Kimi K3 is Moonshot AI's flagship model: a mixture-of-experts network with 2.8 trillion parameters in total, 104 billion of them active for each token, a 1-million-token context window and native vision. Moonshot released it in July 2026 and offers it three ways: through its own API and apps, through inference partners, or as downloadable weights under the Kimi K3 License. This guide answers the questions to settle before you choose it: what the model is, what the API costs in practice, whether a company can use it, what running it takes, and where it is the wrong pick.
What is Kimi K3?
Kimi K3 is a general-purpose, multimodal model aimed at long coding sessions, knowledge work and reasoning. Moonshot builds it on two architecture changes, Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), which it says improve how information flows across sequence length and model depth. The model card lists 93 layers, 69 of them KDA and 24 gated MLA, with 896 routed experts of which 16 are active per token, plus two shared experts. Moonshot says these changes, together with refined training recipes, give about 2.5 times the scaling efficiency of Kimi K2.
Mixture-of-experts explains why the headline number and the working number differ. All 2.8 trillion parameters have to be stored, but each token passes through only about 104 billion of them.
The model reads text and images in one network, through its MoonViT-V2 vision encoder, and it supports a 1,048,576-token context window. Moonshot says it runs at maximum reasoning effort by default and will add low and high effort modes in later updates. Reuters reported the launch on July 17, 2026, and the paper is on arXiv as arXiv:2607.24653. The weights are on Hugging Face under the Kimi K3 License, and the model card recommends vLLM, SGLang or TokenSpeed as inference engines.
How much does the Kimi K3 API cost?
The API bills three kinds of input and output, and their shape matters more than any single headline rate. Cache-hit input, tokens the provider already holds in cache, costs one-tenth of cache-miss input. Output costs five times as much as cache-miss input. Cache writes are billed at the cache-miss input rate. Check the current numbers on the Kimi API Platform before you budget, because the rates are set there and can change.
In practice, an agent that resends a long context on every turn spends mostly on cache-hit input, so the discount decides the bill. Moonshot says its official API reaches a cache-hit rate above 90 percent on coding workloads. That is the company's own figure, and your rate depends on how your harness builds prompts. A chat that writes long answers spends mostly on output, the most expensive bucket. Keeping the start of the prompt stable, so the cache can hit, is the cheapest optimization most teams can make.
Judge cost per finished task, not cost per million tokens. A cheap model that needs three retries and a round of human cleanup can cost more than a dearer one that finishes the job once. The same idea applies to the agents you already run: see what Claude Code sends to the API and what each part costs walks through how to price each part of a request, and cost-xray, a Tigerless Labs tool, does that for Claude Code and Codex.
How long a context can Kimi K3 use?
The window is 1,048,576 tokens, about one million. The window size is the limit; what the model does with it is a separate question. In Moonshot's own BrowseComp run, K3 scored 90.4 with the full 1M-token window and no context management, and 91.2 when context was compacted at 300,000 tokens. Both figures are self-reported.
Length has a cost beyond the bill. Moonshot's limitations note warns that a harness which drops earlier reasoning can make the model unstable, and long sessions are where that bites. A community sample of r/LocalLLaMA threads, summarized by Aexol AI, reports that K3 sometimes mixes up timelines or state changes in long general chats. Test your longest real session before you rely on the full window.
Can a company use Kimi K3 commercially?
For most companies, yes, with three conditions to check. The license is custom, not MIT or Apache 2.0, so read its numbered sections instead of assuming the usual permissive terms.
Section 1 requires you to keep Moonshot's copyright and permission notice in every copy and to comply with applicable law.
Section 2 covers "Model as a Service": giving a third party access to inference or fine-tuning, through an API for example, in a way that lets them control inputs, parameters or training data. If you or an affiliate operate such a business and aggregate revenue exceeds $20 million over any 12 consecutive months, you must sign a separate agreement with Moonshot before commercial use. The license excludes end-user products that embed model features inside a specific feature or harness, and it excludes simply relaying requests to models hosted by others.
Section 3 applies to commercial products with more than 100 million monthly active users or more than $20 million in monthly revenue. Those products must display "Kimi K3" prominently in the user interface.
Section 4 switches sections 2 and 3 off for two cases: internal use, defined as use that does not make the software, its outputs or its underlying capabilities available to third parties, and use accessed through Moonshot's official products or certified inference partners. The internal exemption is narrower than it sounds. A customer-facing product built on the model is outside it, even when that product does not meet the Section 2 test.
Section 5 provides the software and its outputs "as is", with no warranty. VentureBeat's analysis of the full-weights release reads the same clauses for enterprise buyers. This is a reading of the license text, not legal advice. Ask counsel before you deploy anywhere near a threshold.
Can you run Kimi K3 yourself?
Not casually. The weights are public, but a 2.8-trillion-parameter model is a data-center workload. Moonshot recommends deploying it on supernode configurations with 64 or more accelerators to get the efficiency it claims, and it says it has contributed a KDA prefix-caching implementation to vLLM. The weights are quantized to MXFP4 with MXFP8 activations, using quantization-aware training from the supervised fine-tuning stage onward. Moonshot says this is for broad hardware compatibility, not for fitting on a desktop.
Hobbyists have tried anyway. Community threads on r/LocalLLaMA include workstation builds and CPU-offload experiments, and the results posted so far are slow. One post reports 0.23 tokens per second on a workstation with two 96 GB GPUs and 512 GB of system memory, and its author reported a bug in the llama.cpp pull request they used (the thread). The figures come from a third-party snapshot of the post dated July 30, 2026; the live Reddit page could not be checked. Read such posts as experiments, not benchmarks.
For most teams the practical routes are Moonshot's API, a certified inference partner, or a private deployment from a vendor that already has the hardware. Each route changes which license conditions apply, as section 4 shows.
How does Kimi K3 compare with Claude Fable 5 and GPT-5.6 Sol?
Moonshot says Kimi K3 trails the strongest proprietary models overall, Claude Fable 5 and GPT-5.6 Sol, while beating the other models in its suite. The model card gives the scores, all self-reported by Moonshot, with harness and effort settings in its footnotes. On Terminal-Bench 2.1 it reports 88.3 for K3, against 88.0 for Fable 5 and 88.8 for GPT-5.6 Sol. On BrowseComp it reports 91.2 against 88.0 and 90.4. On GDPval-AA v2 it reports an Elo of 1686, behind Fable 5 at 1747 and GPT-5.6 Sol at 1736. The gaps are small on some benchmarks and large on others, and the models were not all run under one harness, so read the numbers as a direction, not a ranking.
Moonshot also names the gap that benchmarks miss. Its limitations section says K3 "exhibits a noticeable gap in user experience" compared with Claude Fable 5 and GPT-5.6 Sol. Hands-on reports are mixed. One developer says that on normal coding work he cannot tell K3 and Claude apart (Vuink), while a ScriptXeno comparison finds Claude Code still ahead on long-horizon consistency and reliability. These are individual reports, not controlled tests. The only test that settles the question for your work is your own tasks. Run a week of real ones through each model with the same harness, and count what you have to fix afterwards.
Which Tigerless Labs tools depend on the model?
Tigerless Labs tools are built for Claude Code and Codex. Autoharness learns Claude Code skills from your sessions. Agent-memory gives Claude Code and Codex one shared store of Markdown memory. Cost-xray shows what Claude Code and Codex send to the API. All Labs projects are here.
Some tools do not depend on the model at all. pr-test-guard checks pull requests with rule-based tests. On its default path it calls no model and needs no API key, so the model that wrote a change does not change what the checker reports. The seo-ops tool works the same way for websites: it fetches a page the way a crawler does and reports pass or fail, with no LLM involved.
When is Kimi K3 the wrong choice?
Four cases, and Moonshot names the first two itself.
First, the harness. Moonshot warns that if an agent harness does not pass back all the historical thinking content the model needs, or if a session switches to K3 partway through, generation quality "may become highly unstable". It recommends Kimi Code or a harness with verified compatibility, and advises against switching to K3 in the middle of a session.
Second, autonomy. Its training stresses long-horizon tasks, so when it meets a minor problem or an ambiguous instruction it may make decisions on your behalf. Moonshot's advice is to set explicit behavioral limits in the system prompt or in AGENTS.md.
Third, the tasks where the others lead. If your work falls in the categories where Moonshot's own tables put Fable 5 or GPT-5.6 Sol ahead, or where user experience matters most, the lower price does not make up for it.
Fourth, regulated decisions. Where a licensed person has to decide, a model's answer is an input, not the decision. That is how Tigerless runs Lara for Tigerless Health: she reads the policy, compares plans and explains them, and a licensed agent makes every binding decision. The same rule applies to any model, Kimi K3 included. Insurers using AI in customer service covers where a licensed person still steps in.
Conclusion
Kimi K3 is a large, open-weight, multimodal model with a one-million-token window and strong self-reported results on coding and agent benchmarks. Its API bills cached input at a tenth of uncached input and output at five times, so the cache decides what an agent loop costs. Whether a company can use it depends on the three license conditions and on which route it takes to the model.
The best model for your work is the one that finishes your tasks with the least cleanup, at a cost per accepted task you can measure. For Kimi K3 that means a short trial: run a week of real tasks through the API with a stable prompt prefix, count the retries and the cleanup, and check the license against how you plan to deploy it. Then decide.
The free next step is to read the model card and the license yourself before you deploy anything.
FAQ
Is Kimi K3 open source?
Its weights are public on Hugging Face, so it is open-weight. The license is the custom Kimi K3 License, not MIT or Apache 2.0, and its sections 2 and 3 add commercial conditions.
How big is the Kimi K3 context window?
The model card gives a window of 1,048,576 tokens, about one million. Moonshot's own BrowseComp run with the full window and no context management scored 90.4; those figures are self-reported.
Can Kimi K3 read images?
Yes. It is a native multimodal model, and the model card lists text and image input, using the MoonViT-V2 vision encoder.
Is Kimi K3 free to use?
The weights can be downloaded, and the license allows many commercial uses. The API is billed per token, and running the model yourself needs data-center hardware that most teams do not have.
Which agent harness does Moonshot recommend for Kimi K3?
Moonshot says K3 works best with Kimi Code CLI and recommends a harness with verified support for preserved thinking history.
