{"schemaVersion":"1.0","type":"Article","types":["Article"],"slug":"kimi-k3-1-pro-and-pro-highspeed-what-s-new-what-s-claimed-and-what-to-test-first-m6pow","url":"https://zyvop.com/kimi-k3-1-pro-and-pro-highspeed-what-s-new-what-s-claimed-and-what-to-test-first-m6pow","title":"Kimi K3.1 Pro and Pro Highspeed: What’s New, What’s Claimed, and What to Test First","subtitle":"A practical look at Kimi’s new open-weight model, its 2-3× faster Highspeed sibling, and which of the benchmark claims are worth checking on your own workload.","tldr":"Kimi K3.1 Pro is a 1M-context open-weight model with big claims in coding, 3D and finance, and Highspeed is the 2-3× faster version. Here’s what’s new, how to get it, and what I’d test first.","keywords":["AI news","llm-benchmarks","AI coding","open-weight models","Kimi K3.1 Pro","AI Models"],"entities":["Ankit Singh","Software Engineer","AI news","llm-benchmarks","AI coding","open-weight models","Kimi K3.1 Pro","AI Models","ZyVOP"],"keyTakeaways":["Moonshot AI just released Kimi K3.1 Pro, plus a faster sibling called K3.1 Pro Highspeed.","I read the full announcement and the K3 post it builds on, because the new numbers only make sense with that background.","One disclaimer before we start."],"headings":["The facts at a glance","Where K3.1 Pro comes from","Pro vs Highspeed: what we know and what we don’t","Coding","The walking simulator story","3D modeling and design","Finance","The hallucination numbers","Cybersecurity: the unusual section","How to get it","How I’d read these benchmarks","What I’d test first","Wrapping up"],"outboundLinks":["https://www.kimi.ai/blog/kimi-k3-1-pro","https://www.kimi.ai/blog/kimi-k3"],"contentText":"Moonshot AI just released Kimi K3.1 Pro, plus a faster sibling called K3.1 Pro Highspeed. I read the full announcement and the K3 post it builds on, because the new numbers only make sense with that background. One disclaimer before we start. I haven’t run these models myself. Everything below is what Kimi reports, and I’ll point out which numbers come from public benchmarks and which come from Kimi’s own internal tests. The short version: it’s K3 with a much bigger post-training pass, the weights are due by Nov 4, and it gets close to the top proprietary models on several coding and 3D tests. It also has one of the more unusual cybersecurity sections I’ve seen in a model launch. The facts at a glance Detail What Kimi says Model class “3T-class” open-weight model (K3 itself is 2.8T parameters) Context window 1 million tokens Highspeed variant 2-3× the speed of K3.1 Pro Available now Kimi.ai, Kimi Work, Kimi Code, Kimi API Open weights Full weights by Nov 4, 2026 API model names kimi-k3.1-pro and kimi-k3.1-pro-highspeed 💡 Quick note: Kimi calls K3.1 Pro the first model in its new K3.1 series. Where K3.1 Pro comes from To understand K3.1 Pro, you need to know what K3 is. Kimi K3 launched as a 2.8-trillion-parameter model with native vision and a 1M-token context window. It’s built on two architecture pieces Kimi calls Kimi Delta Attention and Attention Residuals, and its mixture-of-experts setup effectively activates 16 of 896 experts. According to Kimi, K3.1 Pro is a post-trained version of K3 with substantially more post-training compute. So the gains come from training, not from a new architecture. flowchart LR A[\"Kimi K3<br/>2.8T parameters<br/>1M context\"] -->|\"much larger post-training compute<br/>joint RL across hundreds of domains\"| B[\"Kimi K3.1 Pro<br/>open-weight, 1M context\"] B --- C[\"K3.1 Pro Highspeed<br/>2-3x the speed of Pro\"]Running reinforcement learning across hundreds of domains at once is messy. Kimi says it reworked every layer of its stack to make it work: the RL framework, long-context inference, training efficiency, sandbox infrastructure, rollout scheduling, and a shared platform that checks environment quality. Their claim is that each extra bit of RL compute gave stable, predictable returns, and that K3.1 Pro improved in every domain they measured. Pro vs Highspeed: what we know and what we don’t Here’s everything the announcement says about Highspeed: it runs 2-3× faster than K3.1 Pro, for more responsive interactions. That’s it. There are no separate benchmark numbers for Highspeed, no pricing, and no statement on whether quality matches Pro. Every benchmark in this post is for K3.1 Pro. ⚠️ Heads up: Don’t assume Highspeed scores the same as Pro. Until Kimi publishes numbers, treat that as something to test. Here’s how I’d pick between them. This is my own rule of thumb, not Kimi’s guidance. flowchart TD S[\"New task for the model\"] --> Q1{\"Is a human waiting<br/>on each reply?\"} Q1 -->|\"Yes: chat, pair coding, live tools\"| H[\"Try Highspeed first\"] Q1 -->|\"No: long agent runs, batch jobs\"| Q2{\"Is the task hard<br/>or long-horizon?\"} Q2 -->|\"Yes\"| P[\"Start with Pro\"] Q2 -->|\"No\"| H H --> T[\"Run both on your own tasks<br/>and compare quality, not just speed\"] P --> TCoding This is where Kimi leads with its strongest numbers. On Terminal-Bench 4.0, which tests coding agents on realistic, resource-constrained terminal tasks with strict task-specific tests, K3.1 Pro lands just behind Claude Opus 5.5: Model Terminal-Bench 4.0 Claude Opus 5.5 66.4% Kimi K3.1 Pro 65.7% GPT-6 Astra 58.2% The gap to Opus 5.5 is 0.7 points. On Kimi’s own Kimi Code Bench 3.0 Long-horizon, K3.1 Pro scores 61.2% against K3’s 55.95%. That one is an internal benchmark, so it tells you more about K3 → K3.1 progress than about rivals. Kimi also says K3.1 Pro works across Unity, Unreal Engine and Godot, and combines them with web search and 3D generation to build complete experiences. Its front-end pages and interactive Three.js scenes are described as having better composition, typography and animation. The walking simulator story The example I liked most is one of Kimi’s internal long-horizon tests. Kimi had K3.1 Pro build a human walking simulator on top of human-atlas, an open-source anatomical model with 296 skeletal components (cartilage included) and 402 muscle components. Over 167 missions and 420 independent reviews, the model analyzed results, diagnosed problems and kept refining the walking controller. It went from collapsing within seconds to walking with alternating strides for 11.2 seconds. Eleven seconds won’t win any races. The interesting part is how it got unstuck. When tweaking the reward function didn’t fix the arm motion, the model stopped tweaking. It ran open-loop probes of the simulator’s dynamics, found 36 errors in the shoulder-muscle moment-arm calculations, fixed them, and retrained the controller. That’s a debugging habit I’d want from a coding agent: question the setup before fiddling with the symptoms. 3D modeling and design Kimi says K3.1 Pro is strong at spatial reasoning and 3D creation. From a text brief, a sketch or a reference image, it can infer shape and proportions and produce usable 3D models. The public number here is BenchCAD, which tests 3D reconstruction from multi-view renderings through CAD code generation. Scores are Voxel IoU on a 1,000-case random subset of a 17,900-case dataset: Model With tools Without tools Claude Opus 5.5 96.2 73.0 GPT-6 Astra 95.9 not reported Kimi K3.1 Pro 92.7 72.8 Claude Fable 5.1 92.6 60.6 DeepSeek-V4.1-Flash-1017 81.6 41.2 Look at the “without tools” column. K3.1 Pro is 0.2 points behind Opus 5.5 there, and well ahead of Fable 5.1. With tools, the gap to the top two widens to 3-4 points. ⚠️ Not a perfectly level table: Kimi ran the open models (including K3.1 Pro and DeepSeek) itself on the 1,000-case subset. The Claude scores come from Anthropic’s Opus 5.5 system card, and GPT-6 Astra’s with-tools score comes from OpenAI’s report. So you’re comparing Kimi’s runs with other labs’ published numbers. Kimi says the model is particularly good at parametric modeling: mechanical parts, architectural models, configurable products, custom furniture. It can write and run scripts for tools like Blender and CAD software to build and edit assets, without needing MCP connectors. There’s also a product angle. 3D Make is now live on kimi.ai and the Kimi app. You can preview and edit models online, download them ready for 3D printing, or export them as videos or Live Photos. Finance Finance is the second big area Kimi pushes. The headline: K3.1 Pro scores 64.4 on OfficeQA Pro v2, which Kimi describes as state-of-the-art. That benchmark tests fiscal data analysis over real financial documents, with retrieval, cross-document checks and multi-step math. It also scores 58.6 on Frontier Finance. Kimi’s internal finance benchmarks show bigger jumps over K3: Internal benchmark K3 K3.1 Pro Financial Modeling 50.4 57.6 Financial Research 58.8 70.4 Business Operation 8.9 37 The Business Operation result (long-horizon decision-making in a simulated business) is about 4.2× K3’s score. It’s the biggest relative jump in the whole post, and it’s an internal test, so I’d read it as direction rather than proof. The hallucination numbers Finance work needs accuracy, so Kimi made a point of improving factual reliability. On its internal Kimi Hallu Bench Hard, the claim-level hallucination rate fell from 23.4% (K3) to 8.3%. The share of responses with at least one factual error dropped to 42.4% of K3’s rate. Here’s how the “Hard, with tools” results compare (lower is better): Model Hallucination rate GPT-6 Astra 5.5% Kimi K3.1 Pro 8.3% Claude Opus 5.5 15.0% GLM-5.3 22.6% DeepSeek-V4.1-Flash-1017 23.3% Claude Fable 5.1 23.3% ⚠️ Read this carefully: Kimi itself says this is a stress test, built from examples designed to trip models up. These rates are not everyday error rates. And GPT-6 Astra still scores lower than K3.1 Pro here. Cybersecurity: the unusual section Cyber capability is a touchy subject for open-weight releases, and Kimi gave it a full section with its own numbers. During development they noticed that vulnerability-research skill kept improving as post-training scaled. When they traced it back, they found cyber-related content in the general mid-training data, and cyber-related tasks in the RL environment mix. Both were side effects of training on broad data. So for the open-weight release, they removed cyber-related data from both stages, while keeping the general-purpose coding tasks the model needs to stay useful. flowchart LR A[\"Cyber-related content in<br/>mid-training corpus\"] --> C[\"Removed for the<br/>open-weight release\"] B[\"Cyber tasks in the<br/>RL environment mix\"] --> C C --> D[\"xdev-bench-hard pass@3<br/>90.5% down to 81.0%\"]They tested this on xdev-bench-hard, an in-house benchmark of expert-curated vulnerability discovery and exploitation tasks. Most tasks reproduce recently disclosed vulnerabilities in software like the Linux kernel, Node.js, JavaScriptCore, Exim and VirGL, with mitigations turned on. Model xdev-bench-hard (pass@3) Kimi checkpoint without the cyber risk-reduction steps 90.5% DeepSeek-V4.1-Flash 85.7% Kimi K3.1 Pro (final) 81.0% GLM-5.3 57.1% Kimi K3 33.3% Two things stand out to me. First, the data removal had a measurable effect: 90.5% down to 81.0% on the same pipeline. Second, K3.1 Pro still jumped a lot over K3 (33.3% to 81.0%), which Kimi says shows that stronger general coding and long-horizon debugging carry over into cyber tasks even after the removal. Kimi also says K3.1 Pro’s cyber capability still sits behind frontier proprietary models that have been publicly available for at least three months. Kimi’s stated principles for open weights: Don’t release weights that extend the absolute frontier of cyber capability. Progressively raise the cyber capability available in open weights. Make the model available in a non-discriminatory way, since defenders also need dual-use tools, and open models let more teams audit their own code, including legacy systems that rarely get security reviews. You can agree or disagree with that reasoning. Either way, it’s a clear position, and the benchmark is internal, so it’s Kimi’s word until others test it. How to get it Channel How to use K3.1 Pro Kimi.ai and mobile apps Update the app (iOS, Android, HarmonyOS) or visit kimi.ai Kimi Work Desktop app version 3.1.0 or later (Windows and Apple silicon Macs) Kimi Code Run it and pick the model with the /model command Kimi API Select kimi-k3.1-pro or kimi-k3.1-pro-highspeed on the API platform; Zero Data Retention is available Kimi Business Enterprise admins can enable it; access is off by default at launch What about pricing? The announcement doesn’t list K3.1 pricing. For reference, K3’s API launched at $0.30 per million tokens for cache-hit input, $3.00 for cache-miss input and $15.00 for output. Check the API platform for current K3.1 rates before budgeting anything. What about self-hosting? Weights arrive by Nov 4. This is a 3T-class model, so it’s a cluster-level job. K3’s own post recommended supernode setups with 64 or more accelerators. K3.1 hasn’t published its own guidance yet. How I’d read these benchmarks A few things to keep in mind before quoting any of these numbers. Max effort. K3.1 Pro results use reasoning effort set to “max”, temperature 1.0 and top-p 0.95. Your speed and token cost will depend on the settings you pick. Harness matters. Several coding results use the Claude Code harness for K3.1 Pro. Competitor scores are often taken from their own reports or public leaderboards, so setups aren’t always identical. Internal benchmarks. Kimi Code Bench, Hallu Bench, the internal finance tests and xdev-bench-hard are Kimi’s own. Useful for tracking progress, hard to verify from outside. Zero-score rules. On OfficeQA Pro, Kimi says it saw some external models pull reference answers from the web, and it scored those tasks zero. On Agents’ Last Exam, it zeroes any run that bypasses the task’s stated requirements. Both are reasonable rules, but they’re Kimi’s rules, and they matter for the “state-of-the-art” claim on OfficeQA Pro v2. Carried-over quirks. K3’s post listed limits: quality can get unstable if the harness doesn’t pass back the full thinking history (or if you switch to K3 mid-session), and the model can be overly proactive on ambiguous tasks. The K3.1 post doesn’t repeat these. Since K3.1 Pro is post-trained from K3, I’d keep them in mind until proven otherwise. What I’d test first If I had access today, this is the order I’d go in: Pro vs Highspeed on the same 10-20 real tasks. Compare quality and latency side by side. A long-horizon coding task. Something that takes many steps in a real repo, using Kimi Code, which K3’s post recommended for verified compatibility. A document-heavy finance or research task. Check every claim against the source documents. The hallucination improvement is the claim that matters most to me. Ambiguous instructions. See how often it makes decisions on its own when the prompt is vague, and tighten your system prompt or AGENTS.md if it does. Self-hosting plans. Wait for the weights and deployment notes before planning hardware. Wrapping up K3.1 Pro looks like a solid step for open-weight models: close to the top proprietary models on Terminal-Bench 4.0 and BenchCAD, a big claimed drop in hallucinations, and an open and detailed explanation of how Kimi handled cyber risk. The open questions are the ones Kimi hasn’t answered yet. How good is Highspeed, really? What does K3.1 cost? How does it behave outside Kimi’s own harnesses? Those are things you only find out by running it. The weights land by Nov 4, so more independent results should follow soon. Sources: Introducing Kimi K3.1 Pro Kimi K3: Open Frontier Intelligence","contentHash":"sha256:c2ccb087df3a837d4899f1cd48e3aee35a1d8f5120d40c3ea37a9327dcc33fb5","authorName":"Ankit Singh","authorUrl":"https://zyvop.com/author/ankit","authorSameAs":[],"category":"AI Models","tags":["AI news","llm-benchmarks","AI coding","open-weight models","Kimi K3.1 Pro"],"audience":"Software engineers and developers building applications with AI Models","tone":"Professional, software engineer perspective","readingTimeMinutes":10,"wordCount":2460,"faqs":null,"primaryTopic":"AI Models","publishedAt":"2026-10-24T03:39:07.588Z","updatedAt":"2026-10-24T03:39:07.588Z","canonicalUrl":"https://zyvop.com/kimi-k3-1-pro-and-pro-highspeed-what-s-new-what-s-claimed-and-what-to-test-first-m6pow"}