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What is MiniMax M2.5?

MiniMax M2.5 is an advanced frontier model designed to deliver real-world productivity across coding, search, agentic tool use, and high-value office tasks. Built on large-scale reinforcement learning across hundreds of thousands of structured environments, it achieves state-of-the-art results on benchmarks such as SWE-Bench Verified, Multi-SWE-Bench, and BrowseComp. The model demonstrates architect-level planning capabilities, decomposing system requirements before generating full-stack code across more than ten programming languages including Go, Python, Rust, TypeScript, and Java. It supports complex development lifecycles, from initial system design and environment setup to iterative feature development and comprehensive code review. With native serving speeds of up to 100 tokens per second, M2.5 significantly reduces task completion time compared to prior versions. Reinforcement learning enhancements improve token efficiency and reduce redundant reasoning rounds, making agentic workflows faster and more precise. The model is available in both M2.5 and M2.5-Lightning variants, offering identical intelligence with different throughput configurations. Its pricing structure dramatically undercuts other frontier models, enabling continuous deployment at a fraction of traditional costs. M2.5 is fully integrated into MiniMax Agent, where standardized Office Skills allow it to generate formatted Word documents, financial models in Excel, and presentation-ready PowerPoint decks. Users can also create reusable domain-specific “Experts” that combine industry frameworks with Office Skills for structured, professional outputs. Internally, MiniMax reports that M2.5 autonomously completes a significant portion of operational tasks, including a majority of newly committed code. By pairing scalable reinforcement learning, high-speed inference, and ultra-low cost, MiniMax M2.5 positions itself as a production-ready engine for complex agent-driven applications.

What is DeepCoder?

DeepCoder, a fully open-source initiative for code reasoning and generation, has been created through a collaboration between the Agentica Project and Together AI. Built on the foundation of DeepSeek-R1-Distilled-Qwen-14B, it has been fine-tuned using distributed reinforcement learning techniques, achieving an impressive accuracy of 60.6% on LiveCodeBench, which represents an 8% improvement compared to its predecessor. This remarkable performance positions it competitively alongside proprietary models such as o3-mini (2025-01-031 Low) and o1, all while operating with a streamlined 14 billion parameters. The training process was intensive, lasting 2.5 weeks on a fleet of 32 H100 GPUs and utilizing a meticulously curated dataset comprising around 24,000 coding challenges obtained from reliable sources such as TACO-Verified, PrimeIntellect SYNTHETIC-1, and submissions to LiveCodeBench. Each coding challenge was required to include a valid solution paired with at least five unit tests to ensure robustness during the reinforcement learning phase. Additionally, DeepCoder employs innovative methods like iterative context lengthening and overlong filtering to effectively handle long-range contextual dependencies, allowing it to tackle complex coding tasks with proficiency. This distinctive approach not only enhances DeepCoder's accuracy and reliability in code generation but also positions it as a significant player in the landscape of code generation models. As a result, developers can rely on its capabilities for diverse programming challenges.

Media

Media

Integrations Supported

APIFree
Alibaba AI Coding Plan
BLACKBOX AI
Claude Code
Clawd.run
Cline
Fireworks AI
Kilo Code
Ollama
OpenClaw
Oxlo.ai
Roo Code
Shiori
Tabbit Browser

Integrations Supported

Hugging Face
Together AI

API Availability

Has API

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Free
Free Version

Supported Platforms

SaaS
Windows
Mac
Linux

Supported Platforms

Windows
Mac
On-Prem
Linux

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

MiniMax

Date Founded

2021

Company Location

Singapore

Company Website

www.minimax.io

Company Facts

Organization Name

Agentica Project

Date Founded

2025

Company Location

United States

Company Website

agentica-project.com

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

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