BFCL
Berkeley Function Calling Leaderboard (BFCL) evaluating LLM ability to correctly call functions/APIs with proper arguments across diverse domains. Currently on v3.
Models ranked
21
tracked on this benchmark
Score band
77.5 – 10.8
best → lowest tracked
Snapshot trend
-7.94
Apr 19 → Oct 7 · 3 models
Leaderboard
Tracked models ranked by Function Calling Accuracy (higher is better).
Notes: Aleph Alpha first-party HF model card Aleph-Alpha/Kolibri-1 (FP8 release checkpoint), Evaluation > Post-training table; same value in launch blog/landing page. Harness: Aleph-Alpha-Research eval-framework (Harbor for TerminalBench/SWE-Bench), Kolibri at reasoning effort high, each model at its documented context window. BF16 repo card reports 60.4. BFCL v3 multi-turn 39.8 not seeded. Self-reported by the lab.
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