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!

.@Kimi_Moonshot Kimi K3 is starting to roll out on Ollama's cloud subscriptions. We are working on improving Ollama's cloud to be much more transparent on the pricing to show the best performance / $. Try it with the tools you already use. Claude Code: ollama launch claude --model kimi-k3:cloud OpenCode: ollama launch opencode --model kimi-k3:cloud

According to data from SensorTower, Kimi app downloads nearly quintupled and daily active users jumped by ~40%. The K3 hype is translating into real users. Charts of the Week:

📣 Kimi K3 is now available in GitHub Copilot for @code! Try Kimi Moonshot's latest open-weight model for agentic coding, now hosted by @FireworksAI_HQ. 📖 Learn more:

📣 @Kimi_Moonshot's Kimi K3, an open-weight model, is now generally available and rolling out in GitHub Copilot. The model shows frontier-level abilities on agentic coding with highly cost-effective pricing. It is hosted by @FireworksAI_HQ. Learn more. 👇

Build Slides with Kimi Work - Tutorial #1#. Kimi Slides handles the entire slide-building process: - Clear structure and research, powered by Kimi K3 - Cohesive design, including polished charts and SmartArts - Editable and ready to download Let us know what you'd like to see next in the comments!

Code Arena now measures fullstack capabilities! View overall rankings across AI models on full-stack web development tasks: multi-step reasoning, tool use, and end-to-end app generation. - Kimi K3 (Max) takes #1# - GPT 5.6 Sol (xHigh) at #2# - Claude Fable 5 at #3# See more scores at:

It takes a crew to land a moonshot. Join the Kimi Ambassador Program. Anyone who's turned an untested idea into reality knows: moonshots are never a solo mission. It takes a crew. The Kimi Global Ambassador Program finds likeminded individuals on the same path, bringing the crew together. We hope that you: 🌙 Are influential in your own community, whether it be in tech, entrepreneurship, content creation, or on campus 🌙 Have implemented Kimi K3 into products, agents, workflows, teams, and communities 🌙 Are willing to share your experiences and passions to a broader community Apply here:

We are releasing PerceptionBench, a benchmark that isolates visual perception and evaluates it as a set of atomic capabilities - discovered from how today's models fail, rather than defined in advance. From frontier-model failures across 42 benchmarks, we derive 10 atomic perceptual capabilities and construct 3,000 verified questions, each isolating a single capability and answerable by looking, with no reasoning or external knowledge required. Blog: GitHub: Hugingface:

Big update: Among open-weight models, Kimi K3 (Max) is #1# in the Agent Arena with +9.75% net-improvement, surpassing GLM-5.2 (Max) at +7.12%, and landed the #1# spot across 5 signals (see below). Kimi K3 (Max) is also now #1# in open-weight in the Frontend Code (1682 pts) and Text (1485 pts) Arenas. Agent Arena measures models on millions of real-world, long-horizon agentic tasks. Models get web search, filesystem, and terminal tools to complete complex workflows: writing code, creating slide decks, researching the web, building apps, and analyzing documents. We use causal tracing methodology to measure a model's net improvement, which indicates how much it improves outcomes relative to the average model. Congrats to the @Kimi_Moonshot team for their contribution to the open ecosystem.

Kimi K3 is now live on @togethercompute! Happy to have Together AI as our day0 launch partner, giving developers immediate access to K3 through high-throughput inference optimized for coding agents and production workloads. A huge thank-you to the Together AI team for moving fast and making this launch possible!