Kimi K3

Overview

Kimi K3 is an open-weight multimodal reasoning model from MoonshotAI, released on July 16, 2026. Its stated scale is 2.8 trillion parameters, and it is positioned for complex coding, knowledge work and agentic workflows that need to run over longer horizons. The available description does not specify particular benchmark results or narrower areas of strength, so its fit is best assessed from its listed capabilities and interface characteristics.

The model accepts text, images and video, while its listed output is text. It supports reasoning and tools, and its context window is 1,048,576 tokens. That combination gives teams room to provide substantial context and use the model in tool-enabled workflows, while keeping the response format text-based.

For teams integrating through an API, Kimi K3 is identified as moonshotai/kimi-k3. Open weights are available, with the Hugging Face identifier moonshotai/Kimi-K3. This is relevant for teams that want to consider the model as both a hosted API option and an open-weight model, though the facts here do not specify deployment requirements or license terms.

Key features

Long context and multimodal input

A 1,048,576-token context window is a defining practical attribute for projects that need to pass long documents or accumulated conversation context in a single request. Kimi K3 also accepts images and video alongside text. Its listed modality and output are text, so teams should treat it as a model that can take multimodal input but returns text.

Reasoning and tool support

Reasoning and tools are both listed as supported. These capabilities align with the model’s stated use in complex coding, knowledge work and agentic workflows. The facts do not detail specific tool integrations or agent behavior, so implementation decisions should be based on the interfaces available to a given provider rather than assumptions about built-in workflow features.

Output capacity

The maximum output is 943,718 tokens. This is a substantial ceiling for tasks that need lengthy generated responses, but it should not be confused with a guarantee that every request can use the entire input context and output allowance at once.

Pricing

The listed API pricing is USD 2.7 per 1M input tokens and USD 13.5 per 1M output tokens. Output tokens are priced at a higher rate than input tokens, making the balance between supplied context and generated response important when estimating usage costs. The facts provide token rates only; they do not include separate charges or terms.

Platforms

Kimi K3 is listed under MoonshotAI, with the model ID moonshotai/kimi-k3 and Hugging Face ID moonshotai/Kimi-K3. It is open-weight and supports tools, vision, reasoning and a very large context window. No operating system, SDK, hosting environment or deployment instructions are specified here, so teams should confirm those details for their intended integration path.

Who it's for

Kimi K3 is a candidate for teams building coding assistants, knowledge-work applications or agentic systems that benefit from reasoning and tool use. Its text, image and video inputs may suit applications where prompts need to combine written material with visual content. The large context window also makes it relevant when an application needs to supply extensive source material or retained context.

It may be less suitable where the primary constraint is output-token cost, since its listed output rate is USD 13.5 per 1M tokens. Teams should also distinguish the model’s stated capabilities from implementation details not specified here, including deployment requirements for its open weights and the exact tools available through an API.

Pros and cons

Pros

• Open weights, with a named Hugging Face identifier.
• Accepts text, image and video inputs.
• Supports reasoning and tools.
• Offers a 1,048,576-token context window and maximum output of 943,718 tokens.
• Positioned for complex coding, knowledge work and longer-horizon agentic workflows.

Cons

• Output pricing is considerably higher than input pricing: USD 13.5 versus USD 2.7 per 1M tokens.
• The listed output is text, despite support for multimodal inputs.
• The available facts do not specify benchmark performance, deployment requirements, licensing terms or particular tool integrations.

Alternatives

For other models in the same family, see Kimi K3 (batch), Kimi K2.7 Code, Kimi Latest, Kimi K2.6, Kimi K2.5, Kimi K2 Thinking, Kimi K2 0905 and Kimi K2 0711. The listed facts do not include specifications or prices for these alternatives, so they should be compared on the requirements that matter to your stack.

Browse MoonshotAI models, Open-weight models, Vision language models, Reasoning models, Longest-context models and Newest models for category-level discovery.

Verdict

Kimi K3’s strongest case is its combination of open weights, multimodal input, reasoning and tool support, plus an unusually large listed context window. Those traits make it worth considering for teams working on demanding coding, knowledge-work and agentic applications. The key tradeoffs are its higher output-token price and the fact that the available details leave deployment, licensing and specific integration behavior unresolved. It is a promising fit when those capabilities match a team’s requirements, but API economics and implementation constraints should be checked before adoption.