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What is Kimi K3.1 Pro?

Kimi K3.1 Pro is a multimodal large language model developed by Moonshot AI to support advanced reasoning, autonomous AI agents, software development, 3D creation, and complex knowledge work. The model is based on a mixture-of-experts architecture containing approximately 2.8 trillion total parameters and 104 billion active parameters. Its one-million-token context window enables the processing of extensive documents, source code repositories, technical materials, and long-running task histories. Kimi K3.1 Pro builds on the Kimi K3 foundation with additional reinforcement learning and post-training optimization across programming, analytical reasoning, and agentic tasks. Its software engineering capabilities include code generation, debugging, terminal operations, automated testing, application development, and multistep problem-solving across existing repositories. The model can develop interactive websites, animated interfaces, browser-based 3D environments, and game projects using tools and frameworks such as Three.js, Unity, Unreal Engine, and Godot. Its multimodal reasoning capabilities allow it to interpret images, sketches, and visual references when generating technical designs or structured outputs. Three-dimensional modeling features include CAD script generation, parametric geometry creation, asset modification, and integration with tools such as Blender. Financial reasoning capabilities support complex spreadsheet models, financial statement analysis, investment research, cross-document verification, and evidence-based reporting. Kimi K3.1 Pro also supports tool-assisted workflows in which AI agents plan tasks, execute actions, evaluate results, and refine outputs across multiple steps. The model is available through Kimi's web and mobile applications, Kimi Work, Kimi Code, and the Kimi API, with a Highspeed variant providing faster inference and an open-weight release planned for November 4, 2026.

What is Inkling-Small?

Inkling-Small is an efficient multimodal AI model built to deliver strong reasoning and coding performance at a fraction of Inkling’s size. It is a Mixture-of-Experts transformer with 276 billion total parameters and 12 billion active parameters. The model was trained on NVIDIA GB300 NVL72 systems and is designed to combine high capability with more efficient inference. Inkling-Small supports native reasoning across text, images, and audio, allowing it to work across multimodal tasks without relying on separate encoders. Its context window supports up to one million tokens, making it useful for long-form reasoning, large-scale code understanding, document analysis, and agentic workflows. Users can adjust reasoning effort from minimal to extra high depending on whether they need faster responses or deeper computation. The model’s training process includes improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning. These techniques helped Inkling-Small outperform its larger counterpart on reasoning and coding benchmarks. The model performs well in coding and tool-use harnesses and exceeds 80% on SWE-bench Verified. Its encoder-free architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens. By combining efficient MoE design, one-million-token context, adjustable reasoning effort, multimodal processing, coding strength, and tool-use performance, Inkling-Small is designed for developers and teams that need capable AI with lower active compute requirements.

Media

Media

Integrations Supported

Model Context Protocol (MCP)
Claude Code
Claw Code
Command Code
Cosmos
Go
Grengin
Java
Kimi Code
Lua
Nebius Token Factory
Objective-C
Oh My OpenAgent
OpenCode Zen
Rapid Claw
React
Ruby
Shiori
Tabbit Browser
Visual Studio Code

Integrations Supported

Model Context Protocol (MCP)

API Availability

Has API

API Availability

Has API

Pricing Information

$2 per 1M tokens
API usage is pay-as-you-go: $2 per million input tokens and $10 per million output tokens. Cached input is $0.20 per million tokens; cache writes are $2 per million tokens. Kimi Code also provides tier-based membership access, with quotas and context limits depending on the tier.

Pricing Information

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per million output tokens

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Moonshot AI

Date Founded

2023

Company Location

China

Company Website

www.kimi.ai/

Company Facts

Organization Name

Thinking Machines Lab

Date Founded

2025

Company Location

United States

Company Website

thinkingmachines.ai/news/inkling-small/

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

AI Vision Models

Not specified

Open-Weight AI Models

Not specified

Small Language Models

Not specified

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