Moonshot’s latest model leapfrogged the month-old GLM-5.2 and a host of proprietary competitors to finish just behind GPT-5.6 Sol and Claude Fable 5 on many benchmarks.
What’s new: Moonshot AI introduced Kimi K3, a 2.8 trillion-parameter vision-language model. The company made the model available immediately via API and promised to release its weights by July 27, which would make Kimi K3 the largest known open weights model to date.
- Input/output: Text, images, and video in (up to 1 million tokens), text out (up to 1 million tokens, 62.0 tokens per second)
- Architecture: Mixture-of-experts transformer, including Kimi Delta Attention layers; 2.8 trillion parameters total, 16 of 896 experts (an estimated 50 billion parameters) active per token
- Features: Adjustable reasoning levels (currently max and always on, low and high promised), tool use, structured output, automatic context caching
- Performance: Third on Artificial Analysis's Intelligence Index (57), first among open models; first place on Arena.ai's Code Arena WebDev leaderboard
- Availability: Via Kimi.com, Kimi mobile apps, Kimi Work app, and Kimi Code CLI; $3.00/$0.30/$15.00 per million input/cached/output tokens; weights coming soon; Kimi app memberships range from free tier to $199 per month
- Undisclosed: Active parameter count, training datasets and methods, license for promised weights
How it works: Kimi K3 incorporates two architectural changes that Moonshot recently published: Kimi Delta Attention, which cuts the memory and computation that attention consumes over long inputs, and Attention Residuals, which lets each layer choose which earlier layers to draw on rather than summing them all equally. Moonshot said that these changes, along with a sparser mixture-of-experts design and improved training and data recipes, made training about 2.5 times as efficient as its predecessor’s training runs, measured in model improvement for a given amount of compute. The company promised further details in a forthcoming technical report.
- The model uses Kimi Delta Attention (KDA) for three of every four attention layers. KDA is a linear attention mechanism: Rather than comparing each new token to every previous token, it maintains a fixed-size memory that updates as it reads input, deciding for each value of that memory when to overwrite old information. In Kimi Linear, an experimental model released in 2025, KDA cut memory use by up to 75 percent and multiplied output speed by up to 6 times at input lengths of 1 million tokens.
- Kimi K3 uses Attention Residuals in place of standard residual connections to carry information between a transformer’s layers. Standard connections add each layer’s output to a running total with equal weight, so each layer’s individual contribution dilutes with more layers. Attention Residuals apply attention across depth, so each layer learns to draw selectively on the outputs of earlier layers, much as standard attention draws selectively on earlier tokens. In experiments published earlier this year, Moonshot developed this technique in two forms: a version that attends over the outputs of every earlier layer, and the version that Kimi K3 uses, which groups layers in blocks and attends over the outputs of those. A model using block-level Attention Residuals matched the performance of a model with standard residual connections but used 20 percent less training compute.
Performance: Kimi K3 outperformed every open weight model in independent tests of intelligence, agentic tasks, and coding. It trailed only the top few proprietary models on most benchmarks.
- On Artificial Analysis’ Intelligence Index, a composite of 9 evaluations of economically useful tasks, Kimi K3 set to max reasoning (57) falls behind only Claude Fable 5 set to max reasoning with fallback (60) and GPT-5.6 Sol set to max reasoning (59). The closest open weights model is GLM-5.2 set to max reasoning (51).
- On AutomationBench-AA, which tests automated workflows across business software, Kimi K3 set to max reasoning (53 percent) led all models. On GDPval-AA v2, a head-to-head ranking of models’ work on tasks common in finance, healthcare, law, and other economically central fields, Kimi K3 set to max reasoning (1,668 Elo) trailed only Claude Fable 5 set to max reasoning with fallback (1,760 Elo) and GPT-5.6 Sol set to max reasoning (1,743 Elo).
- Kimi K3 debuted at the top of Arena.ai’s Code Arena WebDev leaderboard, in which developers pick the better of two models’ outputs for the same frontend web development task. At launch, it achieved a preliminary ranking of 1,679 Elo, placing first in six of seven frontend domains.
- Kimi K3 set to max reasoning completed Artificial Analysis's Intelligence Index at $0.95 per task, near GPT-5.6 Sol set to max reasoning ($1.04 per task), cheaper than Claude Fable 5 set to max reasoning with fallback ($2.75 per task) and higher than GLM-5.2 set to max reasoning ($0.47 per task).
Behind the news: Over the past year, open models have regularly outdone each other, but this is the closest they’ve come since 2024’s Llama 3 to the state of the art. Kimi K2 introduced Moonshot’s 1 trillion-parameter line, and [Kimi K2.5] then Kimi K2.6 each became the leading open weights model on Artificial Analysis’ Intelligence Index in turn. The competition kept pace: Last month, Z.ai’s GLM-5.2 took the lead, at as little as a quarter of the cost of comparable proprietary models. Just three days after Kimi K3’s launch, Alibaba, a financial backer of Moonshot, introduced Qwen3.8-Max-Preview, an early version of a 2.4 trillion parameter model that the company claims trails only Claude Fable 5 and promises to release with open weights.
Why it matters: Kimi K3 whittles away at every reason why a developer choosing a model might default to the top proprietary model. It performs within three points of Claude Fable 5 at a much lower cost per task, and when released, its open weights allow fine-tuning, distillation, and closer study. Whoever owns those weights sets usage policies: Many requests that Claude Fable 5 refuses won’t cause friction for Kimi K3 users. Finally, competition this close — on price, control, and performance — pressures proprietary developers to deliver better, cheaper models.
We’re thinking: Kimi K3's efficiency gains stem from innovations like KDA’s linear attention, redesigned residual connections, and greater sparsity. Two of these Moonshot published as papers with code. When labs that face computation limits respond with architectural creativity and publish it, all developers benefit.