[whatmodel.sh](../../README.md) / MiniCPM3-4B
# MiniCPM3-4B
OpenBMB · released 2024-09 · minicpm-model-license
**score 48.7** ±2.0 · #395 of 732 · from 3 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks MiniCPM3-4B among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #271 of 313* 26% 2
coding ▁▁▁▃▅●█▇▅▄▃▁ #298 of 469* 72% 1
agent tasks · #271 of 313 · 26% of its score
Beats [DeepSeek R1 0528 Qwen3 8B](deepseek-r1-0528-qwen3-8b.md), [Magistral Small 1.0 (2506)](magistral-small-2506.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank higher overall. Trails [Granite 4.0 H 1B](granite-4-0-h-1b.md), [Granite 4.0 1B](granite-4-0-1b.md), [SmolLM3 3B](smollm3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) 25.6% ▁▁▁▂●█▅▅▆▂▂▂ beats 15/77 hard · avg 31% 17% at its level
BFCL v3 50.3% ▁▁▁▁▁▃●█▅▄▁▁ beats 34/114 medium · avg 51% 9.5% blunt · at its level
coding · #298 of 469 · 72% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Yi 1.5 9B Chat](yi-1-5-9b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench official (Jan 1–Nov… 21.5% ▁▁·▁▂●▆█▅▂·▂ beats 6/31 hard · avg 23% 72% sharp
source results share of the score
LiveCodeBench official website results 1 72% ▇▇▇▇▇▇▇▇▇
Berkeley Function Calling Leaderboard r… 2 26% ▇▇▇
prior: pull toward the middle 2% ▇
weighted average = 48.7
Licences: LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run MiniCPM3-4B (Q4_K_M, needs ~2.47 GB, from [huggingface.co/openbmb](https://huggingface.co/openbmb/MiniCPM3-4B-GGUF/tree/816dc79b35f92827e0d2d87aacea3567e49661a8))
```sh
llama cli -hf openbmb/MiniCPM3-4B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf openbmb/MiniCPM3-4B-GGUF:Q4_K_M
```
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run MiniCPM3-4B (Q4_K_M, needs ~2.47 GB, from [huggingface.co/openbmb](https://huggingface.co/openbmb/MiniCPM3-4B-GGUF/tree/816dc79b35f92827e0d2d87aacea3567e49661a8))
```sh
lms get https://huggingface.co/openbmb/MiniCPM3-4B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: 4bit (~2.4 GB, Mac only) `lms get https://huggingface.co/mlx-community/MiniCPM3-4B-4bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run MiniCPM3-4B (Q4_K_M, needs ~2.47 GB, from [huggingface.co/openbmb](https://huggingface.co/openbmb/MiniCPM3-4B-GGUF/tree/816dc79b35f92827e0d2d87aacea3567e49661a8))
```sh
ollama run hf.co/openbmb/MiniCPM3-4B-GGUF:Q4_K_M
```
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run MiniCPM3-4B (4bit, needs ~2.4 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/MiniCPM3-4B-4bit/tree/7a5f80ff3161be21f0a90e9be7137495633b2b99))
```sh
mlx_lm.chat --model mlx-community/MiniCPM3-4B-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/MiniCPM3-4B-4bit
```
Model repository and documentation: [huggingface.co/openbmb](https://huggingface.co/openbmb/MiniCPM3-4B)
[interactive version](https://whatmodel.sh/model/minicpm3-4b) · [JSON](https://whatmodel.sh/api?format=json&all=1)