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Description

Kimi K3 is a large-scale AI model from Moonshot AI designed for advanced reasoning, software engineering, visual understanding, agentic workflows, and knowledge work. The model is built with 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear attention design created to support long-context intelligence. It also includes Attention Residuals and a native 1 million token context window, giving developers room to work with large files, repositories, documentation sets, transcripts, and enterprise knowledge bases. Kimi K3 always runs with thinking mode enabled and currently supports maximum reasoning effort by default. Developers can access the model through Moonshot’s OpenAI-compatible API using Python, cURL, and the OpenAI SDK. The API supports standard chat completions, streaming output, structured JSON Schema responses, partial continuation from a prefix, custom tool calling, required tool choice, and dynamic tool loading. Kimi K3 also supports vision inputs, including local images encoded as base64 and video files uploaded through the file API. Automatic context caching helps repeated long-prefix workflows become more efficient without requiring manual cache IDs or extra cache parameters. By combining long context, visual understanding, tool use, structured output, and advanced reasoning, Kimi K3 is built for developers creating sophisticated AI agents, coding systems, research tools, and enterprise applications.

Description

LTM-2-mini operates with a context of 100 million tokens, which is comparable to around 10 million lines of code or roughly 750 novels. This model employs a sequence-dimension algorithm that is approximately 1000 times more cost-effective per decoded token than the attention mechanism used in Llama 3.1 405B when handling a 100 million token context window. Furthermore, the disparity in memory usage is significantly greater; utilizing Llama 3.1 405B with a 100 million token context necessitates 638 H100 GPUs per user solely for maintaining a single 100 million token key-value cache. Conversely, LTM-2-mini requires only a minuscule portion of a single H100's high-bandwidth memory for the same context, demonstrating its efficiency. This substantial difference makes LTM-2-mini an appealing option for applications needing extensive context processing without the hefty resource demands.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Alibaba AI Coding Plan
Brokk
CSS
Claude Code
Command Code
Go
HTML
Kimi Code
Kimi Work
Objective-C
PHP
Pi Agent
PowerShell
Rust
SQL
Shiori
Solidity
Together AI
Viktor
XML

Integrations

Alibaba AI Coding Plan
Brokk
CSS
Claude Code
Command Code
Go
HTML
Kimi Code
Kimi Work
Objective-C
PHP
Pi Agent
PowerShell
Rust
SQL
Shiori
Solidity
Together AI
Viktor
XML

Pricing Details

$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 Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Moonshot AI

Founded

2023

Country

China

Website

kimi.ai

Vendor Details

Company Name

Magic AI

Founded

2022

Country

United States

Website

magic.dev/

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