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What is MiniMax M3.1 Flash?

The MiniMax M3.1 Flash is a sophisticated hosted language model tailored for developers engaged in creating coding assistants, AI agents, and a variety of software solutions. Accessible via the MiniMax API and MiniMax Code, it is capable of processing inputs in text, image, and video formats to produce relevant textual outputs. Its documented features include code generation, debugging, handling complex reasoning tasks, utilizing tools, analyzing codebases and documents, and optimizing computer workflows. The API is equipped with an extensive context window capable of accommodating up to 1 million tokens, provides flexible reasoning options, and supports request formats that are compatible with OpenAI and Anthropic standards. Usage is billed according to the number of tokens utilized for input, output, and cached input. MiniMax offers thorough documentation to facilitate API integration and ensure effective model use. The M3.1 Flash can be accessed through the hosted API, M Plan, and MiniMax Code, with future plans to release model weights, which will further broaden its utility in various applications. This development marks a remarkable advancement in the incorporation of AI technologies into the software development lifecycle, paving the way for enhanced innovation in the field. Additionally, the ongoing improvements to the platform promise to keep it at the forefront of AI-assisted development tools.

What is LTM-2-mini?

LTM-2-mini is designed to manage a context of 100 million tokens, which is roughly equivalent to about 10 million lines of code or approximately 750 full-length novels. This model utilizes a sequence-dimension algorithm that proves to be around 1000 times more economical per decoded token compared to the attention mechanism employed by Llama 3.1 405B when operating within the same 100 million token context window. Additionally, the difference in memory requirements is even more pronounced; running Llama 3.1 405B with a 100 million token context requires an impressive 638 H100 GPUs per user just to sustain a single 100 million token key-value cache. In stark contrast, LTM-2-mini only needs a tiny fraction of the high-bandwidth memory available in one H100 GPU for the equivalent context, showcasing its remarkable efficiency. This significant advantage positions LTM-2-mini as an attractive choice for applications that require extensive context processing while minimizing resource usage. Moreover, the ability to efficiently handle such large contexts opens the door for innovative applications across various fields.

Media

Media

Integrations Supported

APIFree
Alibaba AI Coding Plan
BLACKBOX AI
Claude Code
Clawd.run
Cline
ClinePass
Factory Droid
Fireworks AI
Kilo Code
MaxHermes
MiniMax Agent
Model Context Protocol (MCP)
Ollama
OpenCode Go
OpenTag
Roo Code
Tabbit Browser
Together AI
Vision Agents

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

$0.15/1M input tokens
Input: $0.15 per 1 million tokens; output: $0.60 per 1 million tokens; cached input: $0.015 per 1 million tokens. Standard pay-as-you-go billing in USD, based on token usage.

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Not specified

Company Facts

Organization Name

MiniMax

Date Founded

2022

Company Location

China

Company Website

www.minimax.io/blog/minimax-m31f

Company Facts

Organization Name

Magic AI

Date Founded

2022

Company Location

United States

Company Website

magic.dev/

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

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