19 July 2026  Product News

Kimi K3 Lands on Springbase AI: Inside the 2.8 Trillion Parameter Open-Weight Model

Moonshot AI just released the largest open-weight model ever shipped. It codes for hours on its own, reads a million tokens at once, and sees what it is building. Here is what matters, and how to try it today.

It is not every day a model comes along that rewrites the rules of what is possible.

On July 16, Moonshot AI dropped Kimi K3, the largest open-weight AI model ever released, clocking in at 2.8 trillion parameters. No open model has come this close to the 3 trillion mark before.

The numbers matter because they translate to capability. As the team at Moonshot put it, parameters are like the neural connections in a brain. Nearly 3 trillion of them means the model holds more knowledge, reasons deeper, and answers more precisely.

And now it is available on Springbase AI.

Key takeaways

• Kimi K3 is a 2.8 trillion parameter open-weight model from Moonshot AI, released July 16, 2026. Weights go fully open on July 27.
• It reads a million tokens at once, understands images natively, and stays efficient through a Mixture of Experts design with 896 experts.
• Its strengths are long-horizon coding, knowledge work, and visual reasoning. It scored 88.3 on Terminal Bench 2.1, just behind GPT-5.6 Sol.
• It is live on Springbase AI now, alongside GPT-5.6, Claude Fable 5, Gemini, Grok 4.5, and DeepSeek V4 Pro. One workspace, one NPR subscription, no dollar card.

What makes Kimi K3 different

The scale is one thing. The architecture is another.

Big, but efficient

Kimi K3 runs on a Mixture of Experts (MoE) design with 896 experts, activating only 16 for each task. That keeps it fast despite its massive size. The model also uses Kimi Delta Attention (KDA) and Attention Residuals, two architectural updates that improve how information flows across long sequences and deep networks. The result is a 2.5x improvement in scaling efficiency over the previous generation.

In plain terms: it turns compute into intelligence more effectively.

A context window the size of three novels

The context window sits at 1 million tokens. That is roughly three novels, back to back. You can feed it entire codebases, lengthy research papers, or complete project documentation in a single prompt, and it will hold all of it in mind while it works.

It sees what it is building

Native vision understanding comes baked in. Kimi K3 processes images, screenshots, and visual feedback alongside text, which matters for any task where you need the model to look at the result, not just imagine it.

Where it excels

Long-horizon coding

Kimi K3 operates with minimal human oversight. It sustains long engineering sessions, navigates massive repositories, and orchestrates terminal tools on its own. In Moonshot's tests, it autonomously optimized GPU kernels, built a GPU compiler from scratch, and even designed a chip for its own architecture.

Knowledge work

The model processes vast amounts of source material, coordinates tools, and produces structured results. In one demonstration, Kimi K3 recreated a computational astrophysics study in about two hours. The same work typically takes a senior researcher one to two weeks.

Visual reasoning

Kimi K3 blends software engineering with vision. It uses screenshots and visual feedback to improve game development, frontend work, and CAD design, iterating on what it sees rather than guessing.

Benchmark performance

Benchmark figures as shared by Moonshot AI at launch.

On the Arena AI leaderboard for frontend coding, Kimi K3 took the top spot.

How does Kimi K3 compare to GPT-5.6 and Claude Fable 5?

Honestly: overall performance still trails the top proprietary models. But in the areas that matter most for builders, such as programming, visual understanding, knowledge work, and long-horizon tasks, Kimi K3 holds its own against the best.

And it does this as an open-weight model. The gap between open and proprietary is closing, and K3 just closed it a little more.

What this means for you

For developers in Nepal, the open-weight part is the headline. Anyone can download, adapt, and run Kimi K3 freely. No gatekeeping. No per-seat licensing. No waiting for an enterprise sales call.

And if you would rather just use it, Kimi K3 is live on Springbase AI alongside GPT-5.6, Claude Fable 5, Gemini, Grok 4.5, and DeepSeek V4 Pro. That means you can compare outputs across models without leaving the platform, route quick tasks to lighter models, and save the heavy models for work that needs them. One workspace. One NPR subscription. No dollar card.

Getting the most from Kimi K3 on Springbase

A model this capable rewards a workspace that can keep up with it. A few ways to put K3 to work:

• Plan Mode for big engineering goals. Give Springbase a real goal, like migrating a service or refactoring a module. You get an editable, step-by-step plan to review and approve before the AI runs it. A million-token context window plus a visible plan is a strong combination.
• Recipes for repeatable work. When you find a prompt that works, save it once with variables and rerun it in one click. Code review checklists, research briefs, release notes: if it repeats, make it a Recipe.
• Knowledge bases for grounded answers. Upload your own docs and let K3 cite your files instead of the open internet. Pair its reasoning with your actual project documentation.
• Agents for follow-through. Let agents research, draft, and act across your connected tools, so the work finishes instead of stalling at a good answer.

Start using it today

Kimi K3 is live on Springbase AI right now. The default thinking effort is set to max, with low and high modes coming in future updates.

If you are a developer, researcher, or knowledge worker, this one is worth testing. Feed it a large codebase. Ask it to analyze a problem that has been sitting in your backlog. See how it handles a task that would normally eat your afternoon.

The model weights go fully open on July 27. But you can access Kimi K3 today, right inside the Springbase workspace.