搜索“DeepSeek”

3 is the largest open LLM any Chinese company has ever released, assuming the open weights arrive on July 27 as promised. The AI community and much of X are calling it Moonshot’s “DeepSeek R1” moment, three weeks after Z.ai (Zhipu AI) sparked its own viral storm with GLM 5.2. The timing was deliberate: K3 landed just ahead of the World Artificial Intelligence Conference in Shanghai where Chinese President Xi Jinping called for greater open-source collaboration. What makes K3 an unusual release t

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naturally we want to train our own model on longer horizon tasks. This introduces two challenges. First, rollouts can extend far beyond the raw context window. Second, as shown by DeepSeek R114, RL on reasoning tasks tends to produce progressively longer responses, but we want the model to be efficient in its reasoning and only elaborate on difficult tasks. We address these issues with training for self-compaction and an alternating length penalty. When an agent approaches the context limit, we

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A Cross-harness generalization across diverse agent frameworks Call via API on Alibaba Cloud Model Studio . Performance Text Benchmarks Opus-4.6 Max K2.6 Thinking GLM-5.1 Thinking DeepSeek-V4-Pro Max Qwen3.6-Plus Qwen3.7-Plus Coding Agent Terminal Bench 2.0-Terminus 65.4 66.7 63.5 67.9 61.6 70.3 SWE-Verified 80.8 80.2 -- 80.6 78.8 77.7 SWE-Pro 57.3 59.5 58.8 59.0 56.6 57.6 SWE-Multilingual 77.5 76.7 -- 76.2 73.8 75.8 NL2repo 47.6 42.8 41.0 35.5 34.4 41.1 SciCode 51.9 52.2 45.1 -- 41.4 51.3 QwenW

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from labs.scale.com. Claude Opus 4.7, MiniMax-M2.7, and MiniMax-M3 used Mini-SWE-Agent as scaffolding, with evaluation logic aligned with the official method. NL2Repo: Scores for DeepSeek-V4-pro, Kimi-k2.6, and GLM-5.1 are taken from https://qwen.ai/blog?id=qwen3.7. Other models were evaluated on internal infrastructure with a sandbox configured as 1C2G and a 4-hour timeout. Claude Opus 4.7, MiniMax-M2.7, MiniMax-M3, and Gemini 3.1 Pro used Claude Code scaffolding; GPT-5.5 used Codex scaffoldin

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livered the final 1.2x improvement in the last hours. We also ran the same task with several other models under identical conditions. GLM 5.1 reached 7.3x; Kimi K2.6 reached 5.0x; DeepSeek V4 Pro reached 3.3x; Qwen3.6-Plus reached 1.1x. Models that stopped early did so because the agent issued no tool calls for five consecutive rounds — the model concluded it could no longer make progress and voluntarily ended the session. In addition to achieving strong kernel generation results on PPUs, Qwen3.

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tches or exceeds open-weight models such as Nemotron-3-Nano-30B-A3B and Mistral-Small-4-119B while remaining competitive with first-generation frontier, reasoning models including DeepSeek-R1-0528, Gemini-2.5-Pro. Alongside ZAYA1-8B, Zyphra introduces Markovian RSA, a novel test-time compute methodology that combines parallel trace generation with fixed-length context chunking to enable unbounded reasoning while keeping memory costs constant. With this methodology, ZAYA1-8B approaches or exceeds

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the performance of models many times its size such as Mistral-Small-4-119B, and remaining competitive with substantially larger first-generation frontier reasoning models such as DeepSeek-R1-0528, Gemini-2.5-Pro and Claude 4.5 Sonnet. With our novel Markovian-RSA test-time compute methodology, we achieve significant additional performance gains — exceeding Claude 4.5 Sonnet and GPT-5-High on HMMT'25 (89.6 vs 88.3) and closing in on frontier open-weight models such as DeepSeek-V3.2 on mathematic

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the history to keep the context within limits. BrowseComp: we tested two strategies, simple context-folding achieved a score of 69.0, while using the same discard-all strategy as DeepSeek-V3.2 and Kimi K2.5 achieved 78.6. WideSearch: we use a 256k context window without any context management. MMLU-ProX: we report the averaged accuracy on 29 languages. WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL. MAX

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长程执行：通过大规模 fully-async agentic RL 训练，显著提升针对复杂任务的长程自主执行能力，使得 Ring-2.5-1T 可以轻松适配 Claude Code 等智能体编程框架和 OpenClaw 个人 AI 助理。 1. 深度思考与长程执行 为评估 Ring-2.5-1T 的深度思考和长程执行能力，我们选取了具有代表性的开源思考模型（DeepSeek-v3.2-Thinking、Kimi-K2.5-Thinking）和闭源API（GPT-5.2-thinking-high、Gemini-3.0-Pro-preview-thinking-high、Claude-Opus-4.5-Extended-Thinking）作为参考。 Ring-2.5-1T 在数学、代码、逻辑等高难推理任务（IMOAnswerBench、AIME 26、HMMT 25、LiveCodeBench、ARC-AGI-V2）和智能体搜索、软件工程、工具调用等长程任务执行（Gaia2-search、Tau2-bench、SWE-Bench Verified）上均达到了开源领先水平。 我们还额外测试

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ini 3 Pro on key reasoning benchmarks. The table below presents a more comprehensive set of evaluation scores. Capability Benchmark GPT-5.2 -Thinking Claude-Opus -4.5 Gemini 3 Pro DeepSeek V3.2 Qwen3-Max -Thinking Knowledge MMLU-Pro 87.4 89.5 89.8 85.0 85.7 MMLU-Redux 95.0 95.6 95.9 94.5 92.8 C-Eval 90.5 92.2 93.4 92.9 93.7 STEM GPQA 92.4 87.0 91.9 82.4 87.4 HLE 1 35.5 30.8 37.5 25.1 30.2 Reasoning LiveCodeBench v6 87.7 84.8 90.7 80.8 85.9 HMMT Feb 25 99.4 - 97.5 92.5 98.0 HMMT Nov 25 - - 93.3 9

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rXiv, 2024. [2] Ahmadian, Arash, et al. "Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms." arXiv, 2024. [3] Shao, Zhihong, et al. "Deepseekmath: Pushing the limits of mathematical reasoning in open language models." arXiv, 2024. [4] Yu, Qiying, et al. "Dapo: An open-source llm reinforcement learning system at scale." arXiv, 2025. [5] Liu, Zichen, et al. "Understanding r1-zero-like training: A critical perspective." arXiv, 2025. [6] Yao, Feng, et a

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EXAONE 4.0 32B model, and the open-weight models demonstrating the best performance in their class: gpt-oss-120b (hereafter GPT), Qwen3-235B-A22B-Thinking-2507 (hereafter Qwen3), DeepSeek-V3.2 (hereafter DeepSeek). K-EXAONE demonstrated outstanding competitiveness across diverse fields including knowledge, mathematics, and coding. It particularly achieved excellent results on the knowledge benchmark MMLU-Pro, proving its academic knowledge comprehension and reasoning abilities, and outperformed

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firmly among models up to five times its size while being capable of running locally on consumer hardware. Devstral 2 (123B) and Devstral Small 2 (24B) are 5x and 28x smaller than DeepSeek V3.2, and 8x and 41x smaller than Kimi K2—proving that compact models can match or exceed the performance of much larger competitors. Their reduced size makes deployment practical on limited hardware, lowering barriers for developers, small businesses, and hobbyists.hardware. Devstral 2 supports exploring code

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of SoTA Qwen3 thinking models under high pass@k settings even prior to explicit post-training for reasoning, and exceeds other strong reasoning models such as Phi4-reasoning, and Deepseek-R1-Distill. Building on our previous work with AMD, we have been deeply committed to de-risking end-to-end large-scale pretraining on AMD. To create the infrastructure required to train ZAYA1-base, Zyphra, AMD, and IBM collaborated closely to build a large-scale, reliable, and highly performant training cluste

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ior open-source SoTA models by more than 6% points. When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 (671B) and Qwen3 232B-A22B. In the table below, we also compare Devstral to closed and open models evaluated under any scaffold (including ones custom for the model). Here, we find that Devstral achieves substantially better performance than a number of closed-source alternatives. For example, Devstral

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ance, Mistral Medium 3 also surpasses leading open models such as Llama 4 Maverick and enterprise models such as Cohere Command A. On pricing, the model beats cost leaders such as DeepSeek v3, both in API and self-deployed systems. Additionally, Mistral Medium 3 can also be deployed on any cloud, including self-hosted environments of four GPUs and above. Mistral Medium 3 is designed to be frontier-class, particularly in categories of professional use. In the evaluations below, we use numbers rep

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a powerful base for accelerating progress. Note that Mistral Small 3 is neither trained with RL nor synthetic data, so is earlier in the model production pipeline than models like Deepseek R1 (a great and complementary piece of open-source technology!). It can serve as a great base model for building accrued reasoning capacities. We look forward to seeing how the open-source community adopts and customizes it. Performance Human Evaluations We conducted side by side evaluations with an external t

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