MiniCPM5-2B: pricing, features, alternatives

MiniCPM5-2B is OpenBMB's second MiniCPM5 dense model, built for on-device assistants, local coding agents, and tool-use jobs that cannot spend a frontier API. The Hugging Face card lists a standard LlamaForCausalLM stack: about 2.52B parameters, 42 layers, GQA with 16 query heads and 2 KV heads, and a 131,072 token context. License is Apache-2.0. r/LocalLLaMA flagged the release on 2026-09-06. Hugging Face showed 236 likes when we checked on 2026-09-08. The GitHub repo OpenBMB/MiniCPM had about 10,360 stars.

Compare Qwen3.8-27B if you wanted a larger dense local chat model, GLM-5.3-Flash if you wanted a cheap hosted Chinese fast SKU, or MiniCPM5 Skills if you wanted the deploy and finetune SKILL.md pack.

Model Specifications

Spec	MiniCPM5-2B
Type	Causal LM, dense Transformer
Parameters	2,516,756,480 (1,981,982,720 non-embedding)
Layers / GQA	42 layers; 16 Q heads, 2 KV heads
Context	131,072 tokens. No RoPE scaling.
License	Apache-2.0
Card date	Hugging Face created 2026-09-06

Limitation: this is a 2B-class on-device model. It is not a 27B dense substitute and not a hosted frontier SKU. OpenBMB's own radar still trails some 4B models on isolated coding-agent rows such as SWE-bench Pro.

Key Features

2B-class open-source SOTA on the card's set: OpenBMB reports an average 53.9 versus LFM2.5-2.6B 33.2, Qwen3.5-2B 28.0, and Gemma-4-E2B-it 24.6. Treat those as vendor numbers, not an audit.
Long context without extra scaling: 128k native. vLLM needs --max-model-len 131072 if you want the full window.
Tool calling: XML-style calls. SGLang is the recommended backend with --tool-call-parser minicpm5.
Open UltraData: UltraX, UltraData-Code, UltraData-SFT-Agent-2609 (500K agent samples), UltraData-RL-2609.
Formats: BF16, GGUF, MLX 4bit, GPTQ, plus a DSpark draft model for speculative decoding.

Use Cases

Laptop and edge boxes that need a local assistant with tool use, not a cluster.
Coding agents that must stay on-device and can live with 2B-class SWE scores.
Teams already on vLLM, SGLang, Ollama, or llama.cpp who want a Llama-architecture checkpoint with no custom kernels.

Pricing

Weights are free. You pay for your GPU, NPU, or CPU.

Piece	Price	Source 2026-09-08
MiniCPM5-2B weights	$0	Apache-2.0 on Hugging Face
Hosted API	Not on the card	No Inference Provider listed

Getting Started

Open huggingface.co/openbmb/MiniCPM5-2B.
Serve with pip install "vllm>=0.21" then vllm serve openbmb/MiniCPM5-2B --port 8000, or SGLang >=0.5.16.
Sampling: temperature=1.0, top_p=0.95. Think mode is the default for 2B.
Load MiniCPM5 Skills if the agent should pick a backend instead of you.

First-party resource: MiniCPM5-2B model card.

Frequently Asked Questions

Is this MiniCPM4?

No. This is MiniCPM5-2B, the second MiniCPM5 dense release after MiniCPM5-1B.

Can I run it in Ollama?

Yes. Use the GGUF repo openbmb/MiniCPM5-2B-GGUF and the Ollama cookbook in the GitHub skills folder.

Does it beat Qwen3.8-27B?

Different size class. MiniCPM5-2B is an on-device 2B. Qwen3.8-27B is a 27B dense local model.

Alternatives

Qwen3.8-27B: larger dense local chat.
GLM-5.3-Flash: hosted fast coding SKU.
DeepSeek V4 Flash: MIT MoE agent model, not on-device 2B.

Tips

Quote 2.52B params, 131k context, Apache-2.0, and 236 HF likes from pages checked 2026-09-08.
Do not skip --tool-call-parser minicpm5 on SGLang if you need tools.
Untied lm_head: old MLX converters that assume Llama tied embeddings will drop the head.

Conclusion

MiniCPM5-2B is the current OpenBMB on-device dense 2B: 131k context, Apache-2.0, and a real Llama checkpoint you can serve without a fork. Start at the Hugging Face card, then decide whether MiniCPM5 Skills already covers the install path.