# Bfcl

BFCL, the Berkeley Function Calling Leaderboard, tests whether a model can use tools
correctly. The current version, V4, came out in July 2025. The model is given a list of
available functions and a request, and has to pick the right function, fill in the right
arguments, and, when nothing fits, call nothing at all.

Correctness is checked mechanically: the call the model produced is compared to the expected
one, function by function and argument by argument, and multi-step tasks are checked by the
state they leave behind. The overall score is a weighted mix: 40% agentic tasks (searching
the web to answer a multi-step question, and storing then recalling facts across a
conversation), 30% multi-turn conversations, 10% real requests users submitted to the
leaderboard, 10% curated single calls, and 10% "hallucination" cases where the correct
answer is to call no tool.

A high score means the model is precise with tool calls in a controlled setting. It says
less about your agent, because your tools have their own quirks and your users don't phrase
requests like a benchmark. The hallucination slice is the one to watch. A model that calls a
tool when it shouldn't is the model that takes an action nobody asked for.

V4 is where BFCL stopped being a single-call test and became an agent test. The earlier
versions measured one call at a time, and most top models had saturated them. The agentic
and multi-turn parts now decide the ranking.

## Questions answered under this concept

- [Does a high BFCL score mean an agent will work in production?](https://tessary.ai/answers/bfcl/does-a-high-bfcl-score-mean-an-agent-will-work-in-production)
- [What changed in BFCL's V4?](https://tessary.ai/answers/bfcl/what-changed-in-bfcls-v4)
- [What is BFCL?](https://tessary.ai/answers/bfcl/what-is-bfcl)

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Source: https://tessary.ai/answers/bfcl
All concepts: https://tessary.ai/answers
From Tessary, agent reliability for AI agents in production: https://tessary.ai