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MiMo-V2-Flash
- Text Generation
MiMo-V2-Flash is an open-source Mixture-of-Experts language model from Xiaomi optimized for fast, long-context reasoning and coding. It combines a 309B-parameter MoE architecture with only 15B active parameters to deliver high performance at low cost.
About the model
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.
Model capabilities
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.
Use cases
6 Most Valuable Use Cases
- General Chat Assistant
- Complex Code Generation
- Long-Context Document Analysis
- Software Agent Orchestration
- Legal & Policy Review
- Product Support Automation
Transparent pricing
Cost Comparison
LLM API offers the lowest latency and cost for MiMo‑V2‑Flash–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 70 tps | 99.99% | $0.06 | $0.06 | 64K tokens |
| Xiaomi | Global | ~150ms | ~40 tps | ~99.9% | ~$0.08 | ~$0.08 | ~32K tokens |
| OpenAI | US East | ~110ms | ~50 tps | ~99.9% | ~$0.10 | ~$0.10 | ~128K tokens |
| Google Cloud | EU West | ~130ms | ~45 tps | ~99.9% | ~$0.09 | ~$0.09 | ~64K tokens |
| Azure | US West | ~140ms | ~42 tps | ~99.95% | ~$0.11 | ~$0.11 | ~128K tokens |
Performance benchmarks
Technical Specifications
| Metric | MiMo-V2-Flash | Xiaomi MiMo-V2 | Huawei PanGu-Flash |
|---|---|---|---|
| Avg Latency | ~180ms | ~240ms | ~220ms |
| Context Window | 128K | 64K | 128K |
| Input Price ($/1M tokens) | $0.25 | $0.30 | $0.28 |
| Output Price ($/1M tokens) | $0.75 | $0.90 | $0.85 |
| Max Output Tokens | 4K | 4K | 8K |
| Throughput | 60 tps | 40 tps | 50 tps |
| Uptime | 99.9% | 99.5% | 99.7% |
30-day usage via LLM API
- 3.8B
- Prompt tokens processed (last 30 days)
- 11.5M
- API requests served (last 30 days)
- 4.6B
- Completion tokens generated (last 30 days)
- 99.8%
- Avg API uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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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
Decision guide
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.
FAQ
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.