MiMo-V2-Flash
MiMo-V2-Flash is an open-source Mixture-of-Experts language model from Xiaomi optimized for fast, long-context reasoning and coding.
What is MiMo-V2-Flash?
MiMo-V2-Flash is a Xiaomi open-source foundation language model using a Mixture-of-Experts architecture with 309B total parameters and 15B active parameters, designed for efficient high-speed inference. It is mainly used for complex reasoning tasks, code generation, and agent-style workflows where both quality and latency matter. With its 256K–262K token context window, it also serves long-form text generation and analysis use cases such as documentation, data processing, and interactive applications. It belongs to Xiaomi’s MiMo-V2 family of models, alongside variants like MiMo-V2-Pro and MiMo-V2-Omni.
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 | ~$0.08 | ~$0.08 | — | ~150ms | ~40 tps | ~99.9% |
| OpenAI | ~$0.10 | ~$0.10 | — | ~110ms | ~50 tps | 99.06% |
| Google Cloud | ~$0.09 | ~$0.09 | — | ~130ms | ~45 tps | ~99.9% |
| Azure | ~$0.11 | ~$0.11 | — | ~140ms | ~42 tps | 100.00% |
Try this model
Test MiMo-V2-Flash right here — free to start.
Suggestions for your first prompt
Code snippet
Call the model through the OpenAI-compatible API.
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-flash",
messages=[
{
"role": "user",
"content": "Describe this image in one sentence."
}
],
)
print(response.to_json())
{
"model": "xiaomi/mimo-v2-flash",
"messages": [
{
"role": "user",
"content": "Describe this image in one sentence."
}
]
}
5 Core Capabilities
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Advanced Reasoning
Performs strong logical and analytical reasoning, achieving competitive results on complex benchmarks and decision-making tasks at low cost.
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Code Generation
Generates, debugs, and explains source code, performing competitively on software engineering benchmarks like SWE-Bench and related tasks.
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Agentic Workflows
Acts as a foundation for AI agents, handling tool invocation, planning, and multi-step task execution in practical applications.
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Long-Context Chat
Supports extended conversational sessions with very large context windows, maintaining coherence across long interactions and documents.
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Multilingual Support
Understands and generates text in multiple languages, suitable for cross-language interactions and globally-deployed Xiaomi ecosystem products.
6 Most Valuable Use Cases
- General Chat Assistant
- Complex Code Generation
- Long-Context Document Analysis
- Software Agent Orchestration
- Legal & Policy Review
- Product Support Automation
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Unified AI Routing
Intelligently route each request across providers and models based on performance, latency, or cost. One API, pluggable policies, no client rewrites.
One endpoint, any model -
Cost-Aware Orchestration
Dynamically balance premium and budget models with per-project guardrails. Ship features faster while keeping AI spend predictable and auditable.
Control your AI bill -
Automatic Fallback Safety
Recover from provider outages and timeouts with built-in failover to backup models. Your AI features keep working, even when vendors don’t.
Resilient by default -
End-to-End Observability
Trace every request across providers with metrics, logs, and structured events. Debug prompts, tune routing, and prove reliability with real data.
See every token -
Task-Level Abstractions
Define tasks like chat, tools, RAG, or vision once, then swap underlying models freely. Keep business logic stable as the model landscape shifts.
Code to tasks, not models -
High-Throughput Batch
Process millions of requests cost-effectively with batch APIs optimized for concurrency and retries. Perfect for backfills, evaluations, and bulk content generation.
Scale workloads cheaply
When to Use — When NOT to Use
Use it if...
- You need a fast, lightweight vision-language model for mobile or embedded Xiaomi devices.
- Your use case involves on-device image understanding where privacy and offline operation matter.
- You need quick classification, detection, or tagging of photos from Xiaomi hardware.
- Your use case involves multimodal prompts mixing short text with single images or screenshots.
- You need a cost-efficient model to batch-process large volumes of consumer photos.
- Your use case involves prototyping Xiaomi-specific apps that leverage vendor-optimized AI acceleration.
Avoid if...
- You need state-of-the-art long-context reasoning across many documents and images simultaneously.
- Your workload requires highly reliable code generation, debugging, or complex software architecture planning.
- You need nuanced, domain-expert text-only reasoning for legal, medical, or financial decisions.
- Your workload requires handling very long conversations with detailed memory of prior exchanges.
- You need cutting-edge multimodal creativity like storyboarding films or detailed design iteration.
- Your workload requires broad third-party tool integration, plugins, or autonomous multi-step agents.
Frequently Asked Questions
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What is MiMo-V2-Flash?
MiMo-V2-Flash is a Xiaomi multimodal large language model accessible through LLM.API, tuned for fast, low-latency generation on text and images.
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What is MiMo-V2-Flash best suited for?
MiMo-V2-Flash is best for interactive apps needing quick responses, such as chatbots, lightweight agents, and image-aware assistants with rapid turn-around.
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What is the context window of MiMo-V2-Flash?
MiMo-V2-Flash supports a context window up to 8,000 tokens via LLM.API, suitable for moderately long conversations and documents.
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How fast is MiMo-V2-Flash in terms of latency?
MiMo-V2-Flash is optimized for low latency, typically streaming first tokens within a few hundred milliseconds under normal load on LLM.API.
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Which modalities does MiMo-V2-Flash support?
MiMo-V2-Flash supports text input and output, plus image input for vision-language tasks like captioning, classification, and grounded Q&A.
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How is MiMo-V2-Flash priced on LLM.API?
MiMo-V2-Flash uses LLM.API’s unified token-based pricing, billed per input and output token according to the Xiaomi MiMo-V2-Flash rate tier.
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How do I call MiMo-V2-Flash through the LLM.API?
Use the LLM.API chat or completion endpoint with the model identifier "xiaomi/mimo-v2-flash" and pass your prompts as usual JSON payloads.
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How does MiMo-V2-Flash compare to similar flash-style models?
Compared to similar flash models, MiMo-V2-Flash emphasizes low latency and solid multimodal capabilities, trading off some reasoning depth for speed.
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What are the main limitations of MiMo-V2-Flash?
MiMo-V2-Flash may underperform larger models on complex multi-step reasoning, long-context synthesis, and highly specialized domain knowledge.
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Does MiMo-V2-Flash support streaming responses on LLM.API?
Yes, MiMo-V2-Flash supports streaming, allowing tokens to be delivered incrementally for faster perceived latency in interactive applications.
COMPARE
Competitive Models
-
MiMo-V2.5-Pro
MiMo-V2.5-Pro is Xiaomi’s flagship open-weight trillion-parameter omnimodal MoE language model optimized for long-context, tool-using AI agents. It is notable for its 1M-token context window and leading performance on agentic and coding benchmarks at comparatively low token cost.
-
Qwen3.6 Plus
Qwen3.6 Plus is Alibaba’s flagship Qwen 3.6 series multimodal reasoning model that offers a very large context window and strong agentic capabilities for complex tasks. It is closed-weight and served via selected infrastructure partners for high-end enterprise and developer use.
-
Qwen3 VL 30B A3B Instruct
Qwen3 VL 30B A3B Instruct is a 30B-parameter Mixture-of-Experts vision-language model from Qwen, offering strong multimodal understanding and generation with a 262K-token context window. It is instruction-tuned for chat-style use and balances high-quality reasoning with relatively efficient active parameter usage.
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