BFCL-v3
Berkeley Function Calling Leaderboard v3 (BFCL-v3) is an advanced benchmark that evaluates large language models' function calling capabilities through multi-turn and multi-step interactions. It introduces extended conversational exchanges where models must retain contextual information across turns and execute multiple internal function calls for complex user requests. The benchmark includes 1000 test cases across domains like vehicle control, trading bots, travel booking, and file system management, using state-based evaluation to verify both system state changes and execution path correctness.
GLM-4.5 from Zhipu AI currently leads the BFCL-v3 leaderboard with a score of 0.778 across 18 evaluated AI models.
GLM-4.5 leads with 77.8%, followed by GLM-4.5-Air at 76.4% and 
LongCat-Flash-Thinking at 74.4%.
Progress Over Time
Interactive timeline showing model performance evolution on BFCL-v3
BFCL-v3 Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Zhipu AI | 355B | — | — | ||
| 2 | Zhipu AI | 106B | — | — | ||
| 3 | Meituan | 560B | — | — | ||
| 4 |  Alibaba Cloud / Qwen Team | 80B | — | — | ||
| 5 | Alibaba Cloud / Qwen Team | 236B | 262K | $0.45 / $3.49 | ||
| 5 | Alibaba Cloud / Qwen Team | 235B | — | — | ||
| 7 | Alibaba Cloud / Qwen Team | 33B | — | — | ||
| 8 | Alibaba Cloud / Qwen Team | 235B | — | — | ||
| 9 | Alibaba Cloud / Qwen Team | 80B | — | — | ||
| 10 | Alibaba Cloud / Qwen Team | 33B | — | — | ||
| 11 | Alibaba Cloud / Qwen Team | 480B | — | — | ||
| 12 | Alibaba Cloud / Qwen Team | 31B | — | — | ||
| 13 | Alibaba Cloud / Qwen Team | 236B | 262K | $0.30 / $1.49 | ||
| 14 | Alibaba Cloud / Qwen Team | 4B | 262K | $0.10 / $1.00 | ||
| 15 | Alibaba Cloud / Qwen Team | 31B | — | — | ||
| 15 | Alibaba Cloud / Qwen Team | 9B | 262K | $0.08 / $0.50 | ||
| 17 | Alibaba Cloud / Qwen Team | 4B | 262K | $0.10 / $0.60 | ||
| 18 | Alibaba Cloud / Qwen Team | 9B | 262K | $0.18 / $2.09 |
FAQ
Common questions about BFCL-v3.
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