Agentic & Tool Use benchmark

# Berkeley Function Calling Leaderboard leaderboard

> Berkeley Function Calling Leaderboard results for 49 AI models, led by Claude Opus 4.5 at 77.5%. What the benchmark measures, who runs it, and a source for every score.
- Canonical page: https://noometry.com/benchmarks/bfcl
- Last updated: 2026-10-10
- Title: Berkeley Function Calling Leaderboard Leaderboard (October 2026): Scores by Model

As of October 2026, Claude Opus 4.5 has the highest published Berkeley Function Calling Leaderboard score on Noometry at 77.5%, out of 49 models with results.

Last verified October 10, 2026

## About Berkeley Function Calling Leaderboard

Tool-calling accuracy across single, parallel and multi-turn function calls, web search, memory and knowing when not to call a tool (BFCL v4).

- **Category:** [Agentic & Tool Use](https://noometry.com/best/agentic)
- **Introduced:** 2024
- **Format:** Function calls
- **Unit:** Percent (random guessing ≈ 0%)
- **Official site:** [gorilla.cs.berkeley.edu](https://gorilla.cs.berkeley.edu/leaderboard.html)

## Top 15 models

Top models on Berkeley Function Calling Leaderboard

1.  Claude Opus 4.5 77.5%
2.  Claude Sonnet 4.5 73.2%
3.  Gemini 3 Pro 72.5%
4.  GLM-4.6 72.4%
5.  Grok 4.1 Fast 69.6%
6.  Claude Haiku 4.5 68.7%
7.  o3 63%
8.  Grok 4 63%
9.  Kimi K2 (Jul 2025) 59.1%
10.  Command A 57.1%
11.  DeepSeek-V3.2-Exp 56.7%
12.  Gemini 2.5 Flash 56.2%
13.  GPT-5.2 55.9%
14.  GPT-5 Mini 55.5%
15.  GPT-4.1 54%
16.  4050607080

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## All results

Berkeley Function Calling Leaderboard results by model
| # | Model | Provider | Score | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | [Claude Opus 4.5](https://noometry.com/models/claude-opus-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 77.5% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 2 | [Claude Sonnet 4.5](https://noometry.com/models/claude-sonnet-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 73.2% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 3 | [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 72.5% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 4 | [GLM-4.6](https://noometry.com/models/glm-4-6) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 72.4% | fc thinking | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 5 | [Grok 4.1 Fast](https://noometry.com/models/grok-4-1-fast) | [xAI](https://noometry.com/providers/xai) | 69.6% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 6 | [Claude Haiku 4.5](https://noometry.com/models/claude-haiku-4-5) | [Anthropic](https://noometry.com/providers/anthropic) | 68.7% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 7 | [o3](https://noometry.com/models/o3) | [OpenAI](https://noometry.com/providers/openai) | 63% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 8 | [Grok 4](https://noometry.com/models/grok-4) | [xAI](https://noometry.com/providers/xai) | 63% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 9 | [Kimi K2 (Jul 2025)](https://noometry.com/models/kimi-k2) | [Moonshot AI](https://noometry.com/providers/moonshot) | 59.1% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 10 | [Command A](https://noometry.com/models/command-a) |  [![](/logos/cohere.svg) Cohere](https://noometry.com/providers/cohere) | 57.1% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 11 | [DeepSeek-V3.2-Exp](https://noometry.com/models/deepseek-v3-2-exp) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 56.7% | prompt + thinking | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 12 | [Gemini 2.5 Flash](https://noometry.com/models/gemini-2-5-flash) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 56.2% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 13 | [GPT-5.2](https://noometry.com/models/gpt-5-2) | [OpenAI](https://noometry.com/providers/openai) | 55.9% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 14 | [GPT-5 Mini](https://noometry.com/models/gpt-5-mini) | [OpenAI](https://noometry.com/providers/openai) | 55.5% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 15 | [GPT-4.1](https://noometry.com/models/gpt-4-1) | [OpenAI](https://noometry.com/providers/openai) | 54% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 16 | [o4-mini](https://noometry.com/models/o4-mini) | [OpenAI](https://noometry.com/providers/openai) | 53.2% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 17 | [Qwen3 235B-A22B](https://noometry.com/models/qwen3-235b-a22b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 52.1% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 18 | [GPT-5 Nano](https://noometry.com/models/gpt-5-nano) | [OpenAI](https://noometry.com/providers/openai) | 51.5% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 19 | [GPT-4.1 mini](https://noometry.com/models/gpt-4-1-mini) | [OpenAI](https://noometry.com/providers/openai) | 50.5% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 20 | [Qwen3 32B](https://noometry.com/models/qwen3-32b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 48.7% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 21 | [Qwen3 8B](https://noometry.com/models/qwen3-8b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 42.6% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 22 | [Qwen3-30B-A3B](https://noometry.com/models/qwen3-30b-a3b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 41.4% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 23 | [Qwen3 14B](https://noometry.com/models/qwen3-14b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 41% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 24 | [Mistral Large](https://noometry.com/models/mistral-large) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 38.4% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 25 | [Mistral Medium](https://noometry.com/models/mistral-medium) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 37.7% |  | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 26 | [Llama 4 Maverick](https://noometry.com/models/llama-4-maverick) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 37.3% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 27 | [Mistral Small](https://noometry.com/models/mistral-small) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 37.1% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 28 | [Gemini 2.5 Flash-Lite](https://noometry.com/models/gemini-2-5-flash-lite) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 36.9% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 29 | [Qwen3-4B](https://noometry.com/models/qwen3-4b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 35.7% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 30 | [GPT-4.1 nano](https://noometry.com/models/gpt-4-1-nano) | [OpenAI](https://noometry.com/providers/openai) | 33% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 31 | [Command R7B](https://noometry.com/models/command-r7b) |  [![](/logos/cohere.svg) Cohere](https://noometry.com/providers/cohere) | 32.1% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 32 | [Llama-3.3-70B-Instruct](https://noometry.com/models/llama-3-3-70b-instruct) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 31.9% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 33 | [Gemma 3 12B](https://noometry.com/models/gemma-3-12b) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 30.4% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 34 | [Gemma 3 27B](https://noometry.com/models/gemma-3-27b) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 29.5% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 35 | [Phi-4](https://noometry.com/models/phi-4) |  [![](/logos/microsoft.svg) Microsoft](https://noometry.com/providers/microsoft) | 28.8% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 36 | [Qwen3-1.7B](https://noometry.com/models/qwen3-1-7b) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 28.4% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 37 | [Llama 4 Scout](https://noometry.com/models/llama-4-scout) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 28.1% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 38 | [Mistral Nemo](https://noometry.com/models/mistral-nemo) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 27.6% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 39 | [Granite 3.1 8b Instruct](https://noometry.com/models/granite-3-1-8b-instruct) | [IBM](https://noometry.com/providers/ibm) | 27.1% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 40 | [Nova 2 Lite](https://noometry.com/models/nova-2-lite) | [Amazon](https://noometry.com/providers/amazon) | 27.1% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 41 | [Llama 3.1-8B](https://noometry.com/models/llama-3-1-8b) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 25.8% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 42 | [Amazon Nova Pro](https://noometry.com/models/amazon-nova-pro) | [Amazon](https://noometry.com/providers/amazon) | 25% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 43 | [Amazon Nova Micro](https://noometry.com/models/amazon-nova-micro) | [Amazon](https://noometry.com/providers/amazon) | 22.3% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 44 | [Llama 3.2 3B](https://noometry.com/models/llama-3-2-3b) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 21.9% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 45 | [Gemma 3 4B](https://noometry.com/models/gemma-3-4b) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 19.6% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 46 | [Ministral 8B](https://noometry.com/models/ministral-8b) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 11.1% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 47 | [Llama 3.2 1B](https://noometry.com/models/llama-3-2-1b) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 10.8% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 48 | [Llama 3.1 Nemotron Ultra 253b v1](https://noometry.com/models/llama-3-1-nemotron-ultra-253b-v1) |  [![](/logos/nvidia.svg) NVIDIA](https://noometry.com/providers/nvidia) | 10% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| 49 | [Gemma 3 1B](https://noometry.com/models/gemma-3-1b) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 7.2% | prompt | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |

## Compare the leaders

-   [Claude Opus 4.5 vs Claude Sonnet 4.5](https://noometry.com/compare/claude-opus-4-5-vs-claude-sonnet-4-5)
-   [Claude Opus 4.5 vs Gemini 3 Pro](https://noometry.com/compare/claude-opus-4-5-vs-gemini-3-pro)
-   [Claude Opus 4.5 vs GLM-4.6](https://noometry.com/compare/claude-opus-4-5-vs-glm-4-6)
-   [Claude Opus 4.5 vs Grok 4.1 Fast](https://noometry.com/compare/claude-opus-4-5-vs-grok-4-1-fast)
-   [Claude Sonnet 4.5 vs Gemini 3 Pro](https://noometry.com/compare/claude-sonnet-4-5-vs-gemini-3-pro)
-   [Claude Sonnet 4.5 vs GLM-4.6](https://noometry.com/compare/claude-sonnet-4-5-vs-glm-4-6)

## Other agentic & tool use benchmarks

-   [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench)
-   [APEX-Agents](https://noometry.com/benchmarks/apex-agents)
-   [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2)
-   [GDPval](https://noometry.com/benchmarks/gdpval)
-   [Remote Labor Index](https://noometry.com/benchmarks/remote-labor-index)
-   [TheAgentCompany](https://noometry.com/benchmarks/the-agent-company)
-   [τ²-bench Airline](https://noometry.com/benchmarks/tau2-airline)
-   [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking)
-   [τ²-bench Retail](https://noometry.com/benchmarks/tau2-retail)
-   [τ²-bench Telecom](https://noometry.com/benchmarks/tau2-telecom)
-   [Cybench](https://noometry.com/benchmarks/cybench)
-   [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench)

## Frequently asked questions

### What does Berkeley Function Calling Leaderboard measure?

Tool-calling accuracy across single, parallel and multi-turn function calls, web search, memory and knowing when not to call a tool (BFCL v4).

### Which model has the highest Berkeley Function Calling Leaderboard score?

As of October 2026, Claude Opus 4.5 has the highest published Berkeley Function Calling Leaderboard score on Noometry at 77.5%, out of 49 models with results.

### What is the best open-weight model on Berkeley Function Calling Leaderboard?

GLM-4.6 has the highest Berkeley Function Calling Leaderboard accuracy among open-weight models at 72.4%, ranking 4 of 49 overall.

### Cite this page

Noometry. (2026). Berkeley Function Calling Leaderboard leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/bfcl

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/bfcl.md).