Why this benchmark is useful
Tool calling is the plumbing behind agents, automations, and research assistants. BFCL gives that plumbing a public test instead of treating every function-call demo as evidence.
Benchmarks / Tool use
Berkeley Function Calling Leaderboard
BFCL
Whether a model can choose and format function calls correctly across single-turn, multi-turn, parallel, and live API-style tool-use tasks.
Tool useSolidActiveMedium contamination riskSince 2024
What this does not measure
- Whether the downstream tool result is useful — BFCL focuses on choosing and formatting calls, not on the whole product workflow after the call returns.
- Security of tool execution — permission boundaries, prompt-injection resistance, and audit logs still need product-specific tests.
- Long-horizon agent reliability — passing function-call tasks does not prove the system can recover over dozens of steps.
Coverage map
- Task family
- Tool use
- Format
- Function/tool-calling tasks with schema-constrained expected outputs across simple, multi-turn, parallel, and live-style settings.
- Scoring
- Accuracy-style score by BFCL category and version. The exact category matters more than the aggregate.
- Maintainer
- Berkeley Gorilla
How to read the scores
Compare models within the same BFCL version and category. A model can be strong at simple calls and weak at parallel or multi-turn calls.
What it does not cover
- Whether the downstream tool result is useful — BFCL focuses on choosing and formatting calls, not on the whole product workflow after the call returns.
- Security of tool execution — permission boundaries, prompt-injection resistance, and audit logs still need product-specific tests.
- Long-horizon agent reliability — passing function-call tasks does not prove the system can recover over dozens of steps.
Scores
20 rowsEvidence ledger
2020 rows
88.7%best score
0source-checked
1sources
2026-04-15source date
Where the rows land
0255075100
Normalized to this benchmark's axis (0–100). Open the table for raw units.
Newest receipts
- Claude Sonnet 4.688.7% · Berkeley Function Calling Leaderboard V4
- GPT 5.587.4% · Berkeley Function Calling Leaderboard V4
- Claude Opus 4.886.9% · Berkeley Function Calling Leaderboard V4
- Kimi K2.685.2% · Berkeley Function Calling Leaderboard V4
- GPT 5.483.8% · Berkeley Function Calling Leaderboard V4
Scores use this benchmark's own unit and axis, not a universal quality score.
| # | Model | Release date | Score | Provenance | Trust |
|---|---|---|---|---|---|
| 1 | Claude Sonnet 4.6AnthropicV4 aggregate score; drill into multi-turn and parallel sub-scores before choosing. | 2026-02-17 | 88.7% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 2 | GPT 5.5OpenAI | 2026-04-23 | 87.4% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 3 | Claude Opus 4.8Anthropic | 2026-05-28 | 86.9% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 4 | Kimi K2.6Moonshot | 2026-04-20 | 85.2% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 5 | GPT 5.4OpenAI | 2026-03-05 | 83.8% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 6 | Gemini 3.5 FlashGoogle | 2026-05-19 | 82.3% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 7 | Gemini 3.1 ProGoogle | 2026-02-19 | 81.6% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 8 | Mistral Large 3Mistral | 2025-12-02 | 80.1% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 9 | Gemini 3 Flash PreviewGoogle | 2025-12-17 | 79.4% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 10 | Qwen 3.7 MaxAlibaba | 2026-05-20 | 78.4% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 11 | o3OpenAIStrong in simple single-turn; drops in multi-turn complexity. | 2025-04-16 | 78.1% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 12 | Grok 4.3xAI | 2026-04-30 | 77.2% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 13 | DeepSeek V4 Flash MaxDeepSeek | 2026-04-24 | 76.8% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 14 | MiniMax M3MiniMax | 2026-05-31 | 76.2% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 15 | Llama 4 MaverickMeta | 2026-04-05 | 75.9% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 16 | DeepSeek V4DeepSeek | 2026-04-24 | 74.6% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 17 | Llama 4 ScoutMeta | 2026-04-05 | 74.1% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 18 | DeepSeek R1DeepSeek | 2025-01-20 | 72.4% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 19 | Claude Haiku 4.5Anthropic | 2025-10-01 | 71.8% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
| 20 | Cohere Command ACohere | 2026-03-15 | 70.6% | Berkeley Function Calling Leaderboard V42026-04-15 | Needs audit |
What it covers
Primary source is the Berkeley Gorilla leaderboard. VerdictPal should store category and BFCL version with every score row.
Where it breaks down
The leaderboard does not replace a product-level integration test with your schemas, auth model, error handling, and adversarial inputs.
Tools that report it
Receipts
Sources and further reading
- Berkeley Function Calling Leaderboard V4· checked 2026-06-03
- Gorilla repository· checked 2026-06-03
- BFCL leaderboard
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