BFCL
BFCL (Berkeley Function Calling Leaderboard) is a benchmark released by the University of California, Berkeley, designed to evaluate large language models on function calling and tool use. The benchmark covers multiple programming languages and diverse function-calling scenarios—including single-function, parallel, and nested calls—and systematically assesses instruction comprehension, parameter extraction, and formatted output through a standardized test set.
Core Facts
Timeline (last 90 days)
On the BFCL tool-calling test, SmolLM3-3B scores 0.945, slightly above Gemma 4 E2B's 0.927, but the difference is not significant; Qwen3-4B (0.940), Qwen3-1.7B (0.935), Qwen2.5-1.5B (0.910), and Granite 3.3 2B (0.920) are all at the same level as Gemma.
In the BFCL official 100-item parallel tool-calling test, Saluki scored 42, surpassing the original model's 35, representing approximately a 20% improvement in success rate.
BFCL is a specialized benchmark released by the University of California, Berkeley, that evaluates the accuracy of large language models in structured function-calling scenarios.
In the tool-calling benchmark BFCL, Qwen3 scored 83%.
In benchmark tests, 2.0 shows notable improvements over the base model on IFBench (37.3→43.7), When2Call (48→58), and BFCL irrelevance (60→78).
BFCL (Berkeley Function Calling Leaderboard) is a tool-calling capability evaluation benchmark released by the University of California, Berkeley, with V4 being its multi-turn dialogue extension.
All Facts (6)
BFCL (Berkeley Function Calling Leaderboard) is a tool-calling capability evaluation benchmark released by the University of California, Berkeley, with V4 being its multi-turn dialogue extension.
75%UnverifiedOn the BFCL tool-calling test, SmolLM3-3B scores 0.945, slightly above Gemma 4 E2B's 0.927, but the difference is not significant; Qwen3-4B (0.940), Qwen3-1.7B (0.935), Qwen2.5-1.5B (0.910), and Granite 3.3 2B (0.920) are all at the same level as Gemma.
50%UnverifiedIn the BFCL official 100-item parallel tool-calling test, Saluki scored 42, surpassing the original model's 35, representing approximately a 20% improvement in success rate.
50%UnverifiedBFCL is a specialized benchmark released by the University of California, Berkeley, that evaluates the accuracy of large language models in structured function-calling scenarios.
50%UnverifiedIn the tool-calling benchmark BFCL, Qwen3 scored 83%.
50%UnverifiedIn benchmark tests, 2.0 shows notable improvements over the base model on IFBench (37.3→43.7), When2Call (48→58), and BFCL irrelevance (60→78).
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