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okens with YaRN. For more technical details on the architecture, training methodology, and experimental analysis of Qwen3.8-Flash-Next, please refer to the technical report in our GitHub repository. Qwen3.8-Flash-Next weights are now available on Hugging Face and ModelScope . The production version, with 1M context by default and official built-in tools, is served as Qwen3.8-Flash on QwenCloud , priced at 0.15 USD per million input tokens and 0.47 USD per million output tokens . Performance Lang

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m the SFT dataset. Max context length is 2K during calibration. GGUF Conversion of the Granite 4.2 models to GGUF is done with the canonical llama.cpp tool as described in https://github.com/IBM/gguf gguf-conversion--quantization . Several GGUF formats are provided: We trained the Granite 4.2 language models on an NVIDIA GB200 NVL72 cluster hosted by CoreWeave, featuring: A 72-GPU NVLink domain for high-speed intra-rack communication A non-blocking Fat-Tree NDR 400 Gb/s InfiniBand fabric for ful

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xplorers, researchers, and full-stack engineers. We move fast, sweat the details, and operate at frontier scale with a roadmap to build the world’s most powerful AI models. Most importantly, we’re united by the belief that doing this right is the only way to do it at all. If our mission resonates with you, we’d love to talk. Related Stories Hill-climbing MAI models for GitHub Copilot and Excel Introducing MAI-Image-2.5-Pro and MAI-Voice-2-Flash Bringing Ode Poetry to life with MAI’s audio models

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xtensively. And we have an ambitious mission we truly believe in. We’re also fortunate to partner with incredible product teams giving our models the chance to reach billions of users and create immense positive impact. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in—come and join us as we work on our next generation of models! Related Stories Building a hill-climbing machine: Launching seven new MAI models Hill-climbing MAI models for GitHub Copilot and Excel

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Build the Future With Us We’re a lean, talent-dense team of explorers, researchers, and full-stack engineers. We move fast, sweat the details, and operate at frontier scale with a roadmap to build the world’s most powerful AI models. Most importantly, we’re united by the belief that doing this right is the only way to do it at all. If our mission resonates with you, we’d love to talk. Related Stories Optimizing the frontier performance curve Hill-climbing MAI models for GitHub Copilot and Excel

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n the city in the image. Object Detection You can use Muse Glimmer to do open ended object detection in images as follows. Here is an end to end script to perform object detection GitHub Gist Llama.cpp Muse Glimmer comes with day-0 llama.cpp support. Meta has distributed calibrated quants in this repo , and Unsloth is releasing optimized quants as well. DFlash speculative decoding is supported as well. You can use a pre-built llama binary to start a llama server or a CLI. To install llama.cpp, r

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policy training, and policy evaluation within one shared Physical AI environment. Explore the models, data, and runtime behind Cosmos-H-Dreams: Cosmos-H-Dreams code and examples: GitHub repository Adapt to your own data: Step-by-step recipe for teacher fine-tuning and self-forcing distillation FlashDreams: GitHub repository NVIDIA Cosmos-Predict2.5: GitHub repository Cosmos-H-Dreams brings action-conditioned surgical world modeling into the real-time loop, creating a new environment for people

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Open Large Language Models," arXiv:2512.02556, 2025. arxiv.org/abs/2512.02556; [12]K. Jordan et al., "Muon: An optimizer for hidden layers in neural networks," 2024. kellerjordan.github.io/posts/muon — see also J. Liu et al., "Muon is Scalable for LLM Training," arXiv:2502.16982, 2025; [13]Fireworks AI, "Frontier RL Is Cheaper Than You Think," 2026. fireworks.ai/blog/frontier-rl-is-cheaper-than-you-think; [14]DeepSeek-AI, "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcemen

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ts | Qwen Blog Publication About Try Qwen Chat Qwen-AgentWorld: Language World Models for General Agents June 22, 2026 · 15 min · 3088 words · Qwen Team | Translations: 简体中文 PAPER GITHUB HUGGING FACE MODELSCOPE Today we release Qwen-AgentWorld , a native language world model that simulates agent environments across seven domains: Native world modeling: environment modeling is the training objective from continual pre-training onward (CPT → SFT → RL), not a post hoc adaptation on top of a general

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t's opened up a class of long-horizon problems that were out of reach for earlier models.Michael Truell CEO and Co-founder Claude Fable 5 is a real step forward for the developers GitHub serves. In our early testing, it took on complex, long-horizon coding tasks with a level of autonomy and reliability that exceeded previous benchmarks. But what excites us most is the direction it points: a future where developers can hand increasingly ambitious work to agents and trust the results across the so

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it for real world use so you can produce highly accurate transcripts for your bespoke use cases up to 5x faster than rival models.6 It’s now being integrated into Copilot, Teams, GitHub, and Dynamics 365 Contact Centre – and it’s also available in Foundry, where it’s the fastest, most efficient and most cost-effective transcription model of any hyper-scaler.5 Paired with that we have MAI-Voice-2, our latest speech generation model. It has beautiful prosody, native-sounding delivery and fine-gra

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SWE-bench Pro: Problematic tasks corrected and all baselines evaluated on the refined benchmark. QwenClawBench: a real-user-distribution Claw agent benchmark; open-source: https://github.com/SKYLENAGE-AI/QwenClawBench . CoWorkBench: an internal cowork benchmark; long-horizon tasks across computer science, finance, law, medical, and other productivity domains. SkillsBench: Evaluated via OpenCode on 78 tasks (excluding 9 external API-dependent tasks); avg of 5 runs. MCP-Mark: GitHub MCP v0.30.3; P

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ion history, task state, and approvals carrying across. Vibe sits between the systems engineering teams already use, with humans in the loop wherever they're needed. It plugs into GitHub for code and pull requests, Linear and Jira for issues, Sentry for incidents, and apps like Slack or Teams for reporting. Each coding session runs in an isolated sandbox, including broad edits and installs. When the work is done, the agent can open a pull request on GitHub and notify you, so you review the resul

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ation benchmark; bilingual (EN/CN), 7 categories; auto-render + multimodal judge; BT/Elo rating. QwenClawBench: a real-user-distribution Claw agent benchmark; open-source: https://github.com/SKYLENAGE-AI/QwenClawBench . CoWorkBench: an internal cowork benchmark; long-horizon tasks across computer science, finance, law, medical, and other productivity domains. SkillsBench: Evaluated via OpenCode on 78 tasks (excluding 9 external API-dependent tasks); avg of 5 runs. MCP-Mark: GitHub MCP v0.30.3; P

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ng effort) + default BM25 retrieval. VITA-Bench: Avg subdomain scores; using claude-4-sonnet as judger, as the official judger (claude-3.7-sonnet) is no longer available. MCPMark: GitHub MCP v0.30.3; Playwright responses truncated at 32K tokens. MCP-Atlas: Public set score; gemini-2.5-pro judger. AIME 26: We use the full AIME 2026 (I & II), where the scores may differ from Qwen 3.5 notes. Vision Language Qwen3.6 is natively multimodal, and Qwen3.6-35B-A3B showcases perception and multimodal reas

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ng effort) + default BM25 retrieval. VITA-Bench: Avg subdomain scores; using claude-4-sonnet as judger, as the official judger (claude-3.7-sonnet) is no longer available. MCPMark: GitHub MCP v0.30.3; Playwright responses truncated at 32K tokens. MCP-Atlas: Public set score; gemini-2.5-pro judger. HLE w/ tool: 256K ctx w/ context-folding; prunes older tool responses upon threshold breach. WideSearch: 256K ctx w/ management; prunes ≥49,152 tool tokens when 208,896 used. AIME 26: We use the full AI

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large codebases is essential. For teams running agentic coding at scale, we’re seeing strong resolution rates and the kind of consistency developers need.Joe Binder VP of Product, GitHub Claude Sonnet 4.6 has meaningfully closed the gap with Opus on bug detection, letting us run more reviewers in parallel, catch a wider variety of bugs, and do it all without increasing cost.Scott Wu CEO, Cognition For the first time, Sonnet brings frontier-level reasoning in a smaller and more cost-effective for

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l Agents | Qwen Blog Publication About Try Qwen Chat Qwen3.5: Towards Native Multimodal Agents February 14, 2026 · 196 min · 41706 words · Qwen Team | Translations: 简体中文 QWEN CHAT GitHub Hugging Face ModelScope DISCORD We are delighted to announce the official release of Qwen3.5 , introducing the open-weight of the first model in the Qwen3.5 series, namely Qwen3.5-397B-A17B . As a native vision-language model, Qwen3.5-397B-A17B demonstrates outstanding results across a full range of benchmark ev

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105 分，显著高于金牌线（78 分）及国家集训队入选线（87 分）。对比 Ring-2.5-1T 与 Ring-1T 的答题结果可以发现，前者在推理逻辑严谨性、高阶数学证明技巧使用以及答案表述完整性方面均有明显提升。我们现已公开 Ring-2.5-1T 在 IMO 2025 与 CMO 2025 中的详细解答，完整内容可通过以下链接查看：https://github.com/inclusionAI/Ring-V2.5/tree/main/examples 此外，在挑战性的智能体搜索 GAIA2-search 任务中，Ring-2.5-1T 达到开源 SOTA 水平。GAIA2 环境强调跨应用工具协作与复杂任务执行能力，Ring-2.5-1T 在规划生成与多步工具调用上的效率与准确性均表现突出。 2. 万亿规模的混合线性注意力架构 在通用智能体时代，深度思考（deep thinking）与长程执行（long-horizon agent）正成为语言基座的基本工作范式。这一转变对基座模型在长程推理解码效率上的架构能力提出了极高要求。作为迈向智能体模型（agentic model）架构的关

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face every day—especially agentic workflows that demand planning and tool calling. This starts unlocking long-horizon tasks at the frontier.Mario Rodriguez Chief Product Officer, GitHub Claude Opus 4.6 is a huge leap for agentic planning. It breaks complex tasks into independent subtasks, runs tools and subagents in parallel, and identifies blockers with real precision.Michele Catasta President, Replit Claude Opus 4.6 is the best model we've tested yet. Its reasoning and planning capabilities h

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