---
title: "OpenBMB's MiniCPM5-1B Beats Every Open 1B Model by 7 Points"
subtitle: "OpenBMB's MiniCPM5-1B packs a 128K context window, hybrid reasoning, and native tool-calling into a 1B model that runs entirely on-device"
url: "https://alphasignal.ai/news/openbmb-s-minicpm5-1b-beats-every-open-1b-model-by-7-points"
author: "AlphaSignal Newsroom"
author_url: "https://alphasignal.ai/editorial-team"
company: "AlphaSignal"
company_url: "https://alphasignal.ai/company/alphasignal"
published: "2026-05-24T08:45:01.000Z"
updated: "2026-06-09T02:15:08.910Z"
type: "Model"
topics:
  - "Llms"
  - "Open Source"
subtopics:
  - "Small Models"
  - "Long Context"
upvotes: 2315
source_url: "https://huggingface.co/openbmb/MiniCPM5-1B-MLX"
comments: 0
truncated: true
---

# OpenBMB's MiniCPM5-1B Beats Every Open 1B Model by 7 Points

> OpenBMB's MiniCPM5-1B packs a 128K context window, hybrid reasoning, and native tool-calling into a 1B model that runs entirely on-device

[MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B), the first release in OpenBMB's MiniCPM5 series, is a 1.08-billion-parameter language model built from the ground up for on-device deployment. It runs on phones, laptops, and Apple Silicon, fits in roughly 0.5GB at 4-bit quantization, and requires no cloud API. For a model this small, the benchmark numbers are genuinely surprising.

MiniCPM5-1B reaches an average score of 42.57 across reasoning, knowledge, code, instruction-following, math, logic, and agentic benchmarks, above the highest average score of 35.61 among strong open-source models in the same size class. Its nearest competitors are Alibaba's Qwen3-0.6B and Qwen3.5-0.8B, and Liquid AI's LFM2.5-1.2B-Thinking. Independent analysis from Artificial Analysis confirms the lead: MiniCPM5-1B scores 17.9 on the Artificial Analysis Intelligence Index, the highest of any open-weights model at 1B parameters or below by 7.4 points. It surpasses its predecessor MiniCPM-V 4.6 1.3B (12.7) by 5.3 points at \~23% fewer parameters, and beats Qwen3.5 2B (Reasoning, 16.3) by 1.6 points at less than half the parameter count.

![Radar chart comparing MiniCPM5-1B capabilities across seven domains against peer models](https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm5/public_leaderboard_radar_en.png) 

## One checkpoint, two modes

MiniCPM5-1B has a built-in `<think>` chat template, switchable via `enable_thinking`. The same checkpoint serves as both a fast assistant and a deliberate reasoner. This is more than a cosmetic toggle. In thinking mode, the model works through problems step by step before answering; in no-think mode, it responds immediately. The recommended sampling parameters differ between the two modes:

* **Think mode:** `temperature=0.9, top_p=0.95` with `enable_thinking=True`
* **No Think mode:** `temperature=0.7, top_p=0.95` with `enable_thinking=False`

The model has 1,080,632,832 total parameters, 679,552,512 non-embedding parameters, 24 layers, grouped-query attention (GQA) with 16 query heads and 2 key-value heads, and a context length of 131,072 tokens. That 128K context window is a meaningful edge for an on-device model. The 128K-token context equals roughly 96,000 words of continuous text, which OpenBMB says enables long roleplay sessions, full-document digests, and agent contexts that persist across many exchanges.

_Preview only. Full text requires an AlphaSignal Pro account._

## Sources

- [MiniCPM5-1B-MLX · Hugging Face (official model card)](https://huggingface.co/openbmb/MiniCPM5-1B-MLX)
- [OpenBMB/MiniCPM GitHub Repository](https://github.com/openbmb/minicpm)
- [Artificial Analysis: MiniCPM5-1B — The leading 1B open weights model](https://artificialanalysis.ai/articles/minicpm5-1b-the-leading-1b-open-weights-model)
- [MiniCPM5-1B Brings 128K On-Device Context to Phones](https://www.gncrypto.news/news/minicpm5-1b-128k-on-device-context-phones/)
- [What Is MiniCPM5-1B? A Practical Guide — Kingy AI](https://kingy.ai/news/what-is-minicpm5-1b-a-practical-guide-to-openbmbs-1b-on-device-language-model/)