Overview

MiniMax M2.5 stands out as a premier long-context model designed for developers handling extensive data workflows. With a massive 1,000,000 token context window, this model excels at processing entire codebases, legal documents, or lengthy technical manuals without losing coherence. Running on FP16 quantization, it delivers high-precision outputs suitable for demanding analytical tasks. As a proprietary model within the MiniMax family, it offers robust reliability for enterprise-grade chat applications where context retention is critical. Developers relying on accurate retrieval over vast information sets will find this architecture particularly effective for complex reasoning tasks.

Integrating MiniMax M2.5 through LLM Resayil provides seamless access to advanced reasoning capabilities without infrastructure overhead. While operating at a 3x credit multiplier relative to the base rate, the investment yields significant returns for projects requiring deep context understanding and nuanced conversation management. Available from the starter tier, this model empowers developers to build sophisticated agents capable of synthesizing information across vast input ranges. Choose MiniMax M2.5 when your application demands exceptional recall and precision over standard conversational limits. This ensures your production environments maintain high performance standards while scaling efficiently.

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 and lengthy documents quickly

Analyzing large codebases for bugs and vulnerabilities

Reviewing multiple legal contracts for compliance issues

Processing hours of video transcripts for insights

Synthesizing extensive research papers into key findings

In-Depth Guide

Full Guide

Complete Guide to MiniMax M2.5 — LLM Resayil

MiniMax M2.5