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Highlights#
We are releasing MiniCPM5-2B, the second model in the MiniCPM5 series, following MiniCPM5-1B. It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios, reaching 2B-class open-source SOTA.
🏆 2B-class open-source SOTA: compared with strong open-source models of similar size, MiniCPM5-2B achieves SOTA performance within this comparison set. It remains competitive with 4B-class models overall, while showing its advantages over models of comparable size in coding, mathematics, long-context understanding, tool use, and agentic tasks.
📂 Open High-Quality Data: Alongside the model, we are releasing the high-quality training datasets behind it as part of the UltraData family: UltraX, a high-quality web pre-training dataset; UltraData-Code, featuring L0–L3 tiered code data management to drive a significant leap in coding capabilities; UltraData-SFT-Agent-2609, comprising 500K agent training samples to enhance comprehensive on-device agent capabilities; and UltraData-RL-2609, with 80K+ high-quality RL training samples covering mathematics, code, general knowledge, and long-context reasoning.
Model List#
Use this directory to choose the model format that matches your runtime:
MiniCPM5-2B
MiniCPM5-1B
MiniCPM5-2B has the following features:
- Type: Causal Language Model
- Architecture: Standard
LlamaForCausalLM - Number of Parameters: 2,516,756,480
- Number of Non-Embedding Parameters: 1,981,982,720
- Number of Layers: 42
- Number of Attention Heads (GQA): 16 for Q and 2 for KV
- Context Length: 131,072
Introduction#
MiniCPM5-2B is the second model in the MiniCPM5 series. It is designed for local assistants, coding agents, tool-use workflows, and reasoning scenarios where a compact model is preferred. The model keeps a small deployment footprint while providing native long-context support.
Evaluation Results#
We compare MiniCPM5-2B with strong open-source models in the same size class, including LFM2.5-2.6B, Qwen3.5-2B, and Gemma-4-E2B-it, while also listing larger models such as Qwen3.5-4B, granite-4.2-3B, Nemotron-3-Nano-4B, Gemma-4-E4B-it, and LFM2.5-8B-A1B for reference.
Within this comparison set, MiniCPM5-2B reaches 2B-class open-source SOTA with an average score of 53.9, and also exceeds all of the larger models included here (the highest is 51.1). Its advantages are most visible in code reasoning, math reasoning, long-context understanding, tool use, and multiple agentic tasks.
Evaluation Results of MiniCPM5-2B and Baselines#
| | MiniCPM5-2B | 2B-class Models | 4B-class Models
| LFM2.5-2.6B | Qwen3.5-2B | Gemma-4-E2B-it | Qwen3.5-4B | granite-4.2-3B | Nemotron-3-Nano-4B | Gemma-4-E4B-it | LFM2.5-8B-A1B
| Average
| 53.9 | 33.2 | 28.0 | 24.6 | 51.1 | 42.7 | 32.6 | 31.2 | 28.4
| Code Reasoning
| LiveCodeBench v6
| 69.1 | 42.1 | 20.2 | 42.9 | 56.4 | 58.9 | 50.7 | 53.9 | 39.8
| LCB-Pro 25Q2 (Easy)
| 68.0 | 30.9 | 10.3 | 27.1 | 58.3 | 54.6 | 51.6 | 45.8 | 27.8
| LCB-Pro 25Q2 (Medium)
| 17.5 | 0.0 | 0.0 | 0.0 | 7.0 | 5.3 | 5.3 | 1.8 | 0.0
| OJBench
| 32.5 | 11.2 | 2.6 | 11.6 | 24.8 | 21.8 | 20.0 | 19.0 | 8.2
| SciCode (wbg)
| 26.3† | 14.2† | 2.8† | 20.9† | 16.1† | 24.9† | 16.4† | 24.4† | 7.8†
| Math Reasoning
| AIME 2025
| 86.5 | 41.9 | 29.6 | 31.7 | 78.8 | 79.4 | 56.3 | 37.1 | 46.0
| AIME 2026
| 86.5 | 45.2 | 29.0 | 39.8 | 82.7 | 83.5 | 62.1 | 45.0 | 56.7
| HMMT Feb 2026
| 63.8 | 33.7 | 20.5 | 17.8 | 64.0 | 60.8 | 51.3 | 30.1 | 38.5
| MATH-500
| 94.6 | 89.6 | 85.8 | 85.4 | 99.0 | 97.0 | 91.6 | 88.2 | 93.2
| Instruction Following
| IFBench
| 66.3 | 59.0 | 46.0 | 25.7 | 59.0 | 73.0 | 58.3 | 28.3 | 51.0
| IFEval
| 86.7 | 93.4 | 77.5 | 31.4 | 90.2 | 93.7 | 88.0 | 44.4 | 90.8
| Multi-IF
| 71.8 | 76.8 | 57.1 | 40.3 | 73.6 | 75.9 | 65.9 | 45.9 | 71.4
| General Knowledge
| MMLU-Pro
| 70.8 | 65.2 | 64.3 | 56.0 | 78.0 | 65.8 | 65.7 | 68.3 | 63.1
| MMLU-Redux
| 84.7 | 80.0 | 80.0 | 71.8 | 88.7 | 78.9 | 79.8 | 83.7 | 80.0
| HLE
| 8.9† | 6.2† | 2.6† | 4.8† | 9.9† | 6.6† | 4.9† | 3.8† | 6.9†
| GPQA-Diamond
| 70.2† | 55.8† | 45.6† | 43.3† | 77.1† | 55.9† | 51.3† | 57.6† | 51.3†
| SuperGPQA
| 40.8 | 26.2 | 38.6 | 30.3 | 52.8 | 39.9 | 37.8 | 38.7 | 34.5
| Long Context
| AA-LCR
| 59.0† | 5.3† | 28.7† | 17.0† | 61.0† | 24.3† | 17.3† | 33.0† | 0.0†
| NoLiMa
| 68.1 | 0.7 | 17.1 | 3.9 | 43.5 | 5.1 | 1.1 | 2.3 | 0.5
| LongBenchPro
| 44.8 | 23.7 | 8.2 | 42.2 | 58.4 | 34.8 | 27.9 | 53.5 | 19.6
The publisher's README is longer; it is cut here. Read the rest at the source.
