MiniCPM5-1B

MiniCPM5-1B is a 1.08 billion parameter dense language model from OpenBMB, released in May 2026 under the Apache 2.0 license. It is positioned as the state-of-the-art (SOTA) model within the 1B-class open-weight category, with its primary strengths in agentic tool use, code generation, and competitive mathematics. Its core innovation lies not in raw size, but in its post-training methodology: a three-stage pipeline of Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and On-Policy Distillation (OPD). This RL+OPD process trains specialized domain teachers (for math, code, QA) and distills them into a single model, yielding a +16-point average score improvement over the SFT-only baseline while drastically reducing overlong, inefficient responses.

The model is architected for efficiency and on-device deployment. It uses the standard LlamaForCausalLM architecture, ensuring compatibility with all major inference engines (vLLM, SGLang, llama.cpp, Ollama, etc.). A key feature is its hybrid reasoning capability, toggled via a `` template and enable_thinking parameter, allowing a single checkpoint to function as both a fast conversational assistant and a deliberate reasoner. With a 128K context window and a footprint of under 1GB at Q4 quantization, it is explicitly designed for local assistants, coding agents, tool-use workflows, and resource-constrained edge scenarios where latency and token efficiency are critical. Benchmarks show it significantly outperforms models like Qwen3-0.6B and LFM2.5-1.2B in areas like AIME mathematics and τ2-Bench agentic tasks.