Kimi K3

Kimi K3

Moonshot AI
MiniMax M1

MiniMax M1

MiniMax
+
+

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About

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.

About

MiniMax‑M1 is a large‑scale hybrid‑attention reasoning model released by MiniMax AI under the Apache 2.0 license. It supports an unprecedented 1 million‑token context window and up to 80,000-token outputs, enabling extended reasoning across long documents. Trained using large‑scale reinforcement learning with a novel CISPO algorithm, MiniMax‑M1 completed full training on 512 H800 GPUs in about three weeks. It achieves state‑of‑the‑art performance on benchmarks in mathematics, coding, software engineering, tool usage, and long‑context understanding, matching or outperforming leading models. Two model variants are available (40K and 80K thinking budgets), with weights and deployment scripts provided via GitHub and Hugging Face.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

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

Audience

AI researchers, developers, and enterprises needing a solution providing LLM capable of long‑context reasoning, efficient compute, and integration via function calls

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

$3 per 1M tokens (input)
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.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Pros & Cons from Real Users

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.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Moonshot AI
Founded: 2023
China
kimi.ai

Company Information

MiniMax
Founded: 2021
Singapore
www.minimax.io/news/minimaxm1

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Categories

Categories

Integrations

SiliconFlow
Augment Code
C++
Claw Code
Hugging Face
JavaScript
Kubernetes
Lua
Novita AI
Objective-C
Okara
OpenClaw
PowerShell
PrivatClaw
Python
Rapid Claw
SQL
Swift
Tailwind CSS
Together AI

Integrations

SiliconFlow
Augment Code
C++
Claw Code
Hugging Face
JavaScript
Kubernetes
Lua
Novita AI
Objective-C
Okara
OpenClaw
PowerShell
PrivatClaw
Python
Rapid Claw
SQL
Swift
Tailwind CSS
Together AI
Claim Kimi K3 and update features and information
Claim Kimi K3 and update features and information
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Claim MiniMax M1 and update features and information