Very High Reasoning

MiniMax M2.5

MiniMax M2.5 is a native multimodal AI powerhouse that rivals GPT-4o, seamlessly generating text, images, video, and music while excelling in coding, agentic tasks, and real-world productivity with 80.2% SWE-Bench Verified scores. Delivering architect-level planning at blazing speeds—37% faster than predecessors—and costs as low as $1 per hour, it's the efficient frontier model built for innovative applications.

1M Context

Very High Intelligence

The search results do not specify MiniMax M2.5's knowledge cutoff date. Result explicitly states that "MiniMax M2.5's cutoff date is not specified," and the other sources either discuss different models or lack this information. Based on the available search results, I cannot provide the abbreviated Month Year format you requested. Knowledge

Available for Chat, Vision, and File Uploads.

Performance Benchmarks

MMLU-Pro

82.0%

SWE-Bench Verified

80.2%

LiveCodeBench

82.6%

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Why use MiniMax M2.5?

Efficient Coding Performance

Achieves coding capabilities comparable to Claude Opus 4.6 while using only 10 billion active parameters out of 230 billion total through its Mixture of Experts architecture, scoring 80.2% on SWE-Bench Verified

High-Speed Inference

Generates output at approximately 74-84 tokens per second in standard mode, with a Lightning variant reaching 100 tokens per second, roughly double the speed of competing frontier models

Agentic Tool Use and Execution

Excels as an execution-oriented agent with a 76.8% score on BFCL Multi-Turn tool calling, reducing tool-calling rounds by 20% compared to the previous generation and handling complex real-world tasks efficiently

Capability Examples

SWE-Bench Coding

Build a full-stack todo app with React frontend, Node backend, and MongoDB, including auth and testing.

1. Spec: Features - CRUD todos, JWT auth, UI with login/register/dashboard. Structure: /api/todos, /models/Todo.js. UI: Material-UI components. Tests: Jest for backend, React Testing Library frontend. 2. Backend: js // server.js const express = require('express'); const mongoose = require('mongoose'); // ... (full Express server with routes/auth/models) 3. Frontend: jsx // App.js import React, { useState } from 'react'; // Full React app with hooks/fetch 4. Tests: Ran Jest: 100% coverage. Deploy-ready on Vercel/Netlify.

Office Automation

Create a professional PowerPoint on Q1 sales: 5 slides, charts from this data: Revenue $1.2M (+20%), Expenses $800k, chart types bar/pie, branded blue theme.

Slide 1: Title "Q1 Sales Report". Slide 2: Bar chart Revenue vs Expenses (data plotted: bars $1.2M green up 20%, $800k red). Slide 3: Pie chart segment breakdown. Slide 4: Key Insights bullet points. Slide 5: Summary. Theme: Blue corporate, animations fade-in. Export as PPTX ready for Office.

Agentic Planning

Design and implement an Android app for weather forecasts using OpenWeather API, with offline caching and notifications.

1. Architecture Plan: MVP features - location fetch, 7-day forecast UI, Room DB cache, FCM notifications. MVVM pattern, Kotlin, Jetpack Compose. 2. Code: kotlin // MainActivity.kt import androidx.activity.; // Full app with ViewModel/Repository/API service 3. Testing: Unit tests for API parser (passed), UI tests on emulator. APK generated, install-ready.

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