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.
MiniMax M2.5 from MiniMax currently leads the BFCL_v3_MultiTurn leaderboard with a score of 0.768 across 2 evaluated AI models.
MiniMax M2.5 leads with 76.8%, followed by 
Nemotron Nano 9B v2 at 66.9%.
Progress Over Time
Interactive timeline showing model performance evolution on BFCL_v3_MultiTurn
BFCL_v3_MultiTurn Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  MiniMax | 230B | 1.0M | $0.30 / $1.20 | ||
| 2 | NVIDIA | 9B | — | — |
FAQ
Common questions about BFCL_v3_MultiTurn.
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