全部 AI 动态

🤝 Day-0 support for MiniCPM5-2B on stable vLLM. ⚡ Dense 2.6B model with 131K native context 🧠 Think / No-Think from the same checkpoint 🔧 Tool Calling support via vLLM’s minicpm5 parser Congrats @OpenBMB on the release, and thanks for keeping it on stock LlamaForCausalLM and opening the training data alongside the weights! 🙌 🔗 http://recipes.vllm.ai/openbmb/MiniCPM5-2B

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

Ling-3.0-flash-Fin and FinFIRST are now open: a finance model built for real workflows, plus a benchmark that verifies the work. Developed by Ant Group with financial institutions and domain experts.🏦 🧠 MODEL 🤖 https://modelscope.ai/models/inclusionAI/Ling-3.0-flash-Fin ⚙️ 124B MoE, only 5.1B active per token, around 256K context. Financial training even lifts its AA Intelligence Index score from 38 to 41. 🛠️ Search sources, analyze filings, build valuations, update complex spreadsheets, and produce editable reports, all in private deployments. 🏆 82.45% on FinFIRST source verification, standing out among the open models evaluated. 📊 BENCHMARK 🤖 https://www.modelscope.ai/datasets/inclusionAI/FinFIRST 🔍 123 expert-authored tasks grade the complete research chain through 701 atomic criteria: not just answers, but sources, definitions, and reasoning.

GPT-6 Astra 现已面向 Work/Codex 中的所有 Pro、Enterprise 和 Business Premium 用户开放，并已在 API 中提供。 我们接下来将开始向 Plus 和 Business 用户推送。 感谢大家的耐心等待。

GPT-6 Astra 现已面向 Work/Codex 中的所有 Pro、Enterprise 和 Business Premium 用户开放，并已在 API 中提供。 我们接下来将开始向 Plus 和 Business 用户推送。 感谢大家的耐心等待。

GPT-6 Astra is now available to all Pro, Enterprise, and Business Premium users alongside the API. I’ve been using it to build apps, LEGO models, and 3D scenes. Here are five things that changed how I use Codex 👇 https://x.com/i/article/2095952228591022080

GPT-6 Astra is our best model yet for computer use, software engineering, and visual understanding to date. Hear from the builders putting it to work:

1/ we just publicly released Muse Spark 1.3 max! we see significantly stronger coding and agentic performance on muse spark 1.3 max, so would strongly recommend trying it out even if you've already tried muse spark 1.3 high or muse spark 1.3 xhigh.