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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.

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Providers Comparison

Speed

Output tokens per second · Higher is better

246
97
92
91
90
80
60
27
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

End-to-End Response Time

Seconds · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

Price

USD per 1M tokens (blended) · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

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

  • Advanced Reasoning

    Performs strong logical and analytical reasoning, achieving competitive results on complex benchmarks and decision-making tasks at low cost.

  • Code Generation

    Generates, debugs, and explains source code, performing competitively on software engineering benchmarks like SWE-Bench and related tasks.

  • Agentic Workflows

    Acts as a foundation for AI agents, handling tool invocation, planning, and multi-step task execution in practical applications.

  • Long-Context Chat

    Supports extended conversational sessions with very large context windows, maintaining coherence across long interactions and documents.

  • 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)
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Architecture & Integration

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

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

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

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