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Glossary

Benchmarking

What is Benchmarking?

In AI, benchmarking means systematically comparing models or systems using defined tasks and criteria. Public benchmarks measure things like general knowledge, reasoning or coding ability; for practical use, however, test cases from your own day-to-day work matter most. Typical criteria are accuracy, completeness, response time and cost. This helps decide whether a smaller, cheaper model is good enough for a task.

How KI.ordo helps

The KI.ordo template “Compare models (benchmark)” sends the same task to a Claude Haiku and a Claude Sonnet model and rates both answers for accuracy, completeness and clarity.

Learn more →: Benchmarking

Matching templates

Topics

  • Model comparison
  • Evaluation
  • Quality

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Related terms

Accuracy and stability of AI models

Accuracy describes how often an AI model produces correct results; stability describes how reliably that performance holds up – with slightly altered inputs, new data or over time. A model can be very accurate in testing yet unstable if merely rephrasing a question leads to a different answer. For real-world use, both properties are checked with fixed test cases, cross-validation and ongoing monitoring.

  • Model quality
  • Robustness
  • +1

AI Quality Assurance Specialist

An AI quality assurance specialist checks whether AI applications work reliably, correctly and appropriately. The role includes test questions and benchmarks, comparing different models, analysing user ratings, and spotting hallucinations and deterioration over time. For example, before switching to a new model, 50 typical customer enquiries are answered by both models and compared against fixed criteria.

  • Quality
  • Testing
  • +1
Template

LLM Costs

The costs of large language models are usually billed per token, i.e. text units roughly the size of a syllable to a word; input and output tokens are priced separately, with output generally more expensive. Larger models cost more per token than small, fast models such as Claude Haiku, which are often sufficient for simple tasks. Prompt caching lowers costs when the same long context is sent repeatedly. Concise instructions and carefully selected knowledge help as well.

  • Costs
  • Tokens
  • +1

BLEU score

The BLEU score (Bilingual Evaluation Understudy) is a metric developed at IBM in 2002 to evaluate machine translations automatically. It measures how many word sequences in a translation also appear in one or more human reference translations and gives a value between 0 and 1 (often shown as 0–100). BLEU is fast and comparable but captures neither meaning nor style well – good paraphrases can score poorly.

  • Evaluation
  • Translation
  • +1