Overview

MiniMax M2.5 delivers exceptional long-context reasoning with a massive 1,000,000-token window, making it ideal for processing extensive documentation or complex multi-turn conversations. Built on FP16 precision, this proprietary model ensures high-fidelity outputs suitable for production-grade applications. Developers can integrate immediately via standard API endpoints, requiring minimal configuration to start streaming responses within minutes. The architecture is optimized for both English and Arabic linguistic tasks, providing robust performance across diverse semantic challenges without requiring fine-tuning. Benchmark data confirms superior retention over long sequences compared to alternative models.

For enterprise deployments, MiniMax M2.5 offers production-ready stability with native Arabic support confirmed for enterprise workflows. Pricing operates on a transparent credit system with a 3x multiplier relative to base rates, ensuring predictable cost management for high-volume usage. Accessible from the starter tier, this model allows teams to scale efficiently while maintaining strict data privacy standards. Whether building research pipelines or customer-facing chatbots, the balance of context capacity and linguistic accuracy provides a competitive edge for sophisticated AI solutions. Decision makers can rely on consistent uptime and clear billing structures for budget planning.

Specifications

Display Name MiniMax M2.5

Family MiniMax

Category Chat

Parameters unknown

Context Window 1,000,000 tokens

Quantization FP16

License PROPRIETARY

Min Tier Starter

Status Available

Pricing

3×

credits per token

1K 3,000 Credits

10K 30,000 Credits

100K 300,000 Credits

View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="minimax-m2.5",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

import anthropic

client = anthropic.Anthropic(
    base_url="https://llmapi.resayil.io/v1",
    api_key="YOUR_API_KEY"
)

message = client.messages.create(
    model="minimax-m2.5",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(message.content[0].text)

const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "minimax-m2.5",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

const data = await response.json();
console.log(data.choices[0].message.content);

curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "minimax-m2.5",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Summarizing entire books for key themes and insights

Analyzing large codebases for bugs and performance optimization opportunities

Reviewing lengthy legal documents for compliance and risks

Processing hours of video transcripts for specific queries

Searching massive data logs for specific error patterns

In-Depth Guide

Full Guide

Complete Guide to MiniMax M2.5 — LLM Resayil

MiniMax M2.5