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MiniCPM5-2B dense model release
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OpenBMB released MiniCPM5-2B, a 2.52 billion parameter dense language model that achieves an average benchmark score of 53.9 across 34 tests, outperforming Qwen3.5-4B (51.1) and positioning itself as the leading model in its size class. The model features a 131,072 token context window, was trained using 400B tokens of deep-thinking supervised fine-tuning paired with reinforcement learning, and is released under Apache 2.0 with full training datasets and intermediate checkpoints.
- 2.52B parameters with 131,072 token context window; averages 53.9 on 34-benchmark model card
- Outperforms Qwen3.5-4B (51.1); strongest in tool use, coding agents, long-context retrieval
- Post-training combines 400B tokens deep-thinking SFT with RL teachers and on-policy distillation merging 16 expert models
- Apache 2.0 license includes pre-training, SFT, RL datasets and Base, Midtrain, SFT-only checkpoints
- GGUF quantized build available for on-device deployment