Kimi K2 Takes Open Models to a Trillion Parameters

According to Moonshot AI, Kimi K2 is a language model with one trillion total parameters whose weights can be downloaded. That puts it at a scale that, until now, had effectively belonged to major proprietary models from OpenAI, Google, and Anthropic.

The launch matters less for the number itself than for what it enables: companies and developers can run, adapt, and study a large model without relying entirely on someone else’s API. Kimi K2 is also presented as a model designed for agents—systems that do more than generate text, using tools, writing code, and chaining steps to complete a task.

One trillion parameters, but not all at once

Kimi K2 uses a MoE architecture, short for mixture of experts. Instead of activating the entire network for each token, the system routes processing to a small subset of experts.

Moonshot says that, out of its one trillion parameters, Kimi K2 activates around 32 billion for each token processed. That is a decisive difference: it makes it possible to combine substantial potential capacity without incurring the compute cost of using a dense one-trillion-parameter model for every response.

The idea itself is not new. DeepSeek popularized the strategy with models such as DeepSeek-V3, which has 671 billion total parameters, and Meta has also adopted it in Llama 4. But Kimi K2 raises the bar for open weights to a scale not previously seen among models available for download.

Moonshot has released a base version intended for further fine-tuning, along with Kimi K2-Instruct, designed to follow instructions and use tools. The model supports contexts of up to 128,000 tokens—a window large enough to work with code repositories, extensive documentation, or long task histories.

The bet is on coding agents

The company has centered its presentation on agentic coding tests. Unlike a conventional coding exam, these evaluations measure whether a model can locate a bug in a real project, modify multiple files, run tests, and propose a working solution.

According to Moonshot’s published table, Kimi K2-Instruct scores 65.8% on SWE-bench Verified, a benchmark based on real GitHub issues. The company places it above GPT-4.1 and below the best results attributed to the latest Claude models on the same test.

The company also places it ahead of GPT-4.1 and Claude Opus 4 on some agentic coding tests, including LiveCodeBench. That distinction matters: benchmarks do not crown a universal winner. Results vary depending on the tools allowed, the runtime, the model that plans the task, and the exact version of each evaluation.

Even so, the gap with closed models has narrowed. Two years ago, downloading an open model capable of competing on complex software fixes would have been unthinkable; now, the limiting factor may be the infrastructure required to serve it rather than access to the weights.

Open weights do not mean a restriction-free license

According to the license published by Moonshot, Kimi K2 is distributed under a modified MIT license. It permits broad use, including many commercial applications, but imposes additional conditions on large-scale services: anyone exceeding 100 million monthly active users or $20 million in monthly revenue must seek a commercial license from Moonshot.

That is why it is important to distinguish between open weights and software that is fully free under a standard license with no usage restrictions. For researchers, small companies, and teams that want to deploy the model internally, the license leaves considerable room to operate. For a large consumer platform, Moonshot retains negotiating leverage.

Moonshot also offers Kimi K2 through an API. According to the prices announced by the company, the cost is $0.60 per million uncached input tokens and $2.50 per million output tokens; cached inputs cost $0.15 per million tokens. That is a competitive price for a model of this scale, although running the weights in-house will still require servers equipped with several high-end GPUs.

China strengthens its position in open models

The announcement reinforces a clear trend: several of the most ambitious advances in models with accessible weights are coming from Chinese labs. DeepSeek, Alibaba’s Qwen, and now Moonshot AI have turned open weights and mixture-of-experts models into a way to compete with closed US systems.

Kimi K2 does not erase the advantage held by companies controlling the most powerful proprietary models, nor does it by itself prove that a team can replace them without incurring costs. But it does change the conversation for companies that need control over their data, custom fine-tuning, or independence from a single provider. The next test will be less eye-catching than the announcement: determining whether its benchmark results can be reproduced on real-world projects and whether the model maintains that performance outside carefully prepared environments.