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Decide what a successful comparison means
Start with the application’s task, not a general-purpose leaderboard. Write down what counts as a successful request and which tradeoffs matter. For example, you might require a minimum task-success rate, a maximum p95 response time, an error-rate ceiling, and a cost limit. The specific thresholds depend on your product and users; there is no workload-independent result that can set them for you.
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Choose a decision rule before running tests. You might compare cost among models that meet a quality floor, compare quality among models that satisfy a latency ceiling, or map the feasible tradeoffs across both. Avoid combining quality, latency, and cost into one score unless you can justify the weights: a single composite can hide a failure that matters to users.
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Sample and curate requests from the workload the application is expected to handle. Include common cases, long or difficult inputs, edge cases, and known failure modes. Keep a held-out set when possible so prompt tuning does not turn the evaluation set into a target. Google recommends a diverse evaluation set aligned with the task, and OpenAI describes evals as structured tests using inputs like those expected in production (Google Cloud guidance; OpenAI evaluation best practices).
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Define how each case will be graded before comparing models. Use an appropriate mix of references, executable checks, rubrics, and human review. OpenAI describes methods including exact or string matching, function-call accuracy, executable grading, and reference-guided grading; Google notes that automated metrics can miss context and nuance, making human assessment useful for some tasks.
- Structured outputs: Check schema validity, required fields, and value constraints.
- Tool-using tasks: Check whether the model selected the right tool, supplied correct arguments, and completed the task.
- Factual or reference-based tasks: Compare claims with appropriate references or ground truth.
- Open-ended responses: Apply a task-specific rubric and sample outputs for human review.
- Safety-sensitive tasks: Track refusals and unsafe responses against the behavior your application requires.
If you use an AI judge for subjective scoring, first check its agreement with human labels on examples from your own rubric. Keep per-case outputs and scores: an overall average alone can conceal a cluster of serious failures.
Keep the comparison conditions consistent
Run each candidate on the same cases with the same prompt, context, tools, output constraints, and decoding settings wherever the models support them. Record the model identifier and version, provider and region, settings, date, and any caching or batching configuration. Repeat runs when output variability could change the conclusion.
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These controls make the results interpretable: if prompts or workload examples differ, a score difference may reflect the test setup rather than the model. Preserve raw outputs as well as aggregate metrics so you can investigate why a candidate passed or failed.
Measure quality as task outcomes
Report task success and the failure modes that matter to the application, both overall and across important case types. Depending on the task, useful measures include factual correctness, format or schema compliance, tool-call correctness, refusal behavior, and human-rated usefulness. No single broad benchmark captures all of these.
When the evaluation platform supports it, ground-truth data can help make results repeatable. Google Cloud Vertex AI documents evaluating compatible model versions and comparing evaluation results across jobs (Vertex AI model evaluation). Keep the grading method and any human-review sample size visible alongside the result so readers of the comparison can interpret it.
Benchmark latency and throughput under realistic load
Test the request mix and concurrency the service is expected to face. A single-request measurement does not reveal how a candidate behaves under load. Record end-to-end latency distributions, throughput, timeouts, and errors; report at least p50 and a tail percentile such as p95 or p99. For streamed responses, record time to first token (TTFT) and inter-token latency as well.
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Capture prompt and output length distributions because generated-token count affects completion latency. Compare candidates at the same load and configuration, then identify the highest tested load at which each still meets its service objectives. Google Cloud’s benchmarking guidance names TTFT, inter-token latency, tokens per second per user, and end-to-end response latency as inference measures (Google Cloud AI accelerator benchmarking).
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Use recorded input and output usage with the provider’s billing rules for the tested configuration. Include retries and failed attempts that consumed tokens, as well as repeated model calls or paid tools in the measured path. Report both cost per request and, when you grade task success, cost per successful task. State how failures and their usage are counted.
Usage-based charges and generated-token counts can affect cost and latency; OpenAI discusses these production considerations, and Anthropic recommends recording cost per task alongside evaluation score (OpenAI production best practices; Anthropic cost and intelligence guidance). Verify current provider rates when you run the comparison and date the calculation, since prices and model availability can change. A cost figure without the usage assumptions and pricing date is difficult to reproduce.
Compare candidates side by side
For a real candidate set, put the decision-relevant measures in one table. Include the test and configuration details needed to interpret the results, rather than publishing an unexplained ranking.
| Comparison area | What to report |
|---|---|
| Quality | Task success overall and by important case type; human-reviewed quality and grader agreement where relevant. |
| Latency | p50 and p95 or p99 end-to-end latency; TTFT and inter-token latency for streaming. |
| Capacity and reliability | Throughput at target concurrency, plus error and timeout rates. |
| Cost | Actual cost per request and cost per successful task, with usage and failure-counting assumptions. |
| Test conditions | Model and version, provider and region, prompt and configuration, test-set date, and run date. |
A plot of quality against cost or latency can make tradeoffs visible. Mark candidates that violate any required threshold instead of letting a low price or strong average quality obscure an unacceptable service result.
Select from the candidates that pass, then validate
Remove candidates that miss minimum quality, latency, reliability, or budget requirements. Among those that remain, choose based on the workload’s priorities and the margin each has against the thresholds. A model barely within a limit may leave less room for traffic spikes or changes in the request mix than one with more headroom.
Before broad rollout, validate the leading candidate in staging or through a controlled production rollout. Monitor quality signals alongside throughput, latency, and errors. Google Cloud’s application guidance covers task-aligned evaluation and deployment considerations, while its benchmarking guidance identifies production performance signals including throughput, latency, and errors (Google Cloud generative AI application guidance; Google Cloud benchmarking). Rerun the evaluation when the data, prompts, model version, or behavior changes.
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