阿里把 Qwen3.8 Max 出了个高吞吐版 Prime,2.4T 参数、1M 上下文还能吃图和视频,已经能在 OpenRouter 直接调了。
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Alibaba 在 Apsara 2026 透露 Qwen4 正在训练,后续 Qwen4.5、Qwen5 要做到 5–10T 参数,用 Qwen 的可以关注下节奏。
SGLang、Qwen和NVIDIA联手推出NVFP4 KV Cache,4-bit精度下1.78倍容量提升,长上下文推理提速近80%。
Qwen4 已开训,参数要扩到 5-10T;Qwen3.8-Max 没人参与自己迭代 33 轮,得分追平 Claude、GPT。
Xiaomi 把 RL 训练全开源,Pro 版跑赢 Qwen3.8 Max,价格比涨价后的 DeepSeek 更低。
Chinese models like Qwen3.8-Max and DeepSeek V4-Pro now rival top closed-source options, affecting AI development costs globally.
jev 这类新决策模型有了开源平替,SemIf 基于 Qwen3.5,本周 LangSmith Gateway 免费用。
For developers, Kev offers a low-cost, open-source alternative to proprietary decision models like Jev, with a small footprint and fast inference times.
For users needing real-time translation, this model reduces latency by 0.5 seconds compared to the previous version, improving the user experience.
This update improves the efficiency and speed of the Qwen3.8-LiveTranslate model by optimizing its internal architecture.
For developers, Qwen3.8-Omni-Flash offers a more affordable alternative to Google's Gemini Flash for multimodal tasks.
For users needing real-time translation, this new model from Alibaba offers lower lag and better speaker separation compared to previous versions.
For users needing high-quality, real-time translation, this model offers significant improvements in speed and accuracy compared to previous versions.
For users needing a powerful multimodal AI, the new model is 26% better than its predecessor and offers a 1 million token context, which is useful for processing long documents or videos.
This matters to developers because it provides a new, open-source approach to fine-tuning models for structured decision-making, offering a concrete alternative to the closed-source Jev.
For users needing a high-performance model with low memory footprint, Bonsai 2 27B offers a significant efficiency advantage over full-precision models.
For users needing to process long-form audio-video content, Qwen3.8-Omni-Flash is more affordable than its predecessor, with a 89% reduction in video input costs.
For users needing to process long audio or video files, the model's price for hourly audio input has dropped by over 98% and for video input by over 93%.
For users needing to process documents in Chinese and English, WeVisDoc offers a free, open-source alternative to commercial models like HunyuanOCR-1.5, with demonstrated improvements in handling degraded or real-world images.
This research provides a new method for improving the exploration capabilities of AI agents, which could lead to more robust and effective systems in various applications.
For developers interested in navigation models, LightNav-0 is a new open-source option that outperforms on 10 key benchmarks compared to other models in its category.
For users needing a reliable, multilingual voice model, Spark-Audio-1.0-Preview offers strong performance in complex, noisy environments, comparable to larger, closed-source models.
This demonstrates the practical application and performance of the Qwen3.8-27B model in creating a complex game, showing its capabilities beyond typical office tasks.
This research provides a method to improve the performance of large language models on GPUs by better managing memory usage, which is relevant for anyone working with or evaluating MoE models.
The Qwen3.8-27B model is now available on Cerebras hardware, offering faster inference speeds compared to its previous deployment.
First open-source MoE model matching Qwen-Max performance, offering developers enterprise-grade capabilities without cost.
Adaption AI 证明 Agent 表现差往往不是模型问题,而是 Harness 设计问题。优化 Harness 能大幅提升性能,还能降低成本。
PlannerForge improves autonomous driving testing efficiency, with open-source models matching commercial performance and reducing collisions by over 50% through cost-tuning.
Developers can access this open-source model for autonomous driving applications, offering a cost-effective alternative to proprietary solutions.
ACE improves efficiency for MoE models without retraining, benefiting developers working with large language models in resource-constrained environments.
阿里开源了首个自动驾驶视觉语言模型Qwen-Drive-1.0-4B,能同时处理3D感知和运动规划,还强化了闭环安全性。
Available for developers in ASEAN region, WeMM-Embedding offers strong performance on multimodal tasks and is already proven at massive scale in WeChat's ecosystem.
Thomson Reuters 花费 4000 万美元训练自己的大模型,用 175 年专业数据,性能接近 Claude Opus 4.8。