Compare AI models on the same representative tasks, under consistent settings, and measure three separate outcomes: how often each task succeeds, how long the complete workflow takes, and what each attempt costs. Set a minimum success rate and maximum acceptable latency before testing; then compare the cost per successful task among models that meet those limits.
How do I compare AI models?
Start with the work the model will actually do—not a general-purpose leaderboard. A model that performs well on one benchmark may be a poor fit for a workflow with different inputs, tools, output requirements, or failure costs. OpenAI recommends comparing candidates on the same inputs and evaluating quality and cost tradeoffs in the intended use case: Model selection.
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- Separate the workload into task types. Group materially different jobs, such as extraction, drafting, classification, or multi-step tool use. Include routine examples and consequential edge cases so an easy majority does not hide a weak spot.
- Define success before running models. Write an observable pass condition for each task. Use deterministic checks where possible; otherwise use a scoring rubric and trained human review. Keep partial-credit scores if they reveal meaningful differences, but also set a pass/fail threshold. OpenAI’s evaluation best practices recommend defining an objective, dataset, and metrics, and including a pass/fail threshold in addition to a numerical score.
- Build a representative evaluation set. Use permitted examples from historical or production work, human-curated cases, or purpose-built examples. If you will tune prompts or system behavior using the results, reserve a separate held-out set to check that improvements generalize.
- Freeze the test conditions. For each candidate, record the model identifier or version, prompt, tools, reasoning or sampling settings, token limits, output format, service region or tier when relevant, and evaluation date. Keep these consistent. If provider defaults differ and cannot be aligned, document the differences rather than describing the comparison as identical.
- Run the same tasks and preserve all outcomes. Record successes, failures, and refusals; do not silently remove inconvenient cases. Repeat tasks when outputs vary between runs, especially for agentic workflows.
- Measure the full user-visible workflow. Set a consistent start and stop point. Include relevant orchestration and tool delays, not just model generation time. If streaming matters, record time to first token as well as completion time.
- Calculate the complete cost of each attempt. Include billed input and output, retries, tool calls, and other model calls in the workflow. Report both cost per attempted task and cost per successful task, with the success count and denominator.
If an AI grader scores open-ended answers, check its agreement with human labels and look for position or verbosity bias. A grader score is not automatically a reliable measure of task success.
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For a one-shot workflow, report the number of passing attempts divided by the number of attempts, alongside the raw counts—for example, “82 of 100 tasks passed.” The denominator matters: a rate without the number of trials can hide how little evidence supports it.
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For variable outputs, repeat the same task and choose a metric that matches the product requirement. Anthropic explains that agent behavior can vary between runs in its guide to evaluating AI agents.
- pass@k measures the chance that at least one of k attempts succeeds. It fits a workflow that can make several attempts and use one successful result.
- pass^k measures the chance that all k attempts succeed. It fits a requirement for consistent success on every attempt.
These are not interchangeable with first-attempt accuracy: pass@k gets more chances to succeed as k increases, while pass^k imposes a stricter all-attempts condition. State k and how attempts were run whenever you report either measure. Do not compare a repeated-attempt result directly with a one-shot result without labeling the difference.
How do I measure LLM latency?
Measure elapsed time for the same workload and service conditions, using identical start and stop definitions across candidates. For an interactive assistant, time to first token may affect perceived responsiveness; for a task that must return a complete result, completion time is essential. For a tool-using agent, include the time spent on relevant calls and orchestration.
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For operational workloads, report a median and a high percentile, such as the 95th percentile, to show both typical and slow-tail performance. This is a practical reporting choice, not a percentile standard prescribed by the cited guide. Keep the workload and service conditions comparable; otherwise latency differences may reflect the setup as much as the model.
Should I compare cost per token or cost per successful task?
Use token prices to estimate an input to the calculation, not as the final measure of value. A workflow’s total spend depends on the amounts of input and output, applicable cached input, retries, tools, and additional model calls. A configuration with a low price per token may still cost more to deliver a completed task if it fails more often or requires extra attempts.
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For each candidate, calculate:
- Cost per attempted task: total measured spend divided by the number of attempts.
- Cost per successful task: total measured spend divided by the number of tasks that passed.
Include the failure rate and denominator so a low cost does not look favorable merely because failed work was excluded. Anthropic’s cost and intelligence guidance recommends evaluating cost per completed task and notes that results can change by workload. Prices and model versions change, so label the provider, model version, currency, price date, and region or service tier when relevant; do not combine prices from one date with performance results from another as though they came from one test.
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Vendor-reported results can illustrate the metric, but they are not universal rankings. Anthropic reports selected internal benchmark examples on its cost-and-intelligence page, including results from a SWE-bench Pro subset chosen for compatibility with its harness; the company says those results are not comparable with the public leaderboard. Treat such figures as evidence about that publisher’s setup, not a prediction for another workload.
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How should I interpret the three metrics?
Keep success, latency, and cost visible as separate axes rather than collapsing them immediately into one weighted score. A single score can conceal the reason a candidate wins and make tradeoffs hard to explain. First remove candidates that miss a required quality floor or latency budget; then compare the costs and benefits of those that remain.
- Look for dominated options. If one model meets or exceeds another’s success and latency while costing less, the more expensive, slower, or less successful option has no clear advantage on those measures. Where no candidate wins on every relevant dimension, choose according to explicit constraints and the consequence of failure.
- Test meaningful configurations. A model’s effort or sampling setting can change quality, speed, and spend. Compare the configurations you could actually deploy, not just model names, using the same workload and pass criteria. OpenAI’s model-selection guidance recommends experiments on the same task inputs; Anthropic’s guidance describes cost-quality tradeoffs for its own offerings.
- Consider routing only when you measure the whole policy. For workloads with distinct easy and hard cases, a lower-cost first pass with verification and escalation may be useful. Measure the added latency and model calls for escalated cases, as well as the final success rate and cost per success.
Why public AI benchmarks do not name one best model
Benchmark scores depend on the tasks included, prompts, harness, model versions, and grading method. A model that leads on one benchmark is not automatically best for a different workflow. Use public scores to shortlist candidates; use a representative local evaluation to decide.
A 2024 review by McIntosh and coauthors examined 23 benchmarks and identified limitations involving bias, implementation consistency, evaluator diversity, prompt complexity, and how well benchmarks measure genuine reasoning. That review is a critique of benchmark methodology, not proof that every benchmark is invalid: Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence.
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