搜索“DeepSeek”

optimizer substantially outperforms the widely-used AdamW optimizer for LLM training. Kimi K2 was designed to further scale up Moonlight, which employs an architecture similar to DeepSeek-V3. Based on scaling-law analysis, we reduce the number of heads for long-context efficiency, and increase MoE sparsity for greater token efficiency. While scaling up, we encountered a persistent challenge: training instability caused by exploding attention logits, an issue that occurs more frequently with Muo

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sk figures await tiered-pricing support Context Haiku 4.5 has been Anthropic’s small model since October 2025. Since then, OpenAI’s GPT-6 Luna, Gemini 3.8 Flash, GLM-5.3 Flash and DeepSeek V4.1 Flash have competed at the low-cost end; Haiku 5.5 matches Luna’s sticker price exactly. It adds effort control and a 1M context; It is explicitly positioned as the cheap worker inside multi-model agent harnesses: Claude Code subagents, Devin Fusion, Copilot subagents, and compaction or summarization step

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sets into actionable insights. Michal SutterReka Releases Rho-1: A 19B Omni-Reasoning Model That Understands, Generates Video and Outputs Robot Actions in One; Michal SutterCan an Open Model Do Security Research? Cantina’s apex-flash-1 Solves 40 of 60 Held-Out Bug Tasks; Michal SutterGoogle Research Moves Federated Learning Into TEEs: Gboard Now Trains With Externally Verifiable Differential Privacy; Michal SutterDeepSeek Harness v0.2 Brings Official Desktop Apps to Its Open-Source Agent Harness

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a. October 7, 2026 Reflection AI’s new open-weight models highlight the growing ambition among U.S. AI companies to compete with and outperform Chinese AI vendors such as Alibaba, DeepSeek, Moonshot and Z.ai. It also shows how the AI market is becoming more divided. The Nvidia-backed vendor unveiled its new open weight model, Beam, on Monday. It is a mixture-of-experts model with 501 billion parameters, aimed at coding, reasoning and agentic workloads. The model was trained on 23.8 trillion toke

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ttack prep, Mistral doesn't explain. In Lakera's B3 AI Security Benchmark, ML4 blocks 93.3 percent of attacks, according to the company. ML4 takes second place in coding, ahead of Deepseek and Qwen In programming, ML4 scores 49.8 percent in the Artificial Analysis Coding Agent Index, putting it ahead of Deepseek V4 Pro and Qwen3.8 Max . Mistral also ran a blind evaluation with Surge AI, where professional annotators rated code quality without knowing which model produced it. ML4 landed second ou

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complex terminal workflows, scoring 61.7% on DeepSWE v1.1, 59.4% on SWE-Atlas-QnA, and 28.3% on Terminal-Bench 4. Its combined Coding Agent Index score of 49.8% places it ahead of DeepSeek V4 Pro 0813 and Qwen3.8 Max. DeepSWE 1.1; Terminal Bench 4.0; SWE Atlas QnA Scores on DeepSWE, Terminal-Bench 4, and SWE Atlas QnA use the numbers reported by the ArtificialAnalysis coding index. Vibe Code Bench v1.1 uses the numbers reported by vals.ai. We also ran a blind human evaluation with Surge AI on co

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ailable model. Instead of chasing peak performance, Beam aims to deliver strong results with minimal compute, putting it in direct competition with Chinese open-weight models from Deepseek and Qwen. Reflection built Beam for coding, logical reasoning, and agentic tasks. The mixture-of-experts model activates 23 billion of its 501 billion total parameters per token, keeping compute costs relatively low. On demanding reasoning tasks, Beam matches GLM 5.2 while using three to four times less comput

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ries,” where a hedge fund or government runs Beam on its own chips and private data, with a test reportedly underway in South Korea Why it matters: Reflection has been called the “DeepSeek of the West,” and while its first model isn’t rattling markets, it enters a U.S. ecosystem light on competition against open Chinese counterparts. Beam’s main comparison is to GLM-5.2, a model Z.ai has already replaced, which says a lot about how far ahead the Chinese labs still are. TOGETHER WITH OPTIMIZELY 🎓

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pabilities over its predecessor. With a GDPval-AA v2 Elo of 1,622 and an AA-Briefcase Elo of 1,400, the model rivals peer models such as GLM-5.3-Flash, Gemini 3.8 Flash (high) and DeepSeek V4.1 Flash (Max). Ling 3.1 Flash also shows notable gains on AutomationBench-AA (62%) and Terminal-Bench v4.0 (33%). ➤ Ling 3.1 Flash scores +2 on AA-Omniscience, a 22 point improvements from Ling 3.0 Flash (-18). Compared to its predecessor, its accuracy rate rose from 18% to 29% while the hallucination rate

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ing complex datasets into actionable insights. Michal SutterCan an Open Model Do Security Research? Cantina’s apex-flash-1 Solves 40 of 60 Held-Out Bug Tasks; Michal SutterGoogle Research Moves Federated Learning Into TEEs: Gboard Now Trains With Externally Verifiable Differential Privacy; Michal SutterDeepSeek Harness v0.2 Brings Official Desktop Apps to Its Open-Source Agent Harness; Michal SutterPrime Intellect Launches Prime Inference: Serverless and Reserved Serving for Frontier Open Models

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l filters would have dropped. The architecture interleaves local and global attention with fine-grained routed experts. Load balancing builds on auxiliary-loss-free balancing from DeepSeek-V3, adding cosine decay of expert-bias updates. The busiest expert reached just 1.04x average load at the end of pretraining. Across all 52 layers, residual norms stayed bounded using depth-based scaling, SandwichNorm, attention gating and FP32 residual accumulation. Pretraining finished in under 4 weeks on 6,

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tches the performance of leading Chinese open models on advanced reasoning benchmarks at dramatically lower costs, a claim that could heat up the race to build a Western answer to DeepSeek, Qwen, and Z.ai. Reflection’s announcement confirms reporting from Axios over the weekend that the startup was close to a launch. The company shared new details in a lengthy blog post Monday , which described Beam as a text-only mixture-of-experts model trained on high-compute reinforcement learning to be effe

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lex datasets into actionable insights. Michal SutterGoogle Research Moves Federated Learning Into TEEs: Gboard Now Trains With Externally Verifiable Differential Privacy; Michal SutterDeepSeek Harness v0.2 Brings Official Desktop Apps to Its Open-Source Agent Harness; Michal SutterPrime Intellect Launches Prime Inference: Serverless and Reserved Serving for Frontier Open Models; Michal SutterMicrosoft AI Releases MAI-Transcribe-2-Streaming: 1 Real-Time Speech-to-Text Model on Artificial Analysis

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ne, typically backed by Claude 3.5 Sonnet or 3.7 Sonnet. The competing report was generated by feeding ScholarQA's retrieved literature excerpts to a different model: o3, o4-mini, DeepSeek-V3, or DeepSeek-R1, depending on the example. Two judge models – GPT-4.1 and DeepSeek-R1 – compared each pair and picked a winner. We ensured that LLM judges were aligned with human preferences (95% agreement) and only kept pairs where both judges agreed, which gave us a cleaner preference set and cut much of

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ction) at equal batch size and throughput ( @ekzhang1 , @cHHillee ). Implication : It helps prefill-heavy workloads, not decode-bound low-latency serving. Compilers and hardware : DeepSeek on Huawei : DeepSeek released an open-source Ascend toolkit with TileLang optimized for Ascend 950 ( @kimmonismus ). AI as compiler : A model translates Triton directly to PTX, with a verifier checking correctness, races and deadlocks. Speedups on B200 reach 1.37x on FlashAttention ( @Azaliamirh ). Vera Rubin

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fixed performance has been falling 47% per quarter since 2023 was one of the more useful macro datapoints of the day @EpochAIResearch . /r/LocalLlama + /r/localLLM Recap 1. Qwen, DeepSeek and AliceAI Large-Model Roadmaps Qwen4-27B just confirmed (Activity: 2353): A conference slide image appears to confirm an upcoming Qwen4 Series lineup, explicitly listing Qwen4-27B alongside Qwen4-Max, Qwen4-Flash, and Qwen4-Plus. The post highlights community interest in whether Alibaba will also release a s

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, so it is very surprising to the established order of names you have come to know and love. And… it is natively omnimodal! Xiaomi made news a few days ago when Fuli Luo, a former DeepSeek star engineer now at Xiaomi , started publishing their final RL training runs live, which showed an abnormal amount of transparency in their internal metrics . As they note in their technical report , they scaled RL compute along three axes: Larger batches and higher throughput : large batches on a fully async

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16ビット版は65.6％だった。3回のうち一度でも見つけた脆弱性は、Altar-1とGLM-5.3の量子化版がともに23件、16ビット版が25件だった。同社は、量子化版と同じ23件を検出しながらモデルのサイズを32.8％減らせた点を、狙いどおりの成果としている。 なお、同社の8月公開の記事では、この32件を使った他モデルの評価結果も紹介している。3回を通じてDeepSeek V4 Proが28件、Opus 5とGrok 4.6がそれぞれ26件を再発見し、検出件数はAltar-1を上回っている。 Hugging Faceで公開⁠、H200を4基使う構成を案内 モデルはHugging FaceのAikidoSec/altar-1から入手できる。モデルカードでは、NVIDIA H200を4基搭載した環境で推論エンジンvLLMを使う構成を案内している。328GBはモデルの重みを保存した際のサイズで、実行時には入力や生成内容に応じた一時データの分もメモリが必要になる。 ライセンスはGLM-5.3 Licenseを継承し、著作権・許諾表示の保持などを条件に商用利用、改変、再配布を認める。ただし、利

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啊。终于在频繁的测试后自己崩溃了，我这里接管催它尽快交付。 最后用时 3 小时 46 分钟，使用 GLM Coding Plan Lite 套餐，5 小时额度消耗 90%，周额度消耗 18%。 等了这么久，看看成品。方向键是反的。最大的问题是，一旦撞到赛道两边的护栏，车就动不了了。最后勉强跑完了一整圈，等了快四个小时，做成这样还是有点失望。 这里我也用了 DeepSeek 拥有视觉能力的新模型 DeepSeek V4.1 Flash 测了一下，只能说半斤八两。方向键终于能正常操作了，但是没有边界限制，可以满图随便跑。 这轮两家的表现都差强人意。 Part 3 · 复刻操作系统 接下来是经典测试题，用一个 HTML 文件复刻整个 macOS 桌面系统，另外还得在里面做一个能玩的射击游戏。用了 1 小时 21 分钟，5 小时额度消耗 18%，周额度消耗 5%。 图标绘制得很精致，访达、备忘录、终端、设置都正常可用，壁纸也能换。 最后看看射击游戏，也没任何问题，走路的时候还有轻微抖动，有不同强度的敌人，整体没有出现任何 bug。 同样用 DeepSeek 也测了一下，效果比起 V4 Fla

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Qwen3.8-Flash on QwenCloud , priced at 0.15 USD per million input tokens and 0.47 USD per million output tokens . Performance Language Qwen3.8-Flash-Next Qwen3.8-27B Qwen3.7-Plus DeepSeek-V4-Flash-0731 Claude-Opus-4.6 (Max) Params 125B 27B 397B 284B -- Activated params 6B 27B 17B 13B -- N-gram embedding params 51B -- -- -- -- Coding Agentic coding DeepSWE 1.1 58.7 42.2 16.5 54.4 -- Agentic coding SWE-bench Pro 62.5 61.7 55.8 56.0 53.4 Multilingual software engineering SWE-bench Multilingual 81.

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