Kimi K3

Kimi K3

Moonshot AI
SWE-2 Flash

SWE-2 Flash

Cognition
+
+

Related Products

  • LTX
    182 Ratings
    Visit Website
  • Yodeck
    8,160 Ratings
    Visit Website
  • OptiSigns
    8,462 Ratings
    Visit Website
  • AnalyticsCreator
    46 Ratings
    Visit Website
  • Mitti (by SafetyCulture)
    869 Ratings
    Visit Website
  • LM-Kit.NET
    29 Ratings
    Visit Website
  • Daylight
    15 Ratings
    Visit Website
  • Qloo
    23 Ratings
    Visit Website
  • SearchUnify
    150 Ratings
    Visit Website
  • Curtain MonGuard Screen Watermark
    7 Ratings
    Visit Website

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

SWE-2 Flash is an AI coding model developed by Cognition to provide fast, cost-efficient software engineering capabilities for autonomous development agents. Built on Thinking Machines' Inkling-Small foundation model, it uses mid-training and reinforcement learning to improve coding performance, reasoning, and task execution. The model supports software development activities such as code generation, code reviews, feature development, documentation updates, and automated engineering workflows. SWE-2 Flash generates approximately 200 tokens per second, making it three times faster and five times less expensive than the larger SWE-2 model. Its 276-billion-parameter mixture-of-experts architecture activates approximately 12 billion parameters during inference to balance performance and computational efficiency. SWE-2 Flash is available through Devin Desktop and CLI, with Cognition also developing a secure model-weight sharing program for trusted research and enterprise partners.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

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

Software developers, AI coding assistant users, software engineering teams, DevOps professionals, autonomous agent developers, AI researchers, enterprise development organizations, and cybersecurity researchers seeking fast, cost-efficient AI coding, automated code reviews, feature implementation, software maintenance, technical reasoning, and agentic development workflows

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Not Supported

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 Not Supported
Free Trial Not Supported

Pricing

$20/month
Free Version Supported
Free Trial Not Supported

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

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.

Pros & Cons from Real Users

Pros

  • This is the kind of model I would use all day for normal engineering work. It is fast enough that it does not feel like I am waiting on the model, but it is still strong enough for feature work, code review, docs, automations, and debugging. The speed is probably the biggest win. Cognition says it runs at around 200 tokens per second versus about 60 for SWE-2, and that difference matters a lot once an agent starts iterating through files and tools. The cost-performance balance is also really good. It is one-fifth the price of SWE-2, while scoring 45.5 on FrontierCode 1.1 Main, 60.1 on DeepSWE 1.1, and 89.9 on SWE-Bench Pro v2 Hard.

Cons

  • My main complaint is that it is not the model I would choose for the hardest repo-wide work. SWE-2 still has a noticeable edge on some tougher benchmarks, so I would move up when the task really needs it.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Moonshot AI
Founded: 2023
China
kimi.ai

Company Information

Cognition
Founded: 2023
United States
cognition.com

Alternatives

Alternatives

GPT-6.1 Sol

GPT-6.1 Sol

OpenAI
GPT-6.1 Sol

GPT-6.1 Sol

OpenAI
Kimi K3.1 Pro

Kimi K3.1 Pro

Moonshot AI
Inkling

Inkling

Thinking Machines Lab
Kimi K2

Kimi K2

Moonshot AI

Categories

Categories

AI Coding Models Supported
AI Models Supported

Integrations

.NET Supported
C Supported
C# Supported
C++ Supported
Cerebras Supported
Go Supported
JavaScript Supported
Kotlin Supported
Kubernetes Supported
Lua Supported
Objective-C Supported
PowerShell Supported
Python Supported
Ruby Supported
SQL Supported
Scala Supported
Solidity Supported
TypeScript Supported
XML Supported
YAML Supported

Integrations

.NET Supported
C Supported
C# Supported
C++ Supported
Cerebras Supported
Go Supported
JavaScript Supported
Kotlin Supported
Kubernetes Supported
Lua Supported
Objective-C Supported
PowerShell Supported
Python Supported
Ruby Supported
SQL Supported
Scala Supported
Solidity Supported
TypeScript Supported
XML Supported
YAML Supported
Claim Kimi K3 and update features and information
Claim Kimi K3 and update features and information
Claim SWE-2 Flash and update features and information
Claim SWE-2 Flash and update features and information