모델
MiMo V2.6 Pro
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MiMo V2.6 Pro

mimo-v2.6-prollms.txt
Xiaomi
MiMo-V2.6-Pro is Xiaomi’s trillion-parameter, natively omni-modal flagship reasoning model for complex projects, long-horizon agentic tasks, coding, research, and professional knowledge work. It supports a 1M-token context window and up to 128K output tokens. Compared with V2.5, it delivers stronger reasoning and long-task performance while retaining the same API pricing.

Pricing

PricingCache ReadWeb Search
$0.48$0.96
$0.0038/M tokens$0.005/request

입력 방식

  • Text
  • Vision
  • Audio
  • Video

출력 방식

  • Text

Context length

  • 1.05M tokens

Max output

  • 131K tokens

Capabilities

  • Thinking
  • Streaming
  • Tool calling
  • Web search
  • URL context
  • Code interpreter
  • Computer use
  • File search
  • Memory tool
  • Structured outputs
  • Structured decision
  • Citations
  • Prompt caching
  • Background mode
  • Server-side sessions

제공업체

Xiaomi xiaomi-mimo-v2.6-pro
Pricing$0.48$0.96
Cache Read$0.0038/M tokens
Web Search$0.005/request
PrecisionⓘNot Disclosed
Latency6.5S
Throughput32.8TPS
Uptime
99.91% uptime 2 days ago
100.00% uptime yesterday
99.89% uptime today

Performance for mimo-v2.6-pro

Uptime은 지난 72시간 동안 성공한 요청의 비율입니다. AIHubMix는 모든 공급자를 지속적으로 모니터링하며, 오류가 발생하거나 응답이 느린 경우 자동으로 차선의 공급자로 재시도합니다; Latency는 요청의 총 왕복 시간입니다(낮을수록 좋음); Throughput은 모델의 출력 속도입니다(초당 토큰 수, 높을수록 좋음).

Uptime
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Latency
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Throughput
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Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://aihubmix.com/v1",
)

response = client.chat.completions.create(
    model="mimo-v2.6-pro",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

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

Frequently asked questions

What is MiMo V2.6 Pro?

MiMo-V2.6-Pro is Xiaomi’s trillion-parameter, natively omni-modal flagship reasoning model for complex projects, long-horizon agentic tasks, coding, research, and professional knowledge work. It supports a 1M-token context window and up to 128K output tokens. Compared with V2.5, it delivers stronger reasoning and long-task performance while retaining the same API pricing.

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