SandBaseSandBase
Start building
Use in agentDocsXiaomi models

Xiaomi modelsChat Completions model

xiaomi/mimo-v2-pro

MiMo-V2-Pro is Xiaomi's flagship foundation model, featuring over 1T total parameters and a 1M context length, deeply optimized for agentic scenarios. It is highly adaptable to general agent frameworks like OpenClaw. It...

Input⌘ / Ctrl + Enter

Try a prompt

1M context131.1K max output
Input $1.00/M · Output $3.00/M · Cache read $0.20/M
OutputReady

Your response will appear here

Choose an example or write a prompt, then click Run.

API details and access

Model Details

ProviderXiaomi
TypeLlm
Model IDxiaomi/mimo-v2-pro

Capabilities

InputText
OutputText
Context1,000,000
Max Output131,072
VisionNot supported
Function CallingSupported

Access details

Chat Completions
Base URLhttps://api.sandbase.ai
API Endpoint/v1/chat/completions
{
  "model": "xiaomi/mimo-v2-pro",
  "messages": [
    {
      "role": "user",
      "content": "Hello"
    }
  ],
  "max_tokens": 512
}

Pricing

Input$1.00 / 1M tokens
Output$3.00 / 1M tokens
Cache read$0.20 / 1M tokens

Related Models

xiaomi/mimo-v2-omniMiMo-V2-Omni is a frontier omni-modal model that natively processes image, video, and audio inputs within a unified architecture. It combines strong multimodal perception with agentic capability - visual grounding, multi-step planning, tool use, and code execution - making it well-suited for complex real-world tasks that span modalities, 256K context window.xiaomi/mimo-v2.5MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding tasks. Its 1M context window supports complete documents, extended conversations, and complex task contexts in a single pass, making it ideal for integration with agent frameworks where strong reasoning, rich perception, and cost efficiency all matter.xiaomi/mimo-v2.5-proMiMo-V2.5-Pro is Xiaomi’s flagship model, delivering strong performance in general agentic capabilities, complex software engineering, and long-horizon tasks, with top rankings on benchmarks such as ClawEval, GDPVal, and SWE-bench Pro. It can independently and autonomously complete professional tasks that would take human experts days or weeks, involving more than a thousand tool calls. Its context length of up to 1M makes it well suited for integration with a wide range of agent frameworks.