AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

LLM Stats

BFCL_v3_MultiTurn

Berkeley Function Calling Leaderboard (BFCL) V3 MultiTurn benchmark that evaluates large language models' ability to handle multi-turn and multi-step function calling scenarios. The benchmark introduces complex interactions requiring models to manage sequential function calls, handle conversational context across multiple turns, and make dynamic decisions about when and how to use available functions. BFCL V3 uses state-based evaluation by verifying the actual state of API systems after function execution, providing more realistic assessment of function calling capabilities in agentic applications.

Evaluation context and evidence

Source
LLM Stats
Publisher
Not recorded
Benchmark release
Not recorded
Collected
2026-08-17
Modality
text

These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.

Original sources

Reported results 2

Shown in source observation order, not ranked. Dates retain the precision provided by the source.

ModelValueUnitProtocolReported date and basisSource
MiniMax M2.50.768Not recordedNot recorded2026-02-12 · model_announcementLLM Stats
Nemotron Nano 9B v20.669Not recordedNot recorded2025-08-18 · model_announcementLLM Stats