WLRouter
Models/MiniMax-M2.7
Model Code
MiniMax/MiniMax-M2.7
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Model overview

Text generationDeep thinking

MiniMax-M2.7 can construct complex agent harnesses and use agent teams, complex skills, tool-search workflows, and related capabilities to complete highly complex productivity tasks.

Company: MiniMaxProvider: Alibaba Cloud

Model capabilities

Input modalityT
Output modalityT
Model experience✓
Function calling✓
Structured output-
Web search-
Prefix completion-
Context cache✓
Batch inference✓
Fine-tuning-

Model pricing

Input$0.3134/1M tokens
Input (cache hit)$0.06269/1M tokens
Output$1.25/1M tokens

Rate limits and context

Maximum input length200K
Maximum input length (thinking mode)200K
Context length200K
Maximum output length128K
Maximum output length (thinking mode)128K
Maximum reasoning length128K
RPM500
TPM20000000

API reference

EndpointPOST /v1/chat/completions
SDKOpenAI-compatible

API code examples

Get API Key
from openai import OpenAI

client = OpenAI(
    api_key="$WLROUTER_API_KEY",
    base_url="https://api.wlrouter.com/v1"
)

response = client.chat.completions.create(
    model="MiniMax/MiniMax-M2.7",
    messages=[
        {"role": "user", "content": "Reply with one short sentence."}
    ],
    max_tokens=32
)

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