llmboard.ai
Benchmarks
CompareRankings
llmboard.ai
Benchmarks
CompareRankings
HomeBenchmarksreasoningBFCL-v3

reasoning benchmark

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.

Updated Aug 11, 2026

Models19
Model coverage19
MetricScore
EvidenceB

On this page

  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

BFCL-v3 Ranking

Higher score ranks better on this benchmark.

19 rows
Columns

Show columns

1ZAGLM-4.5Zhipu AI77.8%100.0%19CAug 11, 2026
2ZAGLM-4.5-AirZhipu AI76.4%94.4%19CAug 11, 2026
3MELongCat-Flash-ThinkingMeituan74.4%88.9%19CAug 11, 2026
4MIMAI-Thinking-1Microsoft72.0%83.3%19CAug 11, 2026
5ACQwen3-Next-80B-A3B-ThinkingAlibaba Cloud / Qwen Team72.0%77.8%19CAug 11, 2026
6ACQwen3-235B-A22B-Thinking-2507Alibaba Cloud / Qwen Team71.9%72.2%19CAug 11, 2026
7ACQwen3 VL 235B A22B ThinkingAlibaba Cloud / Qwen Team71.9%66.7%19CAug 11, 2026
8ACQwen3 VL 32B ThinkingAlibaba Cloud / Qwen Team71.7%61.1%19CAug 11, 2026
9ACQwen3-235B-A22B-Instruct-2507Alibaba Cloud / Qwen Team70.9%55.6%19CAug 11, 2026
10ACQwen3-Next-80B-A3B-InstructAlibaba Cloud / Qwen Team70.3%50.0%19CAug 11, 2026
11ACQwen3 VL 32B InstructAlibaba Cloud / Qwen Team70.2%44.4%19CAug 11, 2026
12ACQwen3-Coder 480B A35B InstructAlibaba Cloud / Qwen Team68.7%38.9%19CAug 11, 2026
13ACQwen3 VL 30B A3B ThinkingAlibaba Cloud / Qwen Team68.6%33.3%19CAug 11, 2026
14ACQwen3 VL 235B A22B InstructAlibaba Cloud / Qwen Team67.7%27.8%19CAug 11, 2026
15ACQwen3 VL 4B ThinkingAlibaba Cloud / Qwen Team67.3%22.2%19CAug 11, 2026
16ACQwen3 VL 30B A3B InstructAlibaba Cloud / Qwen Team66.3%16.7%19CAug 11, 2026
17ACQwen3 VL 8B InstructAlibaba Cloud / Qwen Team66.3%11.1%19CAug 11, 2026
18ACQwen3 VL 4B InstructAlibaba Cloud / Qwen Team63.3%5.6%19CAug 11, 2026
19ACQwen3 VL 8B ThinkingAlibaba Cloud / Qwen Team63.0%0.0%19CAug 11, 2026

BFCL-v3 Score Distribution

A closer view of the leading scores on this benchmark.

BFCL-v3

BFCL-v3 Highlights

The leading models and scores on this benchmark.

Rank #1GLM-4.577.8%Rank #2GLM-4.5-Air76.4%Rank #3LongCat-Flash-Thinking74.4%Rank #4MAI-Thinking-172.0%

What is BFCL-v3?

What BFCL-v3 measures and how its scores work.

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.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
BFCL-v3
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
bfcl-v3|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about BFCL-v3.

Which model scores highest on BFCL-v3?

GLM-4.5 is currently ranked first with 77.8%.

What does BFCL-v3 measure?

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.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

19 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.

Rankings

OverallCodingText ArenaPricing

Modalities

All ModelsImage GenerationImage EditingVideo GenerationImage-to-VideoVideo EditingText-to-SpeechSpeech-to-TextEmbeddings

Benchmarks

All BenchmarksReasoningMathCoding

Vendors

All VendorsOpenAIAnthropicGoogle
llmboard.aiCopyright 2026 llmboard.ai