> ## Documentation Index
> Fetch the complete documentation index at: https://docs.apiyi.com/llms.txt
> Use this file to discover all available pages before exploring further.
# MiMo-V2 Series: Xiaomi's Trillion-Parameter Agent Model
> Xiaomi launches MiMo-V2-Pro (1T+ parameter reasoning model) and MiMo-V2-Omni (unified multimodal model). Pro approaches Claude Opus 4.6 at 1/6th the price. Now available on APIYI.
## Key Highlights
* **Trillion Parameters**: MiMo-V2-Pro has 1T+ total parameters, 42B active, MoE architecture
* **Near-Top Performance**: AA Intelligence Index 49 (#8 globally), ClawEval 61.5 approaching Opus 4.6 (66.3), coding surpasses Sonnet 4.6
* **1/6th the Price**: Pro at \$1 input / \$3 output per M tokens — roughly 1/6th of GPT-5.2 and Opus 4.6
* **Full Multimodal**: MiMo-V2-Omni accepts text, image, video, and audio — with 10+ hours continuous audio understanding
* **1M Context**: Pro supports 1 million token context window
## Background
On March 18-19, 2026, Xiaomi officially launched the MiMo-V2 model family. MiMo-V2-Pro had previously been circulating anonymously on OpenRouter under the codename "Hunter Alpha", generating significant buzz before Xiaomi claimed ownership.
MiMo-V2-Pro is designed as an agentic foundation model optimized for orchestrating complex workflows, tool use, and code execution. MiMo-V2-Omni is a unified multimodal model that natively processes text, image, video, and audio — claimed to be the first omni model supporting 10+ hours of continuous audio understanding.
Both models are now available on APIYI.
## Detailed Analysis
### Core Features
Pro: 1T+ total params, 42B active, 7:1 hybrid attention ratio
Pro at \$1/\$3 per M tokens — \~1/6th the cost of comparable models
Omni accepts text, image, video, and audio inputs natively
Pro: 1M token context window, 131,072 max output tokens
### MiMo-V2-Pro Performance
| Benchmark | MiMo-V2-Pro | Claude Opus 4.6 | GPT-5.2 | Notes |
| ------------------------- | -------------------- | --------------- | ------- | ------------------------------- |
| **AA Intelligence Index** | **49** | — | — | #8 globally, #2 Chinese LLM |
| **ClawEval** | **61.5** | 66.3 | 50.0 | Agentic benchmark |
| **Coding** | Surpasses Sonnet 4.6 | — | — | Code generation & understanding |
### MiMo-V2-Omni Performance
| Benchmark | MiMo-V2-Omni | Comparison | Notes |
| ------------------------- | ---------------------- | ----------------- | ----------------------------------- |
| **AA Intelligence Index** | **43** | avg. 14 | Far above average |
| **BigBench Audio** | **94.0** | — | Audio understanding |
| **MMAU-Pro** | **69.4** | — | Multimodal audio understanding |
| **Image Understanding** | Surpasses Opus 4.6 | MMMU-Pro, CharXiv | Visual reasoning |
| **Audio Understanding** | Surpasses Gemini 3 Pro | — | Environmental sounds, multi-speaker |
### Technical Specs
| Spec | MiMo-V2-Pro | MiMo-V2-Omni |
| -------------------- | ----------------------------------- | ---------------------------- |
| **Context Window** | 1,000,000 tokens | 256,000 tokens |
| **Max Output** | 131,072 tokens | — |
| **Input Modalities** | Text + image | Text + image + video + audio |
| **Output** | Text | Text |
| **Architecture** | MoE (1T+ total, 42B active) | Unified multimodal |
| **Special** | Chain-of-thought, agentic workflows | 10+ hour continuous audio |
## Getting Started
### Code Example
```python theme={null}
from openai import OpenAI
client = OpenAI(
api_key="your-api-key",
base_url="https://api.apiyi.com/v1"
)
# MiMo-V2-Pro - complex reasoning and coding
response = client.chat.completions.create(
model="mimo-v2-pro",
messages=[
{"role": "user", "content": "Design a high-concurrency message queue architecture supporting 1M+ TPS..."}
]
)
print(response.choices[0].message.content)
```
```python theme={null}
# MiMo-V2-Omni - multimodal understanding
response = client.chat.completions.create(
model="mimo-v2-omni",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Describe the contents of this image"},
{"type": "image_url", "image_url": {"url": "https://example.com/image.png"}}
]
}
]
)
print(response.choices[0].message.content)
```
### Recommended Use Cases
Complex coding, agentic workflows, deep reasoning, long document analysis (1M context)
Video understanding, audio transcription & analysis, multimodal document parsing, chart analysis
## Pricing & Availability
### Pricing
| Model | Input Price | Output Price | Notes |
| -------------------------- | ----------------- | ----------------- | ------------------------ |
| `mimo-v2-pro` (up to 256K) | \$1.00 / M tokens | \$3.00 / M tokens | Reasoning model with CoT |
| `mimo-v2-pro` (256K–1M) | \$2.00 / M tokens | \$6.00 / M tokens | Extended context |
| `mimo-v2-omni` | \$0.40 / M tokens | \$2.00 / M tokens | Full multimodal |
MiMo-V2-Pro is priced at roughly 1/6th of Claude Opus 4.6 and GPT-5.2 — exceptional value.
### Deposit Bonuses
Top-up promotions apply. See [promotion details](/en/faq/recharge-promotions).
## Summary & Recommendations
The MiMo-V2 series is Xiaomi's major AI play. Pro approaches Opus 4.6 on agentic benchmarks with its trillion-parameter architecture and 1M context, at just 1/6th the price. Omni's unified multimodal understanding — especially 10+ hour audio — stands out among competitors.
**Recommendation**: Choose Pro for high-value reasoning and coding tasks. Choose Omni for multimodal understanding, especially audio and video.
MiMo-V2 series was recently launched. Implement proper error handling for production use and watch for updates.
Sources: Xiaomi official site `mimo.xiaomi.com`, Artificial Analysis benchmarks, OpenRouter pricing. Data retrieved: March 2026.