📖 The AI Tool Bible

LangSmith vs OpenAI Evals

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

 
LangSmith
Evaluation
OpenAI Evals
Evaluation
TaglineLangChain's eval + observability platform.OpenAI's open-source framework for benchmarking LLMs against a shared registry of evaluations.
CategoryEvaluationEvaluation
PricingFreemium· Free starter; Plus $39/mo per seatFree· Free (MIT); you pay OpenAI API costs for eval runs
ModelPlatform (any LLM)OpenAI GPT models (extensible)
Editorial score8.7 / 10—
Use cases
LLM tracingevalsLangChain integration
llm-benchmarkingregression-testingmodel-graded-evalprompt-evaluationcustom-evals
Pros
  • Tight LangChain integration
  • Strong tracing UX
  • Mature dataset/eval flows
  • Reasonable per-seat pricing
  • Large public registry of ready-to-run evals
  • MIT-licensed and fully open source
  • Supports basic, model-graded, and custom evals
  • Canonical format many published benchmarks adopt
  • W&B and Snowflake logging out of the box
Cons
  • Best value if you're on LangChain
  • UI can feel dense
  • Registry and defaults are OpenAI-centric
  • Model-graded evals can rack up API costs fast
  • UX is CLI + YAML, no hosted dashboard
  • Less actively iterated than commercial rivals
Websitewww.langchain.comgithub.com
Pick LangSmith if
  • ✅ Tight LangChain integration
  • ✅ Strong tracing UX
  • ✅ Mature dataset/eval flows
  • ✅ Reasonable per-seat pricing
Pick OpenAI Evals if
  • ✅ Large public registry of ready-to-run evals
  • ✅ MIT-licensed and fully open source
  • ✅ Supports basic, model-graded, and custom evals
  • ✅ Canonical format many published benchmarks adopt
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