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Description
MAI-Cyber-1-Flash represents Microsoft AI's streamlined, code-intensive security framework designed to detect vulnerabilities within intricate code structures. Originating from the MAI-Thinking-1 family and constructed from the ground up utilizing superior data, it is seamlessly embedded within MDASH, Microsoft's comprehensive system for identifying and addressing vulnerabilities through multiple agents. MDASH leverages over 100 expertly fine-tuned agents along with several advanced models to efficiently locate, confirm, and resolve software vulnerabilities, while MAI-Cyber-1-Flash capably manages up to 90% of related tasks. For particularly complex scenarios, larger models like GPT-5.4 can be engaged, ensuring an expertly calibrated multi-model approach that optimally assigns the appropriate model for each specific task. This collaboration between MDASH and MAI-Cyber-1-Flash has resulted in an impressive performance of 96% on CyberGym, surpassing competitors like Mythos, Gemini, and GPT-based solutions in their ability to analyze extensive codebases for vulnerability detection. Such advancements signify a major leap in ensuring the security and integrity of software systems in an increasingly complex digital landscape.
Description
SWE-1.7 is Cognition’s most capable software engineering model, built to push frontier coding performance while reducing the cost of high-quality agentic rollouts. The model is designed for real-world software development tasks that require extended reasoning, codebase understanding, terminal use, debugging, feature work, migrations, and careful validation. It was trained from a Kimi K2.7 base and improved through Cognition’s reinforcement learning pipeline, including more stable training, stronger infrastructure, better data curation, and long-horizon task techniques. SWE-1.7 is especially optimized for asynchronous software engineering, where an agent needs to work through large projects over longer sessions instead of simply answering short prompts. Its self-compaction capabilities allow the model to summarize its working state and resume from that summary, helping it operate beyond the raw context window on multi-hour tasks. The model is also trained to balance task success with efficiency, using concise reasoning when possible while preserving deeper exploration for harder problems. SWE-1.7 tends to investigate codebases more thoroughly than its base model, reading files, running searches, probing edge cases, and experimenting before making changes. It is available in Devin through web, desktop, and CLI interfaces, with Cerebras serving support at 1000 TPS. SWE-1.7 gives developers and engineering teams a high-performance coding model for complex software projects at a more practical cost.
API Access
Has API
API Access
Has API
Integrations
.NET
C
C#
C++
CSS
Codename MDASH
Dart
Devin Desktop
Go
Kubernetes
Integrations
.NET
C
C#
C++
CSS
Codename MDASH
Dart
Devin Desktop
Go
Kubernetes
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$20/month
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
Microsoft
Founded
1975
Country
United States
Website
microsoft.ai/news/introducing-mai-cyber-1-flash-inside-mdash/
Vendor Details
Company Name
Cognition
Founded
2023
Country
United States
Website
cognition.com