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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. High benchmark score alone doesn't make a model the right pick — weigh it against pricing, API availability, and release date.

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).

Compare candidates
#Model variant and provenanceScore
1
Claude Opus 4.5
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
77.5
2
Claude Sonnet 4.5
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
73.2
3
Qwen3.5-397B-A17B
Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 19, 2026Confidence: Not recordedSource
72.9
4
Gemini 3 Pro
Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 19, 2026Confidence: Not recordedSource
72.5
5
Claude Haiku 4.5
Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 19, 2026Confidence: Not recordedSource
68.7
6
Gemini 2.5 Flash
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
56.2
7
GPT-5 Mini
Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 19, 2026Confidence: Not recordedSource
55.5
8
GPT-4.1
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
54.0
9
o4-mini
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
53.2
10
GPT-4.1 Mini
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
50.5
11
Kimi K2.5
Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 19, 2026Confidence: Not recordedSource
47.1
12
Mistral Large 2
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
38.4
13
Llama 3.3 70B Instruct
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
31.9
14
Llama 3.1 8B Instruct
Version: v4Harness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
25.8
15
Llama 3.2 3B Instruct
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
21.9
16
Ministral 8B
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
11.1
17
Llama 3.2 1B Instruct
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: Apr 14, 2026Confidence: Not recordedSource
10.8

How to read this benchmark

This benchmark scores models where higher is better. Scores are useful for directional filtering and shortlisting — not for universal quality ranking. Prefer benchmarks closest to your workload, then validate the linked model pages for pricing, context window, and provider availability.

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.

FAQ

What does the BFCL benchmark measure?

Berkeley Function Calling Leaderboard (BFCL) evaluating LLM ability to correctly call functions/APIs with proper arguments across diverse domains. Currently on v3. On this page it lists 17 tracked model variants where higher is better.

Is a higher BFCL score always better?

For this benchmark, higher is better. A high score helps you shortlist, but confirm pricing, context window, and provider availability on each model page before committing — the top scorer is not always the right pick for your workload or budget.

How current is this BFCL data?

This benchmark was last reviewed on Apr 15, 2026. The tracked score average moved +21.29 points across the last 2 snapshots.

Related benchmarks

Last reviewed: Apr 15, 2026

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