Audience

Developers, AI agent builders, software engineering teams, research teams, enterprise AI groups, data teams, product teams, and organizations that need long-context reasoning, multimodal understanding, structured output, tool calling, coding support, and OpenAI-compatible API access

About Kimi K3

Kimi K3 is Moonshot AI’s most capable model, built for frontier intelligence scenarios such as software engineering, knowledge work, deep reasoning, and multimodal understanding. The model has 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear attention mechanism, along with Attention Residuals for long-context performance. Kimi K3 supports a 1 million token context window, making it useful for analyzing large codebases, long documents, complex knowledge bases, and multi-step workflows. It includes native visual understanding for images and videos, with support for structured message formats, base64 image input, uploaded video files, and multimodal reasoning. Developers can use Kimi K3 through an OpenAI-compatible API with support for streaming, structured JSON output, partial mode, custom tools, dynamic tool loading, and automatic context caching.

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Pricing

Starting Price:
$3 per 1M tokens (input)
Pricing Details:
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

API:
Yes, Kimi K3 offers API access
See Integrations

Ratings/Reviews - 1 User Review

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5
More Reviews Write a Review

Company Information

Moonshot AI
Founded: 2023
China
kimi.ai

Videos and Screen Captures

Kimi K3 Screenshot 1
Kimi K3 Screenshot 1
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Product Details

Platforms Supported
Cloud
Training
Documentation
Support
Online

Kimi K3 Frequently Asked Questions

Q: What kinds of users and organization types does Kimi K3 work with?
Q: What languages does Kimi K3 support in their product?
Q: What kind of support options does Kimi K3 offer?
Q: What other applications or services does Kimi K3 integrate with?
Q: Does Kimi K3 have an API?
Q: What type of training does Kimi K3 provide?
Q: How much does Kimi K3 cost?

Kimi K3 Product Features

Kimi K3 Additional Categories

Kimi K3 Verified User Reviews

Write a Review
  • A Kimi K3 User
    Developer
    Used the software for: Less than 6 months
    Frequency of Use: Daily
    User Role: User
    Company Size: 26 - 99
    Design
    Ease
    Features
    Pricing
    Support
    Probability You Would Recommend?
    1 2 3 4 5 6 7 8 9 10

    "Epic model"

    Posted 2026-07-16

    Pros: 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.

    Cons: 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.

    Overall: 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.

    Read More...
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