OpenBMB Releases MiniCPM5-2B: A 2.52B Dense Model Averaging 53.9 Across 34 Benchmarks and Built to Run On Device
OpenBMB released MiniCPM5-2B, a 2.52B-parameter Apache 2.0 on-device model averaging 53.9 across 34 benchmarks, ahead of Qwen3.5-4B.
OpenBMB released MiniCPM5-2B, a 2,516,756,480-parameter dense LlamaForCausalLM model with grouped-query attention and a 131,072-token context, under Apache 2.0, runnable via vLLM, SGLang, llama.cpp, and Ollama. It averages 53.9 across 34 benchmarks versus 51.1 for Qwen3.5-4B, with strong tool-use (97.1 on tau2-Bench Telecom) and code results (69.1 LiveCodeBench v6, 46.4 SWE-bench Verified). Training combined 400B tokens of deep-thinking SFT, critic-based JustRL II RL teachers, and on-policy distillation merging 16 RL experts; datasets and intermediate checkpoints were published alongside the weights.
- 2.52B dense model, 131K context, Apache 2.0, standard Llama architecture
- Averages 53.9 across 34 benchmarks versus 51.1 for Qwen3.5-4B
- Strongest on tool use and coding agents; weaker on MMLU-Pro knowledge
- Ships with open SFT/RL datasets and intermediate checkpoints for verifiability
- Organizations
- OpenBMB
- AI models
- MiniCPM5-2BQwen3.5-4B
From the original article
3 min read at marktechpost.com
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.
By Sana Hassan · marktechpost.com
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