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Jev 在 Vercel 的 AI Gateway 上线后增长最快，比之前的模型都猛，做开发的朋友可以看看是不是要接。
Reflection AI 把自家的智能体编程模型 Beam 开源了，AWS 博客还手把手教你在 SageMaker 上部署，vLLM、SGLang 都能用。
Day-0 serving and RL support means practitioners can deploy and fine-tune a 501B-parameter sparse model without waiting for ecosystem catch-up. Teams running large-scale inference can start with vLLM and add reinforcement learning through SkyRL's LoRA or MXFP8 paths. The 1M-token context window puts Beam in direct competition with other frontier sparse models for long-document and agentic workloads.
Google 新模型 ARC-AGI-3 拿了 97.5%，仅次于 OpenAI，但换个测试环境直接掉到 49.8%，数学和写代码还被对手压着，这篇把两面都讲了。
Gemini 4 Argon 刚拿下 WANDR 纪录，用 Perplexity Pro 或 Max 的话现在就能切换体验，可以去试试搜索效果差多少。
Meta 的 816B 大模型现在有量化版能跑在 256GB 内存的机器上了，224GB 那档保住 89.2% 原始质量，有硬件的可以试试。
Reflection把Beam 1.0开源了，501B总参但只激活23B，跑起来不算重，不过评分比GLM 5.3 Flash低一些，可以拿来对比试试。
Apache 2.0 weights with a 1M context window make Beam one of the few permissively licensed models built explicitly for agentic coding workloads. Developers can run it locally or via Ollama Cloud with zero data retention, and it plugs directly into Claude Code, Codex and OpenCode through Ollama's launcher commands. Teams evaluating open alternatives to closed coding models now have a new option to benchmark against their own agent pipelines.
An Apache 2.0 release with NVFP4 quantised weights and a companion training harness is rare, since most open-weight drops stop at the checkpoint. Teams running inference on NVIDIA hardware can take the DFlash2 drafter and FP8/NVFP4 variants directly into production stacks without negotiating licences. Engineers evaluating open models against Llama or Qwen releases should benchmark Beam on their own workloads.
24 of the top 25 open models on Code Arena: WebDev come from Chinese labs, and Beam 1.0 is the lone US exception — a notable shift in the open-weights competitive landscape. Builders evaluating Apache 2.0 agentic models can compare Beam directly against Qwen3.8, Kimi K2.5 Thinking and GLM-5 once the full weights drop this month. Its 23B active parameter design targets reasoning efficiency for agent workloads.
Usage on OpenRouter is a proxy for real developer demand, and 19.5T tokens in a first week puts Step 5 Preview alongside the most-routed open models worldwide. Teams choosing inference backends can compare its cost and throughput against Qwen and DeepSeek offerings on the same leaderboard. Its traction also signals continued Chinese lab momentum in the global API routing market.
An Apache 2.0 release at 501B scale with day-0 4-bit checkpoints gives enterprises an openly licensed frontier-class option alongside DeepSeek and Qwen. Engineers can download the NVFP4 weights directly from Hugging Face and run them on Blackwell Ultra hardware at the stated throughput. The inclusion of the Mirror CLI training harness also makes the full stack reproducible for teams building their own large models.
Day-one inference support on SGLang means teams can self-host Beam immediately instead of waiting on API-only access, an unusual level of launch coordination between a model vendor and an open inference framework. Practitioners evaluating large models against vLLM or TensorRT-LLM can use this deployment path as a direct comparison point. Watch whether other labs adopt similar launch-day community integrations.
Reflection AI 放出了一个新的开源模型，vLLM 还顺手写了优化教程，想自己部署跑推理的可以看看这篇。
Reflection 的第一个开源模型 Beam 出来了，501B 参数只激活 23B，跑分超过 NVIDIA 和 K2 Horizon，但幻觉率有点高，终端任务也比较弱。
Instead of wrapping a general model in a memory framework like Mem0 or MemOS, this uses a small dedicated writer model, cutting inference cost to roughly 3B-model levels while staying cross-framework. Developers building agent pipelines can pip install it and swap storage backends freely, and Mac users can share memory across coding tools via MemHub. The sub-1-point margin over Claude Opus 5 is self-reported and awaits independent replication.
The sparse-activation design at 501B/23B active with a 1M-token window makes Beam a candidate for long-context workloads without full MoE serving costs. Teams running vLLM can deploy it immediately and fine-tune with SkyRL's LoRA or MXFP8 RL paths. Compared with other large reasoning models, Beam's Day-0 coverage of both serving and RL tooling is the differentiator.
A sparse mixture-of-experts model with an integrated agent harness reduces the token cost of running long coding sessions, which directly lowers API and compute bills. Developers building agentic coding workflows can try it on day one through Transformers and pick NVFP4 or FP8 checkpoints to fit their hardware budget.
Meta 新发的开放权重模型 Muse Spark Open，号称美国最强开放模型，胜过 Qwen 3.8 和 Kimi K3，还能两台 Mac Studio 本地跑满血版。
Mistral 晒出自家 Large 4 在 Ulam 家 STEMBench 上工程和天文题的成绩，做 STEM 任务的可以看看这个榜单。
The trade-off here is deliberate: Liquid AI swapped raw accuracy for knowing when not to answer, which matters more for agentic decision workflows where a wrong answer is costlier than a refusal. Builders routing high-volume decisions can compare it directly against Celeris-1 Decision ($32 per 1M tasks), Mercury Decide ($15) and GPT-6 Luna (Decisions) ($79) on both price and speed.
腾讯混元开源了个记忆管家 Hy Memory 2.0，30B 模型自称打赢 Opus 5，MemHub 还能在 10 多款工具间同步记忆，搞智能体的可以试试。
有人发帖夸 Claude Opus 5.5 是现在最强的模型，说 Anthropic 找回了状态，想换模型前可以看看这波评价。
Meta 把 816B 的 MoE 模型开源了，还有 NVIDIA 和 FLUX 的新模型，一条看完今天所有大新闻。
快手 Kling 4.0 放出一段一镜到底的游戏概念视频，镜头跟着角色从窄缝飞到开阔天空，可以看看现在视频生成的一镜到底水平。
Replacing a dedicated speech decoder with a general-purpose 4B LLM means recognition inherits stronger word-context modelling, which helps resolve acoustically ambiguous words — an architectural shift other ASR builders may follow. Developers building multilingual voice features can run it locally today under Apache 2.0, inspecting or fine-tuning it without API costs, and benchmark it against Whisper-class models on their own audio.
黑森林开源了 FLUX 3 Image Klein，文生图、改图、多图参考都能做，作者拿它和 Ideogram 4.5、Krea 3 比了效果，还提醒中文字容易翻车。
Nvidia 开源了 Nemotron 模型，企业能用自己的数据微调 Super VL，还能部署在自己服务器上，不用把数据交给外部云。
PrismML 用三元权重把 Qwen 3.5 压到 0.43GB 起，手机上就能跑本地智能体，llama.cpp 和 MLX 都能部署，权重还开源了。
Xiaomi 的 MiMo V2.6 Pro 在 Agent Arena 开源榜拿了第一，Pro 和 Flash 还都卡在性能成本最优区间，跑智能体的可以关注下。
微软自研基模 MAI-Base-2 训完了，2.8T 总参数，算力效率是 DeepSeek-V4-Pro-Base 的 4~12 倍，还放出了训练细节。
决策模型赛道卷起来了：TypeSafe 刚发 Jev 1.14，第二天 Liquid AI 的 d1-ultra 就宣称八类任务全面领先，看看双方各自强在哪。
Muse Spark Open 跑在 vLLM 上了，支持多模态和 1M 上下文，还有个挺可爱的演示小故事，可以看看。
NVIDIA 把印度语言预训练的语料方案开源了，2.8 万亿 tokens 加完整数据管线，做多语言训练的可以直接抄作业。
StepFun 新模型一开源，四家国产芯片当天就能跑，还搭上了华为 1024 卡的 Atlas 950 超节点，国产算力生态这块值得看看。
Meta 那个专攻写代码的开源模型 Muse Spark Open 上架 SageMaker JumpStart 了，用 SDK 几行就能部署到自己 AWS 账户里跑推理。
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