There is no trustworthy current leaderboard, so the most recent reliable figure is already months old.
BFCL (Berkeley Function Calling)
BFCL, the Berkeley Function Calling Leaderboard, tests whether a model can correctly call software tools: pick the right function and fill in its arguments when asked to actually do something, not just chat about it.
Version 4 extends this to full agent tasks like web search and memory. It is the standard scoreboard for the tool-calling that underpins AI agents.
Given a request like 'book a table for four at 7pm' and a set of available functions, the model must emit the correct function call with the right arguments (date, time, party size). BFCL checks the call against the expected one, so a plausible-looking but wrong argument fails.
If you are building anything that takes actions (querying an API, running code, browsing the web), tool-calling accuracy is the signal that matters most, and BFCL is the most-cited independent measure of it. It is run by a university lab, not the model makers, so the comparison is apples to apples.
Overall accuracy across function-calling categories, evaluated independently by UC Berkeley's Gorilla team against a pinned, reproducible code commit (all model responses are published). Higher is better. Maintained at gorilla.cs.berkeley.edu; the board was last updated in April 2026.
Scores average across many call types; the current leaders sit in the low-to-mid 70s, strong open models trail a few points back, and weak models drop into the 50s and below. There is no single human baseline because the task is machine-checkable correctness, not human judgment.
The main caveat is freshness: the official board was last updated in April 2026, so the newest frontier models (Claude Opus 4.7 and 4.8, Gemini 3.1, GPT-5.5) are not yet ranked on it, and the current top reflects the prior generation. The headline page is also JavaScript-rendered; the real data lives in a companion CSV at the same site. Tool-calling accuracy on a fixed test can also overstate reliability in messy real-world use.
The benchmark creates the need to know. The catalog explains the ideas behind it: