reasoning benchmark
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.
Updated Aug 11, 2026
Models2
Model coverage2
MetricScore
EvidenceB
BFCL_v3_MultiTurn Ranking
Higher score ranks better on this benchmark.
| 01 | MI | 76.8% | 100.0% | 2 | C | |
| 02 | NV | 66.9% | 0.0% | 2 | C |
BFCL_v3_MultiTurn Score Distribution
A closer view of the leading scores on this benchmark.
BFCL_v3_MultiTurn Highlights
The leading models and scores on this benchmark.
What is BFCL_v3_MultiTurn?
What BFCL_v3_MultiTurn measures and how its scores work.
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.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- BFCL_v3_MultiTurn
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- bfcl-v3-multiturn|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about BFCL_v3_MultiTurn.
Which model scores highest on BFCL_v3_MultiTurn?
MiniMax M2.5 is currently ranked first with 76.8%.
What does BFCL_v3_MultiTurn measure?
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.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
2 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.