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!

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!

Happy to have @FireworksAI_HQ as our day0 launch partner and bring Kimi K3 to more developers. With Fireworks, you can now deploy and fine-tune the 2.8T Kimi K3 model with just a few clicks!

We've open-sourced MoonEP, our high-performance communication library for distributed MoE workloads. Built to make expert-parallel communication more efficient at scale, MoonEP helps reduce communication overhead in large MoE training and inference systems. Explore on GitHub:

We've open-sourced AgentENV in collaboration with kvcache-ai. AgentENV is a distributed system for running agent environments at scale. Its components power agentic RL training for Kimi K3, with fast snapshot, resume, and fork support for large-scale parallel agent workflows. Explore on GitHub:

We've open-sourced FlashKDA, our high-performance CUTLASS-based implementation of Kimi Delta Attention kernels. It delivers 1.72×–2.22× prefill speedup over the flash-linear-attention baseline on H20, and works as a drop-in backend for flash-linear-attention. Explore on GitHub:

Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale. Model weights: Tech report: Tech blog:

Kimi K3 is second only to Fable 5 on AA-Briefcase, our agentic knowledge work benchmark, but costs more than Opus 4.8 to run while averaging nearly an hour per task Last week @Kimi_Moonshot released Kimi K3, a 2.8T parameter model that scores 57 on the Artificial Analysis Intelligence Index, comparable to models such as Opus 4.8 and GPT-5.5. On AA-Briefcase, Kimi K3 scores an Elo of 1543, a +727 improvement over Kimi K2.6 and the second highest score recorded, behind only Claude Fable 5 (1574) AA-Briefcase is our new proprietary benchmark for agentic knowledge work, testing models on a fully private dataset of realistic tasks across thousands of complex input files. Tasks require deliverables such as spreadsheets, presentations, and UI mock-ups, with performance combined into a single AA-Briefcase Elo based on correctness, analytical quality, and presentation quality Key results for Kimi K3 on AA-Briefcase: ➤ Second only to Fable 5: Kimi K3 achieves an AA-Briefcase Elo of 1543, the second-highest score overall, ahead of GPT-5.6 Sol (max, 1501), Claude Sonnet 5 (max, 1388), and Claude Opus 4.8 (max, 1347). This is a +727 improvement over the previous-generation Kimi K2.6 (816) and puts Kimi K3 only behind Fable 5 ➤ Strong objective and analytical performance, with comparatively weaker presentation: Kimi K3 achieves a rubric pass rate of 51%, second only to Claude Fable 5 (56%) and ahead of Claude Sonnet 5 (max, 42.3%) and GPT-5.6 Sol (max, 41.8%). It also records an analytical quality Elo of 1754, comparable to Claude Fable 5 (1744). Presentation quality is comparatively weaker, with a Presentation Elo of 1471, below GPT-5.6 Sol (max, 1660) and Claude Opus 4.8 (max, 1492) ➤ ~10x increase in Cost per Task: Kimi K3 averages a cost of $10.57 per task, a ~10x increase from Kimi K2.6, placing it among the most expensive models to run on AA-Briefcase. This is driven by model token pricing, increased output tokens and relatively high turn use, averaging 83 turns per task, versus 67 for Claude Fable 5 and 50 for GPT-5.6 Sol (max). Kimi K3 is priced at $3/$15 per 1M input/output tokens, with a 90% discount for cached tokens ➤ Averages nearly an hour per task: Kimi K3 has an average Time per AA-Briefcase Task of 56.4 minutes. This is driven by a high number of turns, high output token use, and lower speeds using the first-party Kimi API

BREAKING: Kimi K3 by @Kimi_Moonshot is 1st overall on 3D Design with an Elo of 1450. This is a 6 position and 108 Elo jump from @Kimi_Moonshot's previous model, Kimi K2.6. This performance puts Kimi K2.6 82 Elo ahead of Claude Fable 5 by @AnthropicAI in 2nd and 87 Elo ahead of GLM 5.2 by @Zai_org in 3rd. Congratulations to the @Kimi_Moonshot team on this accomplishment!

Exciting update: Kimi K3 has landed at #4# on the Agent Arena leaderboard, matching Claude Opus 4.8 and GPT-5.6 Sol. If Kimi K3's weights are released on schedule by July 27, it will become the #1# open-weight model. This release marks a major leap in agentic performance over Kimi K2.7 Code (#23# to #4#). Based on 8K+ live agentic sessions, Kimi K3 leads on confirmed task success rate (#1#). It also posts a strong +20.6% on praise vs. complaint (#3#). It currently lags the field in steerability (#14#) and bash recovery (#17#). 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. Here's a primer on the 5 signals: User-satisfaction proxies - Confirmed Success: an explicit "yes that worked" feedback from the user - Praise vs. Complaint: implicit sentiment in users reactions - Steerability: can the model course-correct when you push back? Tool-use proxies - Bash Recovery: how it recovers from CLI errors (primary signal for tool use) - Tool Hallucination: does it call tools that don't exist Below we break down how Kimi K3 scored across the 5 signals, drawn from tasks submitted by a global community of users. Congrats @Kimi_Moonshot on another big milestone!

BREAKING: Kimi K3 by @Kimi_Moonshot is officially 1st on Frontend Web App Arena by DesignArena With an Elo of 1326, this open-weight model leads the way, ahead of Fable 5, Sonnet 5, and Opus 4.8 by @AnthropicAI Huge congrats to the @Kimi_Moonshot team for this achievement!