📖 The AI Tool Bible

Berkeley Function-Calling Leaderboard vs LangSmith

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

 
Berkeley Function-Calling Leaderboard
Evaluation
LangSmith
Evaluation
TaglineOpen benchmark from UC Berkeley that ranks LLMs on real-world tool-use and function-calling accuracy.LangChain's eval + observability platform.
CategoryEvaluationEvaluation
PricingFree· Free and open source; you pay only for inference when reproducing runs.Freemium· Free starter; Plus $39/mo per seat
ModelMulti-modelPlatform (any LLM)
Editorial score—8.7 / 10
Use cases
function-calling evaltool-use benchmarkingagent model selectionmulti-turn evalcost/latency comparison
LLM tracingevalsLangChain integration
Pros
  • Reproducible: open dataset, harness, and pip-installable eval package
  • Covers multi-turn, web search, format sensitivity, not just single-shot calls
  • Tracks cost and latency alongside accuracy
  • Backed by peer-reviewed work (ICML 2025) and actively updated
  • Interactive demo lets you sanity-check models on your own schemas
  • Tight LangChain integration
  • Strong tracing UX
  • Mature dataset/eval flows
  • Reasonable per-seat pricing
Cons
  • Academic benchmark, not a managed product or SLA
  • Function-calling focus only; not a general LLM leaderboard
  • Reproducing top runs can get expensive on frontier APIs
  • Best value if you're on LangChain
  • UI can feel dense
Websitegorilla.cs.berkeley.eduwww.langchain.com
Pick Berkeley Function-Calling Leaderboard if
  • ✅ Reproducible: open dataset, harness, and pip-installable eval package
  • ✅ Covers multi-turn, web search, format sensitivity, not just single-shot calls
  • ✅ Tracks cost and latency alongside accuracy
  • ✅ Backed by peer-reviewed work (ICML 2025) and actively updated
Pick LangSmith if
  • ✅ Tight LangChain integration
  • ✅ Strong tracing UX
  • ✅ Mature dataset/eval flows
  • ✅ Reasonable per-seat pricing
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