Skip to content

Benchmark

Berkeley Function-Calling Leaderboard

A benchmark suite from UC Berkeley that evaluates how accurately language models generate function/tool calls across diverse languages and use cases.

Known aliases

  • Berkeley Function Calling Leaderboard v4
  • BFCL

Current stories

buildOne report1 publisher

Grading a trace twice separates where an agent broke from whether the task finished

NVIDIA's developer blog sets out the five-level rollup from step to benchmark and the two scores that read the same trace. Step-level says where the chain broke. End-to-end reads the environment and says whether the refund posted.

Reality

Evidence55
Adoption20
Hype gap+12
Incentives50
Confidence60
buildOne report1 publisher

Google's ToolGrad back-writes the user query from a chain it has already executed

A Google XR team fine-tuned Gemma-3 models on data generated answer-first and says they match proprietary LLMs on tools they never trained on. The post's cost and pass-rate claims carry no numbers.

Publishers:research.google

Reality

Evidence30
Adoption10
Hype gap+40
Incentives60
Confidence35
buildOne report1 publisher

Running the same retail task eight times drops tool-calling agents under 25%

tau-bench scores agents on the database state they leave behind and then reruns each task eight times, and the consistency figure that falls out is a better launch gate than the single-trial accuracy most teams quote.

Publishers:arxiv.org

Reality

Evidence62
Adoption
Not scored
Hype gap+8
Incentives55
Confidence58
buildOne report1 publisher

The conductor is the bottleneck: local agent stacks fail at orchestration, not at the workers

A dev.to build log finds Gemma 4 26B holds deep single-artifact work but loses the plan after one or two hand-offs, while the coordinators that can plan will not fit in memory.

Publishers:dev.to

Reality

Evidence44
Adoption
Not scored
Hype gap−5
Incentives22
Confidence50