AI infrastructure teams
Organizations with the GPU capacity and engineering experience to operate a very large open-weight model.
Independent tool overview
MiMo-V2-Flash at a glance
MiMo-V2-Flash is Xiaomi's open-weight mixture-of-experts model for reasoning, coding, and agentic workflows, with 309 billion total parameters, 15 billion active parameters, and a 256K-token context window.
Visit the official MiMo-V2-Flash site ↗
- Model type
- Open-weight mixture of experts
- Parameters
- 309B total; 15B active
- Context window
- Up to 256K tokens
- Best suited to
- Coding, reasoning, and agentic systems
- Deployment
- Self-hosted or Xiaomi-hosted API
- Knowledge cutoff
- December 2024 in Xiaomi's recommended prompt
Overview
What MiMo-V2-Flash is
MiMo-V2-Flash is a large open-weight model from Xiaomi built for reasoning, software development, and tool-using agents. Its mixture-of-experts architecture activates about 15 billion of 309 billion total parameters for each token, aiming to provide high capability without the inference cost of activating the full model.
This is infrastructure for experienced AI teams, not a lightweight model for a typical laptop. The official model files total roughly 313 GB, and Xiaomi's recommended high-throughput serving examples use multi-GPU deployments. Teams can instead access MiMo through Xiaomi's hosted Studio and API platform.
Xiaomi reports strong coding and reasoning benchmark results, including 73.4 on SWE-bench Verified. Those are vendor-reported evaluations; teams should test the model on their own repositories, tools, languages, latency targets, and safety requirements before choosing it for production.
Use cases
Who MiMo-V2-Flash is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Coding-agent developers
Teams building repository-aware assistants, software agents, or automated development workflows.
Tool-using agents
Products that need multi-turn function calling, long context, and structured interaction with external systems.
Model evaluation teams
Researchers comparing open-weight reasoning models against private workloads and deployment constraints.
Capabilities
Core MiMo-V2-Flash features
309B-parameter MoE architecture
The model has 309 billion total parameters while activating about 15 billion per token, balancing capacity with inference efficiency.
256K-token context
A long context window supports large codebases, extended conversations, and document-heavy agent workflows, although longer prompts increase compute and latency.
Hybrid attention
MiMo-V2-Flash interleaves sliding-window and global attention at a 5:1 ratio; Xiaomi says this reduces key-value cache requirements by nearly six times.
Multi-token prediction
A dedicated prediction module supports self-speculative decoding. Xiaomi reports up to roughly three times faster output in its serving setup.
Agent and tool calling support
The model supports reasoning mode, function calls, and multi-turn tool use through OpenAI-compatible serving interfaces.
Flexible deployment
Teams can download the weights from Hugging Face, serve them with tools such as SGLang or vLLM, or use Xiaomi's hosted Studio and API.
Process
How the MiMo-V2-Flash workflow works
Step 1
Choose hosted or self-hosted access
Use Xiaomi's hosted platform for a faster start, or self-host when infrastructure control, customization, or data location is more important.
Step 2
Confirm licensing and governance
Review the controlling model license and your organization's data, security, and acceptable-use requirements before deployment.
Step 3
Provision the serving stack
For self-hosting, download the weights and size GPU memory, storage, networking, quantization, and concurrency for the expected workload.
Step 4
Configure reasoning and tools
Set sampling parameters for the workload, define function schemas, and retain reasoning_content across messages when using multi-turn tool calls.
Step 5
Evaluate on real tasks
Measure answer quality, code correctness, tool-call reliability, latency, throughput, and cost using representative internal examples.
Step 6
Add production safeguards
Apply access controls, prompt-injection defenses, output validation, monitoring, and human approval for consequential actions.
Cost
MiMo-V2-Flash pricing and free plan
The model weights can be downloaded without a model fee, but self-hosting requires substantial GPU, storage, and operations capacity. Xiaomi also offers hosted Token Plans; the public platform lists monthly prices of $6, $16, $50, and $100, with lower effective monthly prices on annual billing. Confirm included credits and current model eligibility before purchasing.
Self-hosted weights
Free model download
Download MiMo-V2-Flash from Hugging Face and operate it on your own infrastructure.
- Compute, storage, networking, and engineering costs are separate
- Official model files total roughly 313 GB
- Best suited to teams with large-model serving experience
Lite
$6/month
Entry hosted Token Plan, shown at an effective $5.28 per month with annual billing.
- Uses Xiaomi MiMo API credentials and base URL
- Included credits and usage rules apply
- Verify current supported models before subscribing
Standard
$16/month
Mid-volume hosted plan, shown at an effective $14.08 per month with annual billing.
- Higher credit allowance than Lite
- Annual billing discount available
- Usage resets and consumption rules apply
Pro
$50/month
Higher-volume hosted plan, shown at an effective $44 per month with annual billing.
- Designed for heavier API and coding-tool usage
- Annual billing discount available
- Check the platform for current quota details
Max
$100/month
Largest public Token Plan, shown at an effective $88 per month with annual billing.
- Highest listed subscription allowance
- Annual billing discount available
- Check current fair-use and consumption rules
Pricing checked . Check current pricing at the source ↗
Assessment
MiMo-V2-Flash strengths and limitations
Where it stands out
- High-capacity MoE design activates only a fraction of total parameters per token
- Long 256K-token context supports large repositories and extended agent sessions
- Open weights allow infrastructure control and private deployment
- Built-in reasoning, function calling, and multi-turn agent capabilities
- OpenAI-compatible serving options reduce integration friction
What to consider
- The roughly 313 GB model download and multi-GPU serving requirements put self-hosting beyond most individuals and small teams
- A 256K context window can still be expensive and slow when fully used
- Vendor-reported benchmarks may not predict performance on a team's real codebase or agent workflow
- The December 2024 knowledge cutoff makes retrieval or web tools important for current information
- The GitHub repository identifies Apache 2.0 while Hugging Face metadata currently shows MIT, so teams should confirm the controlling license
- Production deployments still need independent security, safety, monitoring, and output-validation layers
Compare
MiMo-V2-Flash alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Coding
Qwen3-Coder-Next
Consider it when you want an open-weight model focused more narrowly on efficient agentic coding.
Explore Qwen3-Coder-Next →Consumer
Mistral 4 Small
Consider it when a smaller open model and more practical deployment footprint matter more than maximum scale.
Explore Mistral 4 Small →Business Operations
DeepSeek
Consider DeepSeek when comparing another established family of open reasoning and coding models.
Explore DeepSeek →Questions
MiMo-V2-Flash FAQs
Is MiMo-V2-Flash open source?
It is best described as open weight: Xiaomi publishes the model files and serving guidance. Review the model's controlling license and accompanying terms before commercial or modified use.
Can MiMo-V2-Flash run on a laptop?
Not in its full official form for practical everyday use. The model files are roughly 313 GB, and useful serving generally requires substantial GPU resources. A hosted API is the easier route for most users.
What is MiMo-V2-Flash good at?
Xiaomi positions it for complex reasoning, coding, and agentic work, including tool calls and long-context tasks. Its actual fit should be tested against your own workload.
Does MiMo-V2-Flash support function calling?
Yes. Xiaomi documents tool calling and multi-turn agent use. Applications should preserve reasoning_content in conversation history for multi-turn tool workflows.
How large is MiMo-V2-Flash?
It has 309 billion total parameters with about 15 billion active for each token. The downloadable Hugging Face files total roughly 313 GB.
How much does MiMo-V2-Flash cost?
The weights are downloadable without a model fee, but self-hosting infrastructure is costly. Xiaomi's hosted platform lists Token Plans from $6 to $100 per month before annual-billing discounts.
Are Xiaomi's benchmark results independently verified?
The results in Xiaomi's launch materials are vendor-reported. Treat them as a starting point and run evaluations on your own repositories, prompts, tools, and deployment stack.
Bottom line
Our MiMo-V2-Flash verdict
MiMo-V2-Flash is a compelling open-weight option for teams that need long-context reasoning, coding, and agent behavior and can support a heavyweight deployment. Most organizations should start through the hosted API, benchmark it on real work, and only self-host when control or economics justify the infrastructure.
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