Day 0 support: Kimi K3.1 Pro on AMD Instinct MI355X 2.8T params, 1M context, open weights. Runs on 8x MI355X with ATOM and vLLM. Recipes, kernels and benchmarks:

Today we're introducing Verda Inference, in preview, with day-0 access to Kimi K3.1 Pro, in partnership with @Kimi_Moonshot 🧵 Frontier open-weight models through one API, engineered for every request, from a single prompt to long-horizon agents.

Kimi K3.1 Pro lands on Nebius Token Factory from day 0. Moonshot’s latest open-weight model delivers coding performance close to Opus 5.5 on Terminal-Bench 4.0. Build the app you’ve been imagining. Turn financial reports into a working model. Bring an interactive 3D world to life. Build with Kimi K3.1 Pro →

Kimi now works with Gmail, Google Drive, and Google Calendar. Summarize Gmail threads, find files in Google Drive, and plan your day with Google Calendar — with secure OAuth access you control. Try them at

We're sorry for the rate limits and unnecessary quota consumption on the K3.1 Pro subscription plan. These were our issues, and we should have fixed them sooner. Today, every subscriber on every plan gets a one-time usage reset, restoring 25% of their monthly quota (usage below 25% resets to zero), equal to one week of usage. Your usage bar will reflect this adjustment by end of day PST. We've also adjusted the plan's parameters and are reducing unnecessary subagent calls and output length, so your quota goes further. Thank you for your patience while we put this right.

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%).

Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model. 🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal. 🔹 Full-project coding: games in Unreal, Unity & Godot. 🔹 Excels at parametric 3D modeling in Blender & CAD. 🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%. 🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks. Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi Work, Kimi Code, and the Kimi API. Open weights by Nov 4, 2026. 🔗 API: 🔗 Tech blog:

Automate Your Workflows with Kimi Work - Tutorial #3# See how Kimi fits into your daily workflows: - Work across your local projects, browser, and computer. - Break down complex tasks using Swarm; iterate in Goal mode. - Customize how Kimi works with Plugins, AGENTS.md and Dashboard. What should we cover next? Let us know in the comments!

Meet the new Kimi Browser Extension, formerly Kimi WebBridge. From your browser sidebar, you can chat with Kimi to navigate websites, fill out forms, and get things done. For repetitive tasks, record your steps once and save them as a skill. Kimi can take it from there next time. Available now on and the Chrome Web Store.

Kimi K3 is now on Amazon Bedrock! Run coding, document analysis, and extended agent workflows with Bedrock's access, encryption, and auditing controls. Explicit prompt caching supported. Start building with K3 on AWS 👉

Since Kimi K3 launched in July, it’s become one of the fastest-growing models we serve through Runpod’s public endpoints. Through our @Kimi_Moonshot partnership, you can now call the managed endpoint or deploy it yourself on an 8xB300 pod. Start with the model. Move closer to the infra when the workload calls for it.

Kimi K3 is now in Cursor! It scores close to the frontier on CursorBench. It's available on US-based inference thanks to our partners Fireworks, Together, and Baseten. Zero Data Retention is also supported.

Introducing Tenet, our first model post-trained for legal. Tenet is a Kimi K3 base that we post-trained with @FireworksAI_HQ on a corpus of publicly available legal data, synthetic data, and human expert data simulating long-horizon legal work. Training increases Tenet's all-pass rate by 82% on LAB and 22% on LAB Contracts relative to the Kimi K3 base model. It achieves state-of-the-art performance on LAB Contracts and places second on LAB. These gains generalize to other leading agentic benchmarks including @mercor's Apex Agents - Corporate Law, @crosbylegal's Redline Bench, and @scale_AI's Professional Reasoning Bench. Tenet is also optimized for token efficiency, operating at less than a fourth the cost of leading foundation models. We additionally post-trained three specialist models for Tenet to use as subagents: 1) M&A Diligence: post-trained with @baseten on our LAB Diligence environment in an RLM harness, this model is optimized for high-scale, long-horizon tasks. 2) Review Tables: trained with @appliedcompute on our Review Table environment, this model is state-of-the-art and cost-effective at high-volume document review and structured data extraction. 3) Firm Knowledge: trained with @EngramLab on our synthetic law firm environment, this model is optimized to learn and search over a firm's knowledge via memory and structured notes. More details on model training, environment design, benchmarking, results, and more in the article by @gabepereyra below. What's next for Harvey’s research? - Scaling LAB to more jurisdictions, practice areas and workflows - Scaling compute to bring new generalist models and capabilities to Harvey More to come soon.

Pareto frontier is live in Agent Arena! Dive into model performance on real-world agentic tasks, compared to the median cost per task. The current models on the Pareto frontier for Agent Arena are: - Claude Opus 5 (High) by @AnthropicAI (+12.34%/ $1.78) - Kimi K3 (Max) by @Kimi_Moonshot (+10.53%/ $0.62) - GPT 5.5 (High) by @OpenAI (+7.75%/ $0.44) - GPT 5.5 by @OpenAI (+6.40%/ $0.28) - Grok 4.5 by @SpaceXAI (+6.08%/ $0.22) - GLM 5.2 (Max) by @Zai_org (+6.06%/ $0.18) - GPT 5.6 Luna (xHigh) by @OpenAI (+4.25%/ $0.04) - Mimo V2.5 Pro by @XiaomiMiMo (-2.22%/ $0.03) Check it out for yourself at the link below. Additional recent model releases will be landing on the Agent Arena leaderboard very soon. Stay tuned.

Top 15 models in Agent Arena vs. median task cost: who's delivering the most for their price? Key findings: Even within the top 5, median cost per task ranges from $0.62 for Kimi K3 (Max) to $3.37 for Claude Opus 5 (Max), a more than 5x difference. Claude Opus 5 (Max) costs almost twice as much as Opus 5 (High), despite scoring slightly lower: - Opus 5 (Max): +12.0% at $3.37 per task - Opus 5 (High): +12.3% at $1.78 per task Kimi K3 (Max) stands out for value near the top. It ranks #4# with +10.5% net improvement, while its $0.62 median task cost is the lowest among the top eight. GPT-5.6 Sol (xHigh) is the highest-ranked OpenAI model at #5#. Its $1.39 median cost is lower than all three Anthropic models ranked above it, although its +9.8% score is also lower. Grok and Qwen deliver competitive performance at some of the lowest costs: - Grok 4.5 achieves +6.1% at $0.22 per task - Qwen-3.8 Max achieves +6.3% at $0.33 per task Agent Arena evaluates models on millions of real-world, long-horizon agentic tasks from a global community of users. Models use tools like web search, filesystem access, and terminal commands to complete complex workflows. Performance is measured as net improvement, using causal tracing methodology to estimate how much each model improves outcomes relative to the average model. Cost is measured below as median cost per task.

Kimi Work for Financial Analysts - Tutorial #2# Use Kimi Work for 3 common investment research tasks: - Build a live investor dashboard - Update financial models in spreadsheet - Process and generate reports in batch Stay tuned for more Kimi Work workflows!