decision rule, but it should not hide a regression on an important category. Keep hard-gate failures visible, and say which dimensions were measured rather than implying the harness covered everything.
Keep offline evaluation separate from a live A/B test
An offline harness compares systems on a selected task set and setup. It helps you choose and improve a system, but it cannot establish how a change affects actual users in a live product. OpenAI says, “For external-facing deployments, evals do not replace more traditional A/B tests and product experimentation,” in its business evaluation primer. For a user-facing launch, use an appropriate product experiment and monitor real outcomes. Update the offline tasks as models, data, and product goals change.
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A custom harness is often enough when you need a small, controlled workflow and want direct control over task records, adapters, and graders. Managed services can offer model-based metrics or interfaces for reviewing paired outputs, but their SDK status, supported models, regional availability, and pricing can change. Google documents a managed evaluation service and a Python-based, human-in-the-loop comparison interface in its judge evaluation documentation and LLM Comparator article. Treat these as implementation options, not prerequisites; verify current service details before building around one.
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