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Average Ratings 0 Ratings
Description
Gemini 3.5 Flash-Lite stands out as the quickest model within Google's Gemini 3.5 lineup, specifically engineered for tasks requiring low latency and for enhancing developer workflows that demand high throughput, including agentic search, document processing, coding, and extensive data analysis. It boasts an impressive output capacity of 350 tokens per second and marks a significant enhancement over earlier Flash-Lite iterations in terms of both quality and agentic capabilities. Developers have the flexibility to adjust the model's thinking level to suit the demands of the task at hand: minimal or low thinking allows for rapid processing of large volumes, while elevated thinking levels accommodate more intricate, multi-step workflows involving subagents. Furthermore, the model is equipped with built-in computational skills, enabling it to interact effectively with various digital environments across compatible platforms. Additionally, Gemini 3.5 Flash-Lite excels in coding, comprehending long contexts, and executing real-world tasks, consistently outperforming its predecessor, Gemini 3.1 Flash-Lite, in critical assessments and even exceeding the performance of Gemini 3 Flash on multiple benchmarks related to agentic functions and software engineering. This impressive performance highlights its potential to transform how developers approach complex workflows and data-intensive tasks.
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.
API Access
Has API
API Access
Has API
Integrations
.NET
C
C++
Codename MDASH
Cursor
Devin Desktop
Gemini 3.6 Flash
Google
Google AI Mode
Google AI Overviews
Integrations
.NET
C
C++
Codename MDASH
Cursor
Devin Desktop
Gemini 3.6 Flash
Google
Google AI Mode
Google AI Overviews
Pricing Details
$0.30 per 1M input tokens
$0.30/1M input tokens and $2.50/1M output tokens
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
Founded
1998
Country
United States
Website
gemini.google.com
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
microsoft.ai/news/introducing-mai-cyber-1-flash-inside-mdash/