#端侧

🚀 Meet MiniCPM5-2B, a 2B-parameter language model bringing high intelligence density to the edge, now open source! It ranks #1 among open-source models under 4B parameters on the @ArtificialAnlys Intelligence Index, with a score of 23. It also scores 20 on the Agentic Index, bringing an early form of general-purpose agent capability to the edge. Across 34 benchmarks, MiniCPM5-2B achieves an average score of 53.9, covering coding, math, long-context understanding, tool use, and agentic tasks. And this release goes beyond the model itself. We’re opening up the data, training recipes, and RL stack behind MiniCPM5-2B. 🤗 Hugging Face: http://huggingface.co/openbmb/MiniCPM5-2B 💻 GitHub: http://github.com/OpenBMB/MiniCPM Modelscope: http://modelscope.cn/models/OpenBMB/MiniCPM5-2B Web: https://openbmb.cn/

OpenBMB’s MiniCPM5 brings 128K context, coding, reasoning, and agentic tool use to on-device models.📜 Apache 2.0. 🤖 https://modelscope.ai/collections/OpenBMB/MiniCPM5 🏆 Both 1B&2B models reach open-source SOTA in their size classes. MiniCPM5-2B also beats the larger models in the official comparison. 🛠️ Switch between fast answers and deep reasoning. Native tool calling powers local assistants and coding agents. 🧠 RL + OPD merges 16 specialist models, including five Agent experts, into MiniCPM5-2B. 📂 Open training data includes 500K Agent SFT samples and 80K+ RL samples for code, math, knowledge, and long-context reasoning. ⚡ Standard Llama architecture works with major inference engines. Official cookbooks and Agent Skills cover deployment and fine-tuning across popular frameworks.

Congrats @OpenBMB on launching MiniCPM5-2B! SGLang has day-0 serving support, with recipes for @NVIDIAAI RTX PRO 6000, RTX 5090, DGX Spark, and Hopper cards. We are seeing over 250 tok/s/user decode at bs=1 on coding tasks with DSpark enabled on a 5090. It's an ultra-efficient 2B with incredible intelligence density. As of today it leads Artificial Analysis's Intelligence Index among open models under 4B, and scores above models more than 4x its size. This is another step toward general-purpose models running 24x7 on device, even on phones. Run MiniCPM5-2B with SGLang 👇

OpenBMB's MiniCPM5-2B scores 15 on the Artificial Analysis Intelligence Index v4.2, the highest of any open weights model under 4B total parameters OpenBMB (@OpenBMB) is the open-source AI group behind the MiniCPM series of efficient small models. MiniCPM5-2B is a 2.6B parameter dense reasoning model with text input and output, released under Apache 2.0. Scoring 15 on the Intelligence Index, MiniCPM5-2B sits one point behind Ling 3.0 Tiny (16), which has ~3x the total parameters. Among open weights models under 4B total parameters, the next best score is Granite 4.2 3B (11). Key results: ➤ The highest Intelligence Index of any open weights model under 4B total parameters, setting a new Pareto-optimal point on Intelligence vs. Total Parameters: Its score of 15 is 4 points clear of Granite 4.2 3B (11). With 2.6B total parameters, it is 1 point ahead of Qwen3.5 4B (Reasoning, 14, estimated) with 44% fewer parameters, and level with Qwen3.5 9B (Reasoning, 15, estimated) at roughly 4x its size. As a dense model, its size advantage is in memory footprint rather than active-parameter compute. ➤ Strong agentic performance at this size: Its GDPval-AA v2 Elo of 831 leads <4B models, and on τ³-Banking it is joint-first with Ling 3.0 Tiny at 21%, compared to 8% for the next best model, Granite 4.2 8B. On AA-Briefcase, it placed second among the measured models in the comparison set with an Elo of 438, above Granite 4.2 8B (324) and just below Ling 3.0 Tiny (485). ➤ Knowledge, coding and long context are where it gives ground: MiniCPM5-2B places 7th in the set on Humanity's Last Exam (9%, behind Gemma 4 12B (Reasoning) at 16%), 8th on Terminal-Bench v2.1 (9%, behind Qwen3.5 9B (Reasoning) at 29%) and scores 0% on CritPt. On SciCode it is second of the five measured models at 26%, behind Granite 4.2 8B (31%). On AA-LCR v1.1 it scores 59%, 5th in the set, one point behind Ling 3.0 Tiny (60%). On GDP.pdf, our new professional document reasoning evaluation, it passes 1% of tasks outright, behind gpt-oss-20b (high) at 2%. ➤ Its AA-Omniscience score of -12 is earned by abstaining from answering rather than accuracy: MiniCPM5-2B attempts only 29% of AA-Omniscience questions, giving it a Non-Hallucination Rate of 78%. Its accuracy of 8% is a point below Ling 3.0 Tiny (9%) and half that of Qwen3.5 9B (Reasoning, 16%). Peers that attempt far more questions are penalized heavily, with Qwen3.5 9B (Reasoning) at -53 and gpt-oss-20b (high) at -63. ➤ It is token-efficient for a reasoning model: MiniCPM5-2B used 19k output tokens per Intelligence Index task, joint-lowest in the comparison model set with Granite 4.2 3B (19k). Ling 3.0 Tiny spends 56k, roughly 3x as many, for 1 more index point. Additional model details: ➤ Parameters: 2.6B (dense) ➤ Context window: 131k tokens ➤ Input modalities: Text only ➤ License: Apache 2.0

GLM-5.3-Flash 正式发布 - 以极具竞争力的价格提供领先性能 - 原生多模态，支持 1M token 上下文窗口 - 320B-A18B 参数模型，采用 MIT 许可证发布 - 此前以 Ox Alpha 名义预览，完全运行在中国 AI 芯片上 博客：http://z.ai/blog/glm-5.3-flash 现已在所有官方平台上线： 模型权重：http://huggingface.co/zai-org/GLM-5.3-Flash API：http://docs.z.ai/guides/llm/glm-5.3-flash 编程套餐：http://z.ai/subscribe ZCode：http://zcode.z.ai/en 对话：http://chat.z.ai AutoClaw：http://autoclaw.z.ai