What is Kimi K3?

Kimi K3 is Moonshot AI’s most advanced model, designed for high-end reasoning, software engineering, multimodal understanding, knowledge work, and agentic AI applications. The model has 2.8 trillion parameters and is built on Kimi Delta Attention, a hybrid linear attention mechanism created for long-context performance. It also uses Attention Residuals and supports a native context window of up to 1 million tokens. This makes Kimi K3 suitable for tasks involving large codebases, long research materials, enterprise documentation, multi-file analysis, legal documents, technical manuals, and complex workflows. Kimi K3 always has thinking mode enabled, with reasoning effort configured through the reasoning_effort field and maximum effort currently supported as the default. Developers can use the model through an OpenAI-compatible API, making it easier to integrate with existing SDKs, clients, and application infrastructure. The model supports streaming responses with separate reasoning and final-answer deltas, allowing applications to display reasoning progress and final content differently. Kimi K3 also supports strict structured output with JSON Schema, partial mode for continuing from a prefix, custom tool calling, required tool use, and dynamic tool loading through system messages. Its vision capabilities support image and video inputs through base64 or uploaded files, enabling analysis of visual content alongside text. Automatic context caching helps workflows that reuse long prefixes, such as large knowledge bases or persistent system context, without requiring developers to manage cache IDs manually. By combining frontier-scale parameters, long-context processing, visual input, structured outputs, tool orchestration, and developer-friendly API compatibility, Kimi K3 gives teams a strong foundation for advanced AI agents, coding assistants, research systems, enterprise automation, and multimodal applications.

Pricing

Price Starts At:
$3 per 1M tokens (input)
Price Overview:
Kimi K3 is priced per 1 million tokens:

Cached input: $0.30
Uncached input: $3.00
Output: $15.00
Context window: 1,048,576 tokens

Cached inputs cost 90% less than uncached inputs, while generated output is the most expensive token category. Prices exclude applicable taxes, which are calculated based on the customer’s jurisdiction.

Integrations

Offers API?:
Yes, Kimi K3 provides an API

Screenshots and Video

Kimi K3 Screenshot 1

Company Facts

Company Name:
Moonshot AI
Date Founded:
2023
Company Location:
China
Company Website:
kimi.ai

Product Details

Deployment
SaaS
Training Options
Documentation Hub
Support
Web-Based Support

Product Details

Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English

Kimi K3 Categories and Features

More Kimi K3 Categories

Kimi K3 Customer Reviews

Write a Review
  • Reviewer Name: A Verified Reviewer
    Position: Developer
    Has used product for: Less than 6 months
    Uses the product: Daily
    Org Size (# of Employees): 26 - 99
    Feature Set
    Layout
    Ease Of Use
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    Would you Recommend to Others?
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    Epic model

    Date: Jul 16 2026
    Summary

    Five stars from me. Kimi K3 looks like one of the more interesting models right now for developers building serious AI agents, coding assistants, and knowledge-work automation. The combination of 1M-token context, native vision, tool calling, long-horizon coding focus, and massive model scale makes it feel purpose-built for the next wave of agentic development.

    Positive

    Kimi K3 looks seriously exciting from the perspective of a developer and AI agent builder. The 1M-token context window is the kind of thing that actually matters when you are working with large repos, long docs, product specs, logs, and messy multi-step agent workflows.

    I also like that Kimi is positioning it around long-horizon coding and end-to-end knowledge work, not just generic chat. The native visual understanding, tool calling support, and deep reasoning focus make it feel like a model built for agents that need to read, plan, inspect, code, and iterate across a real workflow.

    The 2.8T-parameter scale is also hard to ignore. If the real-world performance matches the positioning, Kimi K3 could be a very strong option for developers who want frontier-level capability with long context and multimodal inputs in the same stack.

    Negative

    It is still new, so I would want to test it heavily before depending on it for production agents. Big context windows are useful, but they do not automatically guarantee perfect repo understanding, reliable tool use, or consistent long-running execution.

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