MiniMax

MiniMax M3(5折)

minimax/minimax-m3
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

MiniMax M3永久五折,MiniMax M3 在编程和智能体等专业任务上达到了前沿的能力。它使用了我们提出的全新注意力架构 MSA (MiniMax Sparse Attention ),最高支持 1M 超长上下文。

Input / output modalities
文本 · 图像 · 视频 to 文本
Reference input / output price
Input¥2.1Output¥8.4per 1M tokens
Context window
1M
Added to catalog
Jun 1, 2026

Providers and pricing

ProviderInput lengthInput /MOutput /MCached /MContextMax outputDetails
MiniMax≤ 512K¥2.1¥8.4¥0.421M81.9K
≥ 512K¥4.2¥16.8¥0.84

MiniMax

Latency
1.27s
Throughput
131 tokens/s
Context
1M

Pricing

Input
¥2.1/M tokens
Output
¥8.4/M tokens
Cached
¥0.42/M tokens

Tiered pricing

Pricing varies by input token range.

0–512K Token

Input tier
¥2.1/M tokens
Output tier
¥8.4/M tokens
Cache read tier
¥0.42/M tokens

512K–∞ Token

Input tier
¥4.2/M tokens
Output tier
¥16.8/M tokens
Cache read tier
¥0.84/M tokens

Specifications

Context
1M
Max output
81.9K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

MiniMax M3(5折) code examples and API guide

Modelmesh normalizes requests and responses across service providers behind one consistent API.

Modelmesh provides an OpenAI-compatible Completion API for more than 300 models and service providers. Call it directly, through the OpenAI SDK, or with supported third-party SDKs.

Modelmesh-specific request headers in these examples are optional. When supplied, your application can appear on the Modelmesh rankings.

Supported endpointsSelect an endpoint to switch the example below.
/v1/chat/completions
from openai import OpenAI API_KEY = "$SSY_API_KEY" client = OpenAI( base_url="https://router.shengsuanyun.com/api/v1", api_key=API_KEY, ) try: completion = client.chat.completions.create( model="minimax/minimax-m3", messages=[{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}], temperature=0.6, top_p=0.7, stream=True, ) response_text = "" for chunk in completion: if chunk.choices and chunk.choices[0].delta.content is not None: content = chunk.choices[0].delta.content print(content, end="", flush=True) response_text += content except Exception as error: print(f"Request failed: {error}")
                
              

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