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What is Step 3.5 Flash?

Step 3.5 Flash represents a state-of-the-art open-source foundational language model crafted for sophisticated reasoning and agent-like functionality, prioritizing efficiency; it employs a sparse Mixture of Experts (MoE) framework that activates roughly 11 billion of its nearly 196 billion parameters for each token, which ensures both dense intelligence and rapid responsiveness. The architecture includes a 3-way Multi-Token Prediction (MTP-3) system, enabling the generation of hundreds of tokens per second and supporting intricate multi-step reasoning and task execution, while efficiently handling extensive contexts through a hybrid sliding window attention technique that reduces computational stress on large datasets or codebases. Its remarkable capabilities in reasoning, coding, and agentic tasks often rival or exceed those of much larger proprietary models, further enhanced by a scalable reinforcement learning mechanism that promotes ongoing self-improvement. This innovative design not only highlights Step 3.5 Flash's effectiveness but also positions it as a transformative force in the domain of AI language models, indicating its vast potential across a plethora of applications. As such, it stands as a testament to the advancements in AI technology, paving the way for future developments.

What is Gemini 3.8 Flash?

Gemini 3.8 Flash distinguishes itself as Google's premier model for Flash, featuring significant upgrades over version 3.7 in crucial areas like software engineering, agent-based functions, and complex multi-step reasoning across specialized disciplines. Tailored for extensive coding tasks and autonomous agents, it effectively tackles intricate engineering problems with a thorough approach, ensuring the essential reliability needed for critical enterprise autonomy in niche knowledge sectors. This model shines particularly in quantitative and professional fields that require advanced analysis and reporting, as well as in multi-step reasoning endeavors that encompass STEM, humanities, and other professional sectors. The enhancements it presents stem from a core design strategy: Gemini 3.8 Flash places greater emphasis on demanding tasks by performing additional reasoning steps and employing tools in an iterative fashion, thereby enhancing its overall performance. When operating at increased effort levels, it may utilize more tokens to produce superior results, while developers are also presented with the option to dial down to lower effort levels for different outcomes. This adaptability not only supports a wide range of project requirements but also allows for customized applications based on specific goals and desired results. Consequently, users can engage with the model in ways that align closely with their individual project demands, maximizing its utility across various contexts.

Media

Media

Integrations Supported

Hugging Face

Integrations Supported

Agent Search on Gemini Enterprise Agent Platform
Bind AI
C#
Dart
Gemini 3.5 Flash Cyber
Gemini Enterprise
Gemini Managed Agents
Gemini Spark
Go
Google AI Overviews
HTML
PHP
Replit
SQL
Scala
Solidity
TypeScript
Wikis.ai
XML

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Android
iPhone
iPad

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

StepFun

Date Founded

2023

Company Location

China

Company Website

static.stepfun.com/blog/step-3.5-flash/

Company Facts

Organization Name

Google

Company Location

United States

Company Website

google.com

Categories and Features

AI Models

Not specified

Foundation 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

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

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