モデル
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

入力モダリティ

  • Text
  • Vision
  • Audio
  • Video

出力モダリティ

  • 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

プロバイダー

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
Latency6.6S
Throughput32.6TPS
Uptime
99.91% uptime 2 days ago
100.00% uptime yesterday
99.88% uptime today

Performance for mimo-v2.6-pro

Uptime は過去72時間のリクエスト成功率です。AIHubMix は各プロバイダーを常時監視し、エラーや応答遅延が発生した場合は自動的に次善のプロバイダーで再試行します；Latency はリクエストの総往復時間です（低いほど良い）；Throughput はモデルの出力速度です（トークン/秒、高いほど良い）。

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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