Powered by Xiaomi
MiMo-V2.5
- Text Generation
MiMo-V2.5 is Xiaomi’s open-source, native omnimodal large model designed for text, code, and multimodal agentic workflows with a very long context window. It is part of the MiMo series that emphasizes practical reasoning performance and integration into Xiaomi’s broader AI ecosystem.
About the model
What is MiMo-V2.5?
MiMo-V2.5 is an open-source omnimodal large language model from Xiaomi, supporting text and multimodal inputs for a wide range of AI tasks. It is mainly used for general text generation and understanding, including reasoning over long contexts for chat, analysis, and knowledge work. It is also used for coding assistance, tool-using agents, and multimodal applications such as interpreting images or other media. It belongs to Xiaomi’s MiMo model family, succeeding earlier MiMo 1.x and 2.x generations and sitting below MiMo-V2.5-Pro in the lineup.
Model capabilities
5 Core Capabilities
-
Multimodal Understanding
Processes and jointly understands text, images, audio, and video within a unified 1M-token context for rich applications.
-
Conversational AI
Supports interactive chat-style dialogue with improved instruction following and agent-style responses for complex, multi-step tasks.
-
Long-Context Reasoning
Handles up to one million tokens of context, enabling analysis of long documents and sustained multi-turn reasoning workflows.
-
Speech Transcription
Companion ASR models in the V2.5 series convert spoken input to text, supporting bilingual and noisy real-world conditions.
-
Multilingual Support
Understands and generates content in both Chinese and English, useful for cross-lingual applications and international products.
Use cases
6 Most Valuable Use Cases
- Multimodal Content Understanding
- Long-Context Document Analysis
- Intelligent Agent Automation
- Smart Home Assistance
- AI Coding Assistant
- Voice And Speech Processing
Transparent pricing
Cost Comparison
LLM API offers the lowest costs and fastest performance for MiMo-V2.5-class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 120 img/min | 99.99% | $0.40/1K images | $0.00 | 512 images |
| Xiaomi | Asia Pacific | ~250ms | ~80 img/min | ~99.9% | ~$0.70/1K images | ~$0.00 | ~256 images |
| AWS Marketplace | US East | ~180ms | ~90 img/min | 99.9% | ~$0.95/1K images | ~$0.00 | ~256 images |
| Azure AI Studio | EU West | ~190ms | ~85 img/min | 99.9% | ~$1.00/1K images | ~$0.00 | ~256 images |
Performance benchmarks
Technical Specifications
| Metric | MiMo-V2.5 | Xiaomi MiLM-V2 | Qwen2-72B |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 128K | 64K | 128K |
| Input Price ($/1M) | $0.60 | $0.45 | $0.80 |
| Output Price ($/1M) | $1.80 | $1.50 | $2.40 |
| Max Output Tokens | 4K | 4K | 8K |
| Throughput | 60 tps | 45 tps | 40 tps |
| Uptime | 99.9% | 99.5% | 99.9% |
30-day usage via LLM API
- 3.8B
- Prompt tokens processed (last 30 days)
- 2.1B
- Completion tokens generated (last 30 days)
- 24.5M
- API requests served (last 30 days)
- 98.9%
- Avg uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent Model Routing
Automatically route each request to the optimal model and provider based on latency, cost, and capability—without changing your integration or touching provider SDKs.
One endpoint, every model -
Cost-Aware Orchestration
Enforce per-project budgets, choose cheaper equivalents automatically, and get transparent cost breakdowns across providers so you never lose track of spend again.
Control cost, not usage -
Resilient Failover Engine
Define fallback chains across models and vendors, with automatic retries and graceful degradation to keep your AI features online even during provider outages.
No single point of failure -
End-to-End Observability
Trace every call across providers with structured logs, metrics, and request replays so you can debug failures, tune prompts, and prove reliability in production.
See every token, everywhere -
Task-Level Abstractions
Describe tasks—chat, extraction, tools, ranking—once, and let LLM.API pick and shape the right model so you ship features, not prompt plumbing.
Code for tasks, not models -
High-Throughput Batch Jobs
Run large-scale inference jobs with automatic chunking, concurrency control, and progress tracking—no custom workers or brittle scripts required.
Millions of calls, one job
Decision guide
When to Use — When NOT to Use
Use it if...
- You need an on-device assistant optimized for Xiaomi phones and MIUI ecosystem integration.
- You need basic conversational AI for everyday queries, reminders, and smartphone utilities.
- Your use case involves simple question answering and instructions in Chinese consumer scenarios.
- Your use case involves integrating AI into Xiaomi smart-home or IoT devices locally.
- You need a vendor-aligned model primarily targeting Xiaomi hardware users and services.
Avoid if...
- You need state-of-the-art reasoning, coding, or research capabilities comparable to frontier models.
- Your workload requires broad multilingual support and robust, enterprise-grade internationalization features.
- You need extensive, well-documented cloud APIs and ecosystem tooling beyond Xiaomi’s platforms.
- Your workload requires rigorous, independently audited safety controls and compliance certifications globally.
- You need highly specialized domain performance for law, medicine, or complex financial analysis.
FAQ
Frequently Asked Questions
-
What is MiMo-V2.5?
MiMo-V2.5 is a Xiaomi foundation model focused on fast, cost-efficient text generation and understanding, accessible through the LLM.API unified gateway.
-
What is MiMo-V2.5 best suited for?
MiMo-V2.5 is best for general chatbots, assistants, and backend NLP tasks like classification, extraction, and summarization where low latency and cost matter.
-
What modalities does MiMo-V2.5 support via LLM.API?
Through LLM.API, MiMo-V2.5 currently supports text-in, text-out workflows; it does not support images, audio, or video.
-
What is the context window of MiMo-V2.5?
MiMo-V2.5 supports up to a 32K token context window, including both prompt and generated tokens.
-
How fast is MiMo-V2.5 in terms of latency?
Typical first-token latency is in the low hundreds of milliseconds, with streaming responses for interactive applications depending on request size.
-
How is MiMo-V2.5 priced on LLM.API?
MiMo-V2.5 uses a pay-per-token model on LLM.API, with separate input and output token rates published on the platform’s pricing page.
-
How do I call MiMo-V2.5 through the LLM.API?
You specify the model name "MiMo-V2.5" in your LLM.API request, using the standard chat or completion endpoints with your API key.
-
How does MiMo-V2.5 compare to similar mid-tier models?
MiMo-V2.5 targets a balance of quality, speed, and affordability comparable to popular mid-sized LLMs but is optimized for Xiaomi’s serving stack.
-
What are key limitations of MiMo-V2.5?
MiMo-V2.5 can hallucinate facts, lacks real-time knowledge, and is not suitable for tasks requiring strict domain-specific or legal guarantees.
-
Can MiMo-V2.5 handle long documents and multi-turn conversations reliably?
Yes, within its 32K token context limit, MiMo-V2.5 can manage multi-turn chats and long documents, but very long histories may reduce faithfulness.