Kimi K3.1 Pro

Kimi K3.1 Pro

Moonshot AI
SWE-2 Flash

SWE-2 Flash

Cognition
+
+

Related Products

  • LTX
    182 Ratings
    Visit Website
  • AnalyticsCreator
    46 Ratings
    Visit Website
  • TrustInSoft Analyzer
    6 Ratings
    Visit Website
  • Google AI Studio
    42 Ratings
    Visit Website
  • Gemini Enterprise Agent Platform
    1,161 Ratings
    Visit Website
  • Qloo
    23 Ratings
    Visit Website
  • IONOS Cloud GPU Servers
    45,340 Ratings
    Visit Website
  • Innoslate
    98 Ratings
    Visit Website
  • RaimaDB
    12 Ratings
    Visit Website
  • Azore CFD
    26 Ratings
    Visit Website

About

Kimi K3.1 Pro is a multimodal large language model developed by Moonshot AI for advanced reasoning, software development, 3D modeling, financial analysis, and autonomous AI agent workflows. The model uses a mixture-of-experts architecture with approximately 2.8 trillion total parameters and 104 billion active parameters, supporting a context window of one million tokens. Its coding capabilities include debugging existing applications, generating complete software projects, developing interactive websites, and creating applications with game engines. Kimi K3.1 Pro can analyze images, interpret spatial relationships, generate CAD scripts, and create three-dimensional assets from text descriptions or visual references. Its reasoning and tool-use capabilities support financial modeling, investment research, document analysis, and multistep business workflows. The model is available through Kimi's web and mobile applications, Kimi Work, Kimi Code, and the Kimi API, with a Highspeed variant.

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 Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises 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

Software developers, AI engineers, researchers, financial analysts, and teams building applications or agentic workflows with AI models.

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

$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.
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

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

  • I can give it a large coding job, a research project, or a multi-step agent workflow and it does a much better job staying organized all the way through. Coding is probably the biggest jump. Moonshot reports a 65.7% score on Terminal-Bench 4.0, ahead of GPT-6 Astra’s reported 58.2% and just behind Claude Opus 5.5 at 66.4%. It feels especially good for terminal work, repo-level changes, and tasks where the agent has to keep using tools for a while. The 1M-token context window is also genuinely useful. I can keep a huge amount of source code, documentation, financial material, screenshots, and prior work in context without constantly trimming things down.

Cons

  • The main downside is that the full weights are not available yet. Moonshot says they will be released soon, so the open-weight story is promising, but I would wait for that release before judging local deployment and customization.

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
www.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 K2

Kimi K2

Moonshot AI
Inkling

Inkling

Thinking Machines Lab
Kimi K3

Kimi K3

Moonshot AI

Categories

Categories

AI Coding Models Supported
AI Models Supported

Integrations

.NET Supported
C Supported
C++ Supported
CSS Supported
Cerebras Supported
Dart Supported
Go Supported
HTML Supported
JavaScript Supported
Objective-C Supported
PHP Supported
PowerShell Supported
Python Supported
Ruby Supported
Scala Supported
Solidity Supported
Swift Supported
TypeScript Supported
XML Supported
YAML Supported

Integrations

.NET Supported
C Supported
C++ Supported
CSS Supported
Cerebras Supported
Dart Supported
Go Supported
HTML Supported
JavaScript Supported
Objective-C Supported
PHP Supported
PowerShell Supported
Python Supported
Ruby Supported
Scala Supported
Solidity Supported
Swift Supported
TypeScript Supported
XML Supported
YAML Supported
Claim Kimi K3.1 Pro and update features and information
Claim Kimi K3.1 Pro and update features and information
Claim SWE-2 Flash and update features and information
Claim SWE-2 Flash and update features and information