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Introducing Kimi K3.1 ProWe're introducing Kimi K3.1 Pro, the first model in our new K3.1 series and our most capable 3T-class open-weight model to date, with a 1-million-token context window.
It delivers substantial improvements across coding, 3D creation, financial research, and other agentic capabilities.
We are also releasing Kimi K3.1 Pro Highspeed, which delivers 2-3× the speed of K3.1 Pro for more responsive interactions. Kimi K3.1 Pro and Kimi K3.1 Pro Highspeed are available today on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API. The full model weights will be released by Nov 4. Advancing the Frontier of Open Intelligence Kimi K3.1 Pro is a post-trained version of Kimi K3, with substantially larger post-training compute.
To enable large-scale joint RL across hundreds of downstream domains, we optimized every layer of the stack — the RL framework, long-context inference, training efficiency, sandbox infrastructure, rollout scheduling, and a shared platform for environment quality checks — so that each increment of RL compute delivers stable, predictable returns.
As a result, K3.1 Pro achieves consistent gains in every domain.
Across coding, agentic, and knowledge benchmarks, K3.1 Pro matches or approaches the strongest proprietary models.
On Terminal-Bench 4.0, which evaluates coding agents on realistic, resource-constrained terminal tasks using strict task-specific tests, K3.1 Pro scores 65.7%, ahead of GPT-6 Astra (58.2%) and narrowly behind Claude Opus 5.5 (66.4%). And on Kimi Code Bench 3.0 Long-horizon, our internal benchmark that tests coding agents' multi-step execution, tool use, and end-to-end problem solving across diverse engineering domains, K3.1 Pro scores 61.2%, above K3's 55.95%.
Coding K3.1 Pro covers the full range of coding work, from quick fixes in real codebases to full-project builds. It also brings a more considered visual style to games and web experiences, and knows how to build them with the right tools. K3.1 Pro works effectively across game engines such as Unity, Unreal Engine, and Godot, combining them with web search and 3D generation to create complete experiences. Its front-end pages, interactive Three.js scenes, and 3D assets show stronger composition, typography, animation, and polish.
The examples below were created end to end by K3.1 Pro: K3.1 Pro's long-horizon coding capabilities are also strengthened through large-scale reinforcement learning. In one internal test, it built a human walking dynamics simulator with integrated training and evaluation pipelines on top of human-atlas, an open-source anatomical model of the human body comprising 296 skeletal components, including cartilage, and 402 muscle components.
Across 167 missions and 420 independent reviews, it analyzed experimental results, diagnosed problems, and iteratively refined the walking controller, enabling the simulated human to go from collapsing within seconds to walking continuously with alternating strides for 11.2 seconds. When reward shaping failed to improve arm motion, K3.1 Pro did not simply keep adjusting the reward: it ran open-loop probes of the simulator's dynamics, identified and corrected 36 errors in shoulder-muscle moment-arm calculations, and then retrained the controller.
3D Modeling and Design K3.1 Pro is strong at 3D creation and spatial reasoning.
On BenchCAD, which tests 3D reconstruction from multi-view renderings through CAD code generation, K3.1 Pro scores 92.7% with tools, approaching the performance of Claude Opus 5.5 (96.2%) and GPT-6 Astra (95.9%).
From a text brief, sketch, or reference image, it can infer shape, proportions, and spatial relationships to create usable 3D models.
It also has a stronger grasp of existing asset libraries and model collections.
K3.1 Pro particularly excels at parametric modeling, from mechanical parts and architectural models to configurable products and custom furniture. Without MCP connectors, it can write and run scripts for tools like Blender and CAD software to build and refine assets, edit components, and adjust geometry, materials, and scene layout.
Below are examples of 3D assets K3.1 Pro created for brand films, games, and interactive web experiences: 3D Make is now live on kimi.ai and the Kimi app — preview and edit your 3D models online, download them ready for 3D printing, or export them as videos or Live Photos to share anywhere. Finance With stronger reasoning and agentic capability, K3.1 Pro is capable of end-to-end delivery of complete financial workflows. K3.1 Pro achieves state-of-the-art results on OfficeQA Pro v2 (64.4), which tests complex fiscal data analysis over real financial documents through retrieval, cross-document verification, and multi-step numerical reasoning. K3.1 Pro also improves over K3 on Frontier Finance, which measures support for complete investment workflows from screening to profitability analysis. K3.1 Pro also improves significantly across all our internal finance benchmarks: Financial Modeling evaluates formula linkage, financial logic, and final workbook quality in real-world modeling tasks. K3.1 Pro improves on K3 by 7.2 points. Financial Research evaluates whether models can produce auditable research under evidence and cutoff constraints. K3.1 Pro improves on K3 by 11.6 points. Business Operation evaluates long-horizon management and decision-making in a simulated business environment. K3.1 Pro achieves about 4.2× K3's performance. Financial analysis and investment research demand a high degree of factual accuracy. To address this, we made targeted improvements to K3.1 Pro's factual accuracy on real-world tasks and challenging prompts. On Kimi Hallu Bench Hard bench, an internal benchmark designed to stress-test factual accuracy, its claim-level hallucination rate fell from 23.4% for K3 to 8.3%, while the share of responses containing at least one factual error was just 42.4% of its predecessor's rate. (The evaluation is a stress test for factual accuracy, with examples deliberately crafted to probe situations where models are prone to errors.
The reported rates do not represent overall error rates in daily use.)
The cases below show how K3.1 Pro handles financial modeling, investment analysis, and source-grounded research in practice: Cybersecurity As K3.1 Pro became more capable, we spent a correspondingly larger share of our effort understanding where cyber capability comes from, and how much of it we can control. We therefore analyzed and evaluated K3.1 Pro's vulnerability research capabilities, and applied targeted controls during training to reduce potential misuse risk while preserving general usefulness. Identifying and removing cyber-related data. During development, we observed vulnerability research performance improving steadily as post-training scaled. Tracing the source of this trend, we found that cyber-related content was embedded within the general mid-training corpus, and cyber-related tasks were present in the RL environment mix — a natural byproduct of training on broad data. In preliminary experiments on internal models, we found that without data removal at either the mid-training or the RL stage, downstream cyber capability would increase substantially.
These findings gave us a concrete lever for control.
For the open-weight release of K3.1 Pro, we identified and removed cyber-related data from both training stages, while retaining the general-purpose coding tasks needed for model usefulness. We will continue to apply appropriate controls at every training stage, of which cyber-related data identification and restriction is one example.
Evaluation and results.
After data cleaning, we evaluated K3.1 Pro on xdev-bench-hard, our in-house benchmark of expert-curated, source-code-based vulnerability discovery and exploitation tasks, calibrated to provide meaningful differentiation at our current capability level. Most tasks faithfully reproduce recently disclosed vulnerabilities in software such as the Linux kernel, Node.js, JavaScriptCore, Exim, and VirGL, with mitigations enabled. Each task provides the vulnerable source code, a short bug hint, and the corresponding binary; the agent must develop an exploit, achieve arbitrary code execution in a separate sandbox, and retrieve a secret string for automated verification. We report pass@3 over a random subset of runs, with no hard timeouts or output token limits. On xdev-bench-hard, the checkpoint without these risk-reduction measures — no cyber-related data removal at mid-training and no cyber-related RL environment filtering — solves 90.5% of problems at pass@3; the final K3.1 Pro reaches 81.0%.
The comparison shows that data removal meaningfully reduces cyber capability relative to what the same training pipeline would otherwise produce. At the same time, K3.1 Pro improves substantially over its predecessor, indicating that stronger general coding and long-horizon debugging capabilities generalize to cyber tasks even after targeted data removal.
K3.1 Pro's cyber capability remains behind that of frontier proprietary models that have been publicly available for at least three months. We will keep evolving our internal benchmarks so that evaluation stays informative as model capabilities continue to advance. Open-weight principles. These evaluations frame our open-weight release decision.
First, we aim not to release open weights that would extend the absolute frontier of cyber capability.
K3.1 Pro's offensive cyber capability is, by design, below what our pipeline can produce, and remains behind that of frontier proprietary models.
Second, we intend to progressively raise the cyber capability available in open weights. We believe making K3.1 Pro available in a non-discriminatory way benefits people who need dual-use capabilities for defensive purposes.