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MiMo-V2-Pro

MiMo-V2-Pro is Xiaomi’s flagship trillion-parameter foundation model optimized for long-context, agentic workloads with a 1M-token context window.

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What is MiMo-V2-Pro?

MiMo-V2-Pro is a proprietary large language model from Xiaomi with over 1T total parameters and a 1M-token context length, designed as the company’s flagship foundation model for real-world agentic scenarios. It is mainly used for complex multi-step agent tasks such as workflow orchestration, long-horizon planning, and tool use across large contexts. It is also applied to advanced software engineering assistance and coding, where internal evaluations report performance approaching top frontier models on code intelligence benchmarks. MiMo-V2-Pro belongs to Xiaomi’s MiMo-V2 family of models and succeeds earlier systems like MiMo-V2-Flash within the broader MiMo AI platform.

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

Provider Input Output Cache read /M Latency Throughput Uptime
Xiaomi ~$0.60/1K images ~$0.00 — ~180ms ~450 img/min ~99.9%
Alibaba Cloud ~$0.70/1K images ~$0.00 — ~190ms ~400 img/min ~99.9%
AWS Marketplace ~$0.80/1K images ~$0.00 — ~210ms ~350 img/min ~99.9%
Azure AI Studio ~$0.85/1K images ~$0.00 — ~220ms ~320 img/min ~99.9%

Try this model

Test MiMo-V2-Pro right here — free to start.

MiMo-V2-Pro
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call the model through the OpenAI-compatible API.

python 
                                        from openai import OpenAI
                                            
                                            client = OpenAI(
                                                api_key="YOUR_API_KEY",
                                                base_url="https://inference.example.com/v1"
                                            )
                                            
                                            response = client.chat.completions.create(
                                                model="xiaomi/mimo-v2-pro",
                                                messages=[
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ],
                                            )
                                            
                                            print(response.to_json())
                                        
                                    
                                        {
                                                "model": "xiaomi/mimo-v2-pro",
                                                "messages": [
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ]
                                            }

5 Core Capabilities

  • Text Generation

    Generates high-quality natural language responses, explanations, and content across domains using a trillion-parameter, long-context language backbone.

  • Agentic Reasoning

    Performs multi-step planning, tool use, and complex task execution in agent workflows, optimized for real-world autonomous operations.

  • Long-Context Handling

    Processes and reasons over up to one million tokens of input, enabling understanding of large documents, codebases, and extended histories.

  • Coding and Tooling

    Supports advanced programming assistance, including system design, code generation, debugging, and integration with external tools and APIs.

  • Multilingual Understanding

    Understands and generates multiple languages, enabling cross-lingual assistance and workflow integration in global, multilingual environments.

6 Most Valuable Use Cases

  • Complex Workflow Agents
  • Software Coding Assistant
  • Long-Context Research
  • Business Process Automation
  • Tool-Use Orchestration
  • Smart Device Integration

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Unified AI Routing

    Dynamically route requests across providers and models based on latency, cost, or accuracy. One API, pluggable strategies, no client rewrites.

    One endpoint, every model

  • Cost-Aware Control

    Mix premium and budget models with hard caps and per-project policies. Optimize spend automatically without sacrificing SLAs or developer velocity.

    Ship fast, spend less

  • Resilient Fallbacks

    Define automatic failover chains when a provider degrades or times out. Keep production traffic flowing without manual incident playbooks or redeploys.

    Zero-downtime AI traffic

  • Deep Observability

    Trace every call across providers with metrics, logs, and structured payloads. Debug prompts, compare models, and tune routing from a single pane.

    See every token

  • Task-Level Orchestration

    Describe tasks, not endpoints. LLM.API selects and chains the right tools and models so you can focus on business logic instead of glue code.

    APIs that think in tasks

  • High-Throughput Batch

    Submit massive offline or backfill jobs through a single batch API with automatic chunking, retries, and aggregation tuned for each provider’s limits.

    Millions of calls, one job

When to Use — When NOT to Use

Use it if...

  • You need a cost-effective general-purpose vision-language model for mobile-centric applications.
  • You need tight integration with Xiaomi devices, sensors, or on-device AI capabilities.
  • Your use case involves multimodal queries combining photos, screenshots, and short text prompts.
  • Your use case involves consumer-facing features like smart albums, AR guidance, or camera assistance.
  • You need an on-device or edge-deployable model to reduce cloud inference costs.
  • Your use case involves recognizing everyday objects, scenes, and UI elements in smartphone images.
  • You need a model tuned for Asian consumer scenarios, interfaces, and localized content.

Avoid if...

  • You need state-of-the-art long-context reasoning across large codebases or lengthy technical documents.
  • Your workload requires highly specialized medical, legal, or financial domain expertise and certifications.
  • You need guaranteed multi-cloud neutrality without dependence on a specific hardware ecosystem.
  • Your workload requires ultra-high accuracy for safety-critical decisions in autonomous or industrial systems.
  • You need extensive ecosystem tooling, plugins, and mature third-party integrations available today.
  • Your workload requires training or fine-tuning on massive proprietary datasets entirely in-house.
  • You need fully documented, widely benchmarked performance comparable to leading frontier foundation models.

Frequently Asked Questions

  • What is MiMo-V2-Pro?

    MiMo-V2-Pro is a Xiaomi large language model available via LLM.API, designed for general-purpose text generation and assistant-style interactions.

  • What modalities does MiMo-V2-Pro support?

    MiMo-V2-Pro supports text-in, text-out interactions only when accessed through LLM.API.

  • How is MiMo-V2-Pro priced on LLM.API?

    MiMo-V2-Pro usage on LLM.API is priced per input and output token, with exact rates defined in your LLM.API pricing plan.

  • What is the context window of MiMo-V2-Pro on LLM.API?

    MiMo-V2-Pro supports up to a 16K token context window per request on LLM.API.

  • How fast is MiMo-V2-Pro in terms of latency?

    MiMo-V2-Pro typically returns first tokens within a few hundred milliseconds, with total latency depending on prompt size and output length.

  • How do I call MiMo-V2-Pro through the LLM.API?

    Select the Xiaomi provider and specify the MiMo-V2-Pro model name in your LLM.API request parameters, then send standard chat completion requests.

  • What is MiMo-V2-Pro best suited for?

    MiMo-V2-Pro is best for chatbots, content generation, and general reasoning tasks where cost-effectiveness and stable performance are important.

  • How does MiMo-V2-Pro compare to similar models on LLM.API?

    MiMo-V2-Pro generally trades slightly lower peak capability for more predictable costs and latency compared with top-tier frontier models.

  • What limitations should I be aware of when using MiMo-V2-Pro?

    MiMo-V2-Pro can hallucinate facts, lacks real-time knowledge, and should not be used without verification for safety-critical or highly specialized domains.

  • Can MiMo-V2-Pro handle structured tool calls or function calling?

    MiMo-V2-Pro supports tool-style outputs when you design appropriate JSON schemas and prompting, but it has no built-in tool execution.

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