BFCL v2
Berkeley Function Calling Leaderboard (BFCL) v2 is a comprehensive benchmark for evaluating large language models' function calling capabilities. It features 2,251 question-function-answer pairs with enterprise and OSS-contributed functions, addressing data contamination and bias through live, user-contributed scenarios. The benchmark evaluates AST accuracy, executable accuracy, irrelevance detection, and relevance detection across multiple programming languages (Python, Java, JavaScript) and includes complex real-world function calling scenarios with multi-lingual prompts.
Llama 3.3 70B Instruct from Meta currently leads the BFCL v2 leaderboard with a score of 0.773 across 5 evaluated AI models.
Llama 3.3 70B Instruct leads with 77.3%, followed by 
Llama 3.1 Nemotron Ultra 253B v1 at 74.1% and Llama-3.3 Nemotron Super 49B v1 at 73.7%.
Progress Over Time
Interactive timeline showing model performance evolution on BFCL v2
BFCL v2 Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | 70B | — | — | |||
| 2 | 253B | — | — | |||
| 3 | 50B | — | — | |||
| 4 | 3B | — | — | |||
| 5 | 8B | — | — |
FAQ
Common questions about BFCL v2.
More evaluations to explore
Related benchmarks in the same category
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions