Moonshot AI仅有发布 Kimi K3.1 Pro，是其K3.1系列的第一种型号。 Kimi K3.1 号 Pro是3T级的开量*型，带有100万托肯上下文窗口。 Moonshot团队将其定位在最强的专有模型上，在编码，代理和知识上基准测试。 对开发者来说，它比K3和开放权重的价格便宜，但还没有可用。 *开放权重预计2026年11月发布。
TL；数据
- 大小：2.8T 共计，104B活性参数(MOE),1M-token上下文。
- 运行在：Kimi API今天。 对于K3,Moonshot推荐64+加速器的超节点，带有MXFP4重量和MXFP8激活。 不是单一的GPU模型。
- 性能：在终端编码方面靠近专有边界；在科学终端任务方面紧随其后。
- 最佳：SWE-Marathon 51.7，领先Kimi图中的每个模型(自报).
- 最糟糕的是：终极-奔驰科学0.1,38.1,Kimi图中的GPT-6 Astra为68.1。
什么是Kimi K3.1 Pro? (美国英语).
Kimi K3.1 号机车 Pro是训练后Kimi K3的版本，其训练后计算量要大得多。 Moonshot团队在数百个下游领域联合开展强化学习。 为了支持这一点，Moonshot团队优化了RL框架，长文本推论，沙盒基础设施和推出调度。
基础K3建筑采用Kimi Delta注意力(KDA)和注意力残余(AttnRes). 它通过稳定LatetME框架激活了896名专家中的16名。 该模型通过API接受文本，图像和视频，按照快速启动文件进行。
Moonshot团队还运送了Kimi K3.1 Pro高速。 根据官方的Moonshot博客，其运行速度为2至3x更快。
Kimi K3.1 Pro在基准测试上表现如何？.
以下所有数字均由Moonshot在最大推理努力下自行报告，多为Claude Code牵引。
- 编码：在终端-奔驰4.0,K3.1 Pro得分65.7%. 这恰好是Claude Opus 5.5的后面，在SWE-马拉松上，它领先于51.7，在46.3领先Opus 5.5. Kimi代码座椅3. Long-horison)，是一款内部基准测试，比K3的55.95高61.2分。
- 科学：终端-奔驰科学0.1是明显的缺口。 K3.1 Pro的成绩为38.1分，GPT-6 Astra的成绩为68.1分，Kimi的成绩为63.3分，Opus 5.5分。
- 3D和CAD：用工具在BeningCAD上，K3.1 Pro得分92.7. Opus 5.5得分96.2和GPT-6 Astra 95.9. 没有工具，K3.1 Pro得分72.8，接近Opus 5.5以73.0.
- 财务：K3.1 Pro在Office QA Pro v2上报告一个最先进 64.4。 它在边境金融方面得分58.6 在内部财务测试方面，它在建模方面获得了7.2分，在K3研究方面获得了11.6分。
- 幻觉：在Kimi Halu Bench Hard上，一个内部压力测试，声称的幻觉从23.4%(K3)下降到8.3%。 这大概是Moonshot引用的65%。 GPT-6 Astra仍然在同一测试中贴出较低的5.5%.
Kimi K3.1怎么样？处理网络风险？
Moonshot从中训和RL中移除了与网络相关的数据，用于公开量产发布。 在其内部的xdev-bench-hard上，一个未过滤的检查站在通过@3时解决了90.5%的问题。 运出的 K3.1 Pro 解决了 81.0%。 Moonshot 称它在进攻性网络任务上停留在前沿专有模式后
开发者如何访问 Kimi K3.1 Pro?
该型号在Kimi.ai,Kimi Work,Kimi代码和Kimi API上直播。 API的ID是kimi-k3.1-pro和kimi-k3.1-pro-高速。 端点为OpenAI兼容。
K3.1 定价 Pro为每百万令牌2美元投入和10美元产出。 缓存达到成本0.20美元。 高速耗资4.00元投入，20.00元产出。 K3本身的价格是3美元和15美元。
快速启动的实际限制：
- 思维模式是永远的合理努力低，高或最大(默认最大).
- 温度1.0和最高0.95。
- 输出默认值为131 072个令牌，最多可达1 048 576个。
- 最少加1美元就打开了
- 视觉输入不接受公共图像URL. 使用基数64或上传的文件ID.
如何与竞争模式比较？
* 图取自Kimi的比较图；在竞争对手自己的页面上未确认。
有什么限制？
体重还没有公之于众所以今天不能自我接待
- 2.8T大小指多节点，超节点级服务。
- 科学-重型终端工作跟踪GPT-6 Astra和Opus 5.5乘25+点。
- 几个声称的胜利来自内部的基准测试。
- 对于K3,Moonshot警告说，把推理历史放进绳索会破坏输出。 K3.1 Pro Docs 也说要返回完整的助理消息。
关键外卖
- Kimi K3.1 Pro:2.8T共计，104B活动，1M上下文。
- 终点站 - Bench 4.0,65.7%，落后Opus 5.5的0.7点。
- 领先SWE-马拉松51.7；末端-奔驰科学(38.1)的踪迹差。
- API每百万令牌花费2美元/10美元，比K3便宜。
- 开放权重预计2026年11月开通。
请检查access-date=中的日期值 (帮助) 官方科技博客，API快速启动和X上的发布帖子。 所有功劳都归功于这个项目的研究人员。 并加入我们的通讯。 等等你在电报里吗现在你也可以加入我们的电报。
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Asif Razzaq是Marktechpost AI Media Inc.的首席执行官。 作为一个有远见的企业家和工程师，阿西夫致力于利用人工智能的潜力促进社会福祉。 他最近的努力是人工智能媒体平台的发布，该平台以深入报道机器学习和深层学习新闻而著称，这些新闻在技术上既可靠又为广大观众所容易理解。 该平台每月有超过200万次观看，表明其受观众欢迎。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Moonshot AI has just released Kimi K3.1 Pro, the first model in its K3.1 series. Kimi K3.1 Pro is a 3T-class open-weight* model with a 1-million-token context window. Moonshot team positions it against the strongest proprietary models on coding, agentic and knowledge benchmarks. For developers, it is cheaper API pricing than K3 and open weights yet not available. *open weights are expected to release in November 2026.
TL;DR
- Size: 2.8T total, 104B active parameters (MoE), 1M-token context.
- Runs on: Kimi API today. For K3, Moonshot recommends supernodes of 64+ accelerators, with MXFP4 weights and MXFP8 activations. Not a single-GPU model.
- Performance: Near the proprietary frontier on terminal coding; behind it on scientific terminal tasks.
- Best: SWE-Marathon 51.7, ahead of every model in Kimi’s chart (self-reported).
- Worst: Terminal-Bench Science 0.1 at 38.1, versus 68.1 for GPT-6 Astra in Kimi’s chart.
What is Kimi K3.1 Pro?
Kimi K3.1 Pro is a post-trained version of Kimi K3 with much larger post-training compute. Moonshot team ran joint reinforcement learning across hundreds of downstream domains. To support this, the Moonshot team optimized the RL framework, long-context inference, sandbox infrastructure and rollout scheduling.
The base K3 architecture uses Kimi Delta Attention (KDA) and Attention Residuals (AttnRes). It activates 16 of 896 experts through a Stable LatentMoE framework. The model accepts text, images and video through the API, per the quickstart docs.
Moonshot team also shipped Kimi K3.1 Pro Highspeed. According to the official Moonshot blog, it runs 2 to 3x faster.
How does Kimi K3.1 Pro perform on benchmarks?
All numbers below are self-reported by Moonshot at max reasoning effort, mostly with the Claude Code harness.
- Coding: On Terminal-Bench 4.0, K3.1 Pro scores 65.7%. That is just behind Claude Opus 5.5 at 66.4% in Kimi’s chart. On SWE-Marathon it leads at 51.7, ahead of Opus 5.5 at 46.3. On Kimi Code Bench 3.0 Long-horizon, an internal benchmark, it scores 61.2, up from K3’s 55.95.
- Science: Terminal-Bench Science 0.1 is the clear gap. K3.1 Pro scores 38.1, versus 68.1 for GPT-6 Astra and 63.3 for Opus 5.5 in Kimi’s chart.
- 3D and CAD: On BenchCAD with tools, K3.1 Pro scores 92.7. Opus 5.5 scores 96.2 and GPT-6 Astra 95.9. Without tools, K3.1 Pro scores 72.8, close to Opus 5.5 at 73.0.
- Finance: K3.1 Pro reports a state-of-the-art 64.4 on OfficeQA Pro v2. It scores 58.6 on Frontier Finance. On internal finance tests, it gains 7.2 points in modeling and 11.6 in research over K3.
- Hallucination: On Kimi Hallu Bench Hard, an internal stress test, claim-level hallucinations fell from 23.4% (K3) to 8.3%. That is roughly the 65% drop Moonshot cites. GPT-6 Astra still posts a lower 5.5% on the same test.
How does Kimi K3.1 Pro handle cyber risk?
Moonshot removed cyber-related data from mid-training and RL for the open-weight release. On its internal xdev-bench-hard, an unfiltered checkpoint solved 90.5% at pass@3. The shipped K3.1 Pro solves 81.0%. Moonshot says it stays behind frontier proprietary models on offensive cyber tasks.
How can developers access Kimi K3.1 Pro?
The model is live on Kimi.ai, Kimi Work, Kimi Code and the Kimi API. The API IDs are kimi-k3.1-pro and kimi-k3.1-pro-highspeed. The endpoint is OpenAI-compatible.
Pricing for K3.1 Pro is $2.00 input and $10.00 output per million tokens. Cache hits cost $0.20. Highspeed costs $4.00 input and $20.00 output. K3 itself was priced at $3.00 and $15.00.
Practical limits from the quickstart:
- Thinking mode is always on. Reasoning effort is low, high or max (default max).
- Temperature 1.0 and top-p 0.95 are fixed.
- Output defaults to 131,072 tokens and can go up to 1,048,576.
- Access unlocks after a minimum $1 top-up.
- Vision input does not accept public image URLs. Use base64 or uploaded file IDs.
How does it compare with competing models?
*Figure taken from Kimi’s comparison chart; not confirmed on the competitor’s own page.
What are the limitations?
- Weights are not public yet, so self-hosting is not possible today.
- The 2.8T size points to multi-node, supernode-class serving.
- Science-heavy terminal work trails GPT-6 Astra and Opus 5.5 by 25+ points.
- Several claimed wins come from internal benchmarks.
- For K3, Moonshot warned that dropping reasoning history in a harness destabilizes output. The K3.1 Pro docs also say to return the full assistant message.
Key Takeaways
- Kimi K3.1 Pro: 2.8T total, 104B active, 1M context.
- Terminal-Bench 4.0 at 65.7%, 0.7 points behind Opus 5.5.
- Leads SWE-Marathon at 51.7; trails badly on Terminal-Bench Science (38.1).
- API costs $2/$10 per million tokens, cheaper than K3.
- Open weights are expected in November 2026.
Check out the official tech blog, the API quickstart and the launch post on X. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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