📖 The AI Tool Bible

Berkeley Function-Calling Leaderboard vs Weights & Biases

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

 
Berkeley Function-Calling Leaderboard
Evaluation
Weights & Biases
Evaluation
TaglineOpen benchmark from UC Berkeley that ranks LLMs on real-world tool-use and function-calling accuracy.The ML experiment tracker, now with LLM eval features.
CategoryEvaluationEvaluation
PricingFree· Free and open source; you pay only for inference when reproducing runs.Freemium· Free personal; team from $50/mo per seat
ModelMulti-modelPlatform (any LLM)
Editorial score8.1 / 108.4 / 10
Use cases
function-calling evaltool-use benchmarkingagent model selectionmulti-turn evalcost/latency comparison
ML experimentsLLM evalWeave
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
  • Industry-standard for ML tracking
  • Weave adds LLM-native eval
  • Mature, reliable
  • Strong enterprise features
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
  • Heavier UX than LLM-native tools
  • LLM features still catching up
Websitegorilla.cs.berkeley.eduwandb.ai
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 Weights & Biases if
  • ✅ Industry-standard for ML tracking
  • ✅ Weave adds LLM-native eval
  • ✅ Mature, reliable
  • ✅ Strong enterprise features
Compare a different pair →