OpenBMB Releases MiniCPM5-2B: A 2.52B Dense Model Averaging 53.9 Across 34 Benchmarks and Built to Run On Device
2.52B parameters, 53.9 average benchmark score, runs on-device. OpenBMB's MiniCPM5-2B just moved the efficiency bar.
Why it matters
A dense, open-weight 2B model that beats larger competitors (Qwen3.5-4B at 51.1) across 34 benchmarks with native long context and tool-use capability. Practitioners can now deploy a capable model locally without quantization or architectural forking — a material shift in edge-AI feasibility.
The key facts
8 to know2,516,756,480 parameters (2.52B dense)
53.9 average across 34 benchmarks vs Qwen3.5-4B at 51.1
131,072 token native context window
400B tokens deep-thinking SFT + RL with 16-expert distillation
GGUF quantization starts at 1.56 GB
Loads in vLLM, SGLang, llama.cpp, Ollama, MLX without model-code fork
Apache 2.0 license; weights + pre-training, SFT, RL datasets + intermediate checkpoints released
Clear leads in tool use, coding agents, long-context retrieval
Go to the source
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Publisher excerpt: OpenBMB has released MiniCPM5-2B, a dense causal language model with 2,516,756,480 parameters and a native 131,072 token context. It averages 53.9 across the 34 benchmarks in its model card, ahead of Qwen3.5-4B at 51.1, with its clearest leads in tool use, coding agents and long-context retrieval.…