activeTool use
BFCL
Metric: Function Calling Accuracy (higher is better)Introduced: 2023
Berkeley Function Calling Leaderboard (BFCL) evaluating LLM ability to correctly call functions/APIs with proper arguments across diverse domains. Currently on v3.
Models ranked
17
tracked on this benchmark
Score band
77.5 – 10.8
best → lowest tracked
Snapshot trend
+21.29
Apr 14 → Apr 19 · 5 models
Leaderboard
Tracked models ranked by Function Calling Accuracy (higher is better).
#Model variant and provenanceRelative to leaderScore
1
Claude Opus 4.5
77.5Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
2
Claude Sonnet 4.5
73.2Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
3
Qwen3.5-397B-A17B
72.9Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 19, 2026Confidence: Not recordedSource
4
Gemini 3 Pro
72.5Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 19, 2026Confidence: Not recordedSource
5
Claude Haiku 4.5
68.7Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 19, 2026Confidence: Not recordedSource
6
Gemini 2.5 Flash
56.2Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
7
GPT-5 Mini
55.5Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 19, 2026Confidence: Not recordedSource
8
GPT-4.1
54.0Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
9
o4-mini
53.2Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
10
GPT-4.1 Mini
50.5Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
11
Kimi K2.5
47.1Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 19, 2026Confidence: Not recordedSource
12
Mistral Large 2
38.4Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
13
Llama 3.3 70B Instruct
31.9Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
14
Llama 3.1 8B Instruct
25.8Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
15
Llama 3.2 3B Instruct
21.9Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
16
Ministral 8B
11.1Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
17
Llama 3.2 1B Instruct
10.8Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
How to read this benchmark
This benchmark scores models where higher is better. Use scores for directional filtering and shortlisting, not universal quality ranking; then validate pricing, context window, provider availability, and fit for your workload.
Trust this score when
- There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
- The model list covers the same version family you can actually deploy today.
- Top candidates overlap with your required routing and feature requirements.
Be cautious when
- There is only one benchmark snapshot or the dataset appears stale.
- The benchmark metric direction is opposite of your decision objective.
- The score difference between options is narrow and likely within implementation variance.
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Last reviewed: Apr 15, 2026