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 7, 2026
Published models2
Registry coverage2
MetricScore
EvidenceB
BFCL_v3_MultiTurn leaderboard
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | MI | 76.8% | 100.0% | 2 | C | |
| 2 | NV | 66.9% | 0.0% | 2 | C |
Score distribution
Top published rows on the benchmark's original scale.
BFCL_v3_MultiTurn highlights
The top published results on this benchmark's own scale.
What is BFCL_v3_MultiTurn?
Definition and scoring fields from the benchmark registry.
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. The current registry marks this benchmark as not independently verified with evidence level B.
- Family
- BFCL_v3_MultiTurn
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- bfcl-v3-multiturn|llm-stats-current
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
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 unique published model results are currently shown.
Does this benchmark affect the overall score?
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.