AI 模型和平台
Moonshot AI 发布 Kimi K3.1 Pro，新K3.1的第一种型号系列
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Moonshot AI 推出 Kimi K3.1 维基文库中相关的原始文献：维基文库中相关的原始文献：维基文库中相关的原始文献：维基文库中相关的原始文献：维基文库中相关的原始文献：Pro于2026年10月24日，是其新K3.1系列的首款型号，与名为Kimi K3.1 Pro高速的更快的配套变体并列。 公司将K3.1 Pro描述为它迄今为止最有能力的3T级开放量级模型，拥有100万托肯上下文窗口。
根据公告，K3.1 Pro高速的交付速度是具有相同模型能力的K3.1 Pro的2-3倍。 两种型号均可在Kimi产品家族中找到：Kimi.ai和Kimi移动应用为iOS,Android，和HarmonyOS;Kimi工作桌面应用为3.1.0版本或以后，为Windows和Apple硅Macs；在Kimi代码中，用户用/模型命令选择该型号；通过Kimi API，该型号被列为kimi-k3.1-pro和kimi-k3.1-pro-高速，其中零数据保留为API 客户。 Kimi 企业管理员可以启用 K3.1 为他们的组织提供Pro，默认在发布关闭。
培训和报告业绩
K3.1 Pro是Kimi K3的训练后版本，由Moonshot AI描述为实质性更大的训练后计算而建造。 为了能够在数百个下游域进行大规模联合强化学习，称公司优化了堆积层的每一层：强化学习框架，长文本推论，培训效率，沙盒基础设施，推出时间表，以及环境质量检查共享平台。
该公司报告说，在对agents进行现实、资源紧张的终端任务进行编码的终端-Bench 4.0上，K3.1 Pro的得分为65.7%，领先GPT-6 Astra58.2%，小于Claude Opus5.566.4%。 Kimi代码座椅3. K3.1 Pro对K355.95%的得分为61.2%。 在通过CAD代码生成测试多视图渲染的3D重建的BeningCAD上，K3.1 Pro用工具得分92.7%，以96.2%接近Claude Opus 5.5，以95.9%接近GPT-6 Astra. 一个3D Make 功能在 kimi.ai 和 Kimi app 上直播，让用户在线预览和编辑3D模型，并下载准备用于3D打印。
Moonshot AI还报告OfficeQA Pro v2的最先进分数为64.4，测试了对真实金融文件的复杂财政数据分析，并引证了整个内部金融的K3收益基准测试：金融建模7.2分，金融研究11.6分，以及K3在业务运营方面的业绩约4.2倍。 在Kimi Hallu Bench Hard，即对事实准确性的内部压力测试中，模型的声称水平幻觉率从K3的23.4%下降到8.3%，而至少包含一个事实错误的答复比例是其前身的42.4%。 指出博客表示，评价是故意用来探寻容易出错的情况的，报告的比率并不代表日常使用中的总体误差率。
在博客描述的一次内部长视距编码测试中，K3.1 Pro在人-阿特拉斯之上建造了人类行走动态模拟器，即由296个骨骼成分和402个肌肉成分组成的人体开源解剖模型。 在167次任务和420次独立审查中，模型迭代地完善了一个行走控制器；当奖赏造型未能改进手臂运动时，它对模拟器的动力学进行了打开式探测，发现并纠正了肩部肌肉瞬间臂计算中的36个错误，并对控制器进行了重新训练。
网络安全控制和开放-重量原则
Moonshot AI 称注意到，随着培训后规模的扩大，脆弱性研究的绩效稳步改善，并追踪了在强化学习环境组合中，与网络有关的内容在一般培训中期和与网络有关的任务中嵌入的趋势。 对于K3.1 Pro的开放量发布，称公司在保留模型实用性所需的通用编码任务的同时，确定并移除了两个培训阶段的网络相关数据。
在Xdev-bench-hard上，公司内部的基准测试基于专家验证的源码脆弱性发现和开发任务，一个没有这些降低风险措施的检查站在通过@3时解决了90.5%的问题，而最后的K3.1 Pro则达到81.0%，博客报道。 Moonshot AI 称的结果显示，数据删除比同一培训管道本来会产生的网络能力有实际意义地降低了，K3.1 Pro的网络能力仍然落后于已经公开至少三个月的前沿专利模型。
博客将开放量发布背后的两条原则框架。 第一，该公司的目的不是释放开放权重，因为开放权重将扩展网络能力的绝对前沿。 第二，它打算逐步提高开放权重现有的网络能力，指出非歧视性的接入为更多的团队提供了审计其维持的代码的手段，包括很少接受专门安全审查的遗留系统。
Kimi 代码中的模型配置
分离 Kimi 代码文档详细介绍了这些模型如何被开发者曝光。 Docs列出横跨K3.1 Pro,K3,K2.8预览，和K2.7代码高速家族的6个型号ID，将k3.1-pro描述为3T参数旗舰，上下文窗口最多为1,048,576令牌，供上级成员使用，并描述高速版本具有约两倍配额使用量的相同能力。 K3.1 Pro根据页面在Kimi代码中全面推出。
根据docs,K3.1 Pro模型只支持思维模式，有思维障碍的请求以HTTP 400错误被拒绝。 两种新模型都接受低，高，或最大，以最大为默认的推理努力设置，同时接受图像和视频输入。 在新的会员计划下，k3.1-pro需要Plus及以上，为Pro及以上预留了1M上下文窗口，而k3.1-pro-高速则需要Pro及以上；在遗留计划下，对应的层级是Moderato和Allegretto.
文档建议开始一个新的会话，而不是切换到新的模式的中会话，使用本地响应，聊天完成，或消息API来进行最稳定的输出，并在第三方工具中输入精确的模型ID，因为一个错误的ID会返回到K2.8预览而不思考。
Kimi K3.1 Pro的完整型号重量将在2026年11月4日之前为发布，即公司称。
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AI Models & Platforms
Moonshot AI Releases Kimi K3.1 Pro, First Model in New K3.1 Series
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Moonshot AI introduced Kimi K3.1 Pro on October 24, 2026, the first model in its new K3.1 series, alongside a faster companion variant called Kimi K3.1 Pro Highspeed. The company describes K3.1 Pro as its most capable 3T-class open-weight model to date, with a 1-million-token context window.
According to the announcement, K3.1 Pro Highspeed delivers two to three times the speed of K3.1 Pro with identical model capabilities. Both models are available across the Kimi product family: on Kimi.ai and the Kimi mobile apps for iOS, Android, and HarmonyOS; on the Kimi Work desktop application, version 3.1.0 or later, for Windows and Apple silicon Macs; in Kimi Code, where users select the model with the /model command; and through the Kimi API, where the models are listed as kimi-k3.1-pro and kimi-k3.1-pro-highspeed, with Zero Data Retention available for API customers. Kimi Enterprise administrators can enable K3.1 Pro for their organizations, with access off by default at launch.
Training and Reported Performance
K3.1 Pro is a post-trained version of Kimi K3, built with what Moonshot AI describes as substantially larger post-training compute. To enable large-scale joint reinforcement learning across hundreds of downstream domains, the company said it optimized every layer of the stack: the reinforcement learning framework, long-context inference, training efficiency, sandbox infrastructure, rollout scheduling, and a shared platform for environment quality checks.
The company reports that on Terminal-Bench 4.0, which evaluates coding agents on realistic, resource-constrained terminal tasks, K3.1 Pro scored 65.7 percent, ahead of GPT-6 Astra at 58.2 percent and narrowly behind Claude Opus 5.5 at 66.4 percent. On Kimi Code Bench 3.0 Long-horizon, an internal benchmark covering multi-step execution, tool use, and end-to-end problem solving, K3.1 Pro scored 61.2 percent against K3’s 55.95 percent. On BenchCAD, which tests 3D reconstruction from multi-view renderings through CAD code generation, K3.1 Pro scored 92.7 percent with tools, approaching Claude Opus 5.5 at 96.2 percent and GPT-6 Astra at 95.9 percent. A 3D Make feature is live on kimi.ai and the Kimi app, letting users preview and edit 3D models online and download them ready for 3D printing.
Moonshot AI also reports a state-of-the-art score of 64.4 on OfficeQA Pro v2, which tests complex fiscal data analysis over real financial documents, and cites gains over K3 across its internal finance benchmarks: 7.2 points on Financial Modeling, 11.6 points on Financial Research, and about 4.2 times K3’s performance on Business Operation. On Kimi Hallu Bench Hard, an internal stress test for factual accuracy, the model’s claim-level hallucination rate fell from 23.4 percent for K3 to 8.3 percent, while the share of responses containing at least one factual error was 42.4 percent of its predecessor’s rate. The blog notes that the evaluation is deliberately crafted to probe error-prone situations and that the reported rates do not represent overall error rates in daily use.
In one internal long-horizon coding test described in the blog, K3.1 Pro built a human walking dynamics simulator on top of human-atlas, an open-source anatomical model of the human body comprising 296 skeletal components and 402 muscle components. Across 167 missions and 420 independent reviews, the model iteratively refined a walking controller; when reward shaping failed to improve arm motion, it ran open-loop probes of the simulator’s dynamics, identified and corrected 36 errors in shoulder-muscle moment-arm calculations, and retrained the controller.
Cybersecurity Controls and Open-Weight Principles
Moonshot AI said it observed vulnerability-research performance improving steadily as post-training scaled, and traced the trend to cyber-related content embedded within the general mid-training corpus and cyber-related tasks in the reinforcement learning environment mix. For the open-weight release of K3.1 Pro, the company said it identified and removed cyber-related data from both training stages while retaining the general-purpose coding tasks needed for model usefulness.
On xdev-bench-hard, the company’s in-house benchmark of expert-curated, source-code-based vulnerability discovery and exploitation tasks, a checkpoint trained without those risk-reduction measures solves 90.5 percent of problems at pass@3 while the final K3.1 Pro reaches 81.0 percent, the blog reports. Moonshot AI said the results show data removal meaningfully reduces cyber capability relative to what the same training pipeline would otherwise produce, and that K3.1 Pro’s cyber capability remains behind that of frontier proprietary models that have been publicly available for at least three months.
The blog frames two principles behind the open-weight release. First, the company aims not to release open weights that would extend the absolute frontier of cyber capability. Second, it intends to progressively raise the cyber capability available in open weights, stating that non-discriminatory access gives more teams the means to audit the code they maintain, including legacy systems that rarely receive dedicated security reviews.
Model Configuration in Kimi Code
Separate Kimi Code documentation details how the models are exposed to developers. The docs list six model IDs across the K3.1 Pro, K3, K2.8 Preview, and K2.7 Code Highspeed families, describe k3.1-pro as a 3T-parameter flagship with a context window of up to 1,048,576 tokens for higher-tier members, and describe the Highspeed version as carrying the same capabilities with about twice the quota usage. K3.1 Pro is fully rolled out in Kimi Code, according to the page.
According to the docs, K3.1 Pro models support thinking mode only, and requests with thinking disabled are rejected with an HTTP 400 error. Both new models accept reasoning_effort settings of low, high, or max, with max as the default, and both accept image and video input. Under new membership plans, k3.1-pro requires Plus and above, with the 1M context window reserved for Pro and above, while k3.1-pro-highspeed requires Pro and above; under legacy plans, the corresponding tiers are Moderato and Allegretto.
The documentation recommends starting a new session rather than switching to the new models mid-session, using the native Responses, chat completion, or message APIs for the most stable output, and entering exact model IDs in third-party tools, because a mistyped ID falls back to K2.8 Preview without thinking.
Full model weights for Kimi K3.1 Pro will be released by November 4, 2026, the company said.
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