What is MiMo-V2.6-Pro?
MiMo-V2.6-Pro is a multimodal reasoning model from Xiaomi MiMo and the flagship model in the MiMo-V2.6 family. It is designed for tasks that require more than a short question-and-answer exchange, including long-horizon agents, software development, cybersecurity analysis, research, office-software interaction and computer use.
In practical terms, the model can interpret text alongside images, video and audio, reason over large amounts of information, call tools and maintain a long working context. Its standard API returns text and tool calls rather than directly generating images, audio or video. That distinction matters: MiMo-V2.6-Pro is multimodal in what it can understand, not a general-purpose media-generation model.
Xiaomi provides the model in two forms. Developers can use the hosted model through Xiaomi’s API, or download the XiaomiMiMo/MiMo-V2.6-Pro-RL checkpoint for supported self-hosted deployments. The released weights, technical report, reinforcement-learning code, training environments and related research resources are published under the MIT license, according to Xiaomi’s release materials.
Position in the Xiaomi MiMo lineup
MiMo-V2.6-Pro sits at the top of Xiaomi’s MiMo-V2.6 model family and is positioned for demanding reasoning and agent tasks rather than lightweight chat. Xiaomi also lists a MiMo-V2.6-Pro-UltraSpeed deployment with different pricing, but the UltraSpeed offering is treated as a separate deployment option rather than a different underlying model in the supplied documentation.
The model is separate from Xiaomi HyperAI, the consumer-facing AI feature set integrated into selected Xiaomi devices and HyperOS products. MiMo-V2.6-Pro is primarily a developer and API model, with downloadable weights and compatibility with OpenAI-compatible and Anthropic-compatible API protocols.
Architecture and context window
The downloadable MiMo-V2.6-Pro-RL checkpoint uses a sparse mixture-of-experts, or MoE, architecture. It has approximately 1.02 trillion total parameters, with about 42 billion activated parameters for an individual input. In an MoE model, different expert components are selected for different tokens or tasks, allowing a very large overall network without activating every parameter for every calculation.
The model supports a context window of 1,000,000 tokens. A context window is the amount of input and ongoing conversation material the model can consider in one request or workflow. This unusually large limit is useful for large codebases, lengthy research material, extended agent traces, collections of documents and tasks that require many intermediate steps.
The maximum output is listed as 131,072 tokens, or up to 128K output tokens. Actual responses may be shorter depending on the request, API settings, tool interactions and practical deployment constraints. A large context limit also does not guarantee that every detail in a very long prompt will receive equal attention; developers should still structure important information clearly.
Supported modalities and outputs
MiMo-V2.6-Pro supports text, image, video and audio input. This allows a workflow to combine written instructions with visual material, recorded speech, video content or other supported media. Potential examples include examining a screenshot while explaining a coding problem, reviewing visual evidence during research, or using audio and video as part of a longer analysis task.
The documented standard model output is text. The API can also produce tool calls and structured responses where supported, but the model should not be selected when the requirement is native image generation, audio generation, video generation, music production or speech synthesis. The supplied model information lists those output types as unsupported.
MiMo-V2.6-Pro also supports streaming, context caching, web search and tool calling through Xiaomi’s platform. Structured output is listed among the platform capabilities. These features can help developers build applications that receive partial responses, reuse repeated context, retrieve current information or connect the model to external functions.
Reasoning, coding and agent capabilities
The model’s main purpose is long-horizon reasoning: breaking a complex objective into steps, working through intermediate information and continuing across an extended task. Xiaomi presents it for research, coding, cybersecurity, office software, visual coding and computer-use scenarios. It also appears in demonstrations involving embodied agents and action-oriented computer operation.
For coding, MiMo-V2.6-Pro is intended for substantial software tasks rather than only code completion. Its very large context can help it inspect extensive project material, while tool use can connect the model to execution, search or other developer-defined functions. However, the supplied information does not establish a specific programming-language benchmark or guarantee of code correctness. Developers should validate generated code, especially when it can modify files, execute commands or interact with production systems.
Computer-use capabilities should likewise be understood as an agent feature, not as a direct replacement for application security controls. The model can participate in workflows that involve computer operation and action-oriented behavior, but ordinary API interactions return text and tool calls rather than a direct robot-control stream. External software must interpret those outputs, enforce permissions and decide which actions are safe to execute.
Pricing and API access
Xiaomi’s listed standard API prices are:
| Usage type | Price |
|---|---|
| Input tokens, cache miss | $0.435 per 1 million tokens |
| Input tokens, cache hit | $0.0036 per 1 million tokens |
| Output tokens | $0.87 per 1 million tokens |
| Batch input | $0.2175 per 1 million tokens |
| Batch output | $0.435 per 1 million tokens |
These are usage-based API rates rather than a consumer subscription price. Cache-hit pricing is substantially lower than cache-miss pricing, so applications that repeatedly reuse a long system prompt or reference context may benefit from caching when their request pattern qualifies. Batch rates are lower than the standard interactive rates but are intended for supported batch workloads rather than latency-sensitive interactions.
The model is available through Xiaomi’s OpenAI-compatible and Anthropic-compatible protocols. This can reduce integration work for applications already designed around one of those API styles, although developers should still verify request fields, multimodal formatting, tool definitions, authentication and response behavior against Xiaomi’s documentation.
Main strengths and trade-offs
- Very large working context: The 1-million-token window is useful for large documents, code repositories and extended agent histories.
- Broad input coverage: Text, image, video and audio understanding enables richer analysis than a text-only model.
- Agent-oriented design: Tool calling, web search, streaming, structured output and computer-use workflows support multi-step applications.
- Open release: The MIT-licensed checkpoint and related resources provide a path to self-hosting and research experimentation.
- Large-model capability at a usage price: The model targets difficult reasoning and coding tasks while offering caching and batch prices for workloads that can use them.
Those benefits come with significant operational costs. Although only about 42 billion parameters are activated per input, the total checkpoint is approximately 1.02 trillion parameters. Self-hosting therefore requires substantial multi-GPU infrastructure and specialized configuration. Xiaomi identifies systems such as vLLM and SGLang as supported deployment options, but a downloadable checkpoint should not be confused with a lightweight local model.
The model is also not designed primarily for consistently low-latency responses. Its reasoning and agent focus can make it a better fit for difficult tasks than for high-volume, simple classification or short-answer workloads. A smaller or speed-optimized model may be more appropriate when response time and infrastructure cost matter more than maximum reasoning depth.
Limitations to consider
MiMo-V2.6-Pro’s multimodal input support does not imply that it can generate or edit media directly. Applications requiring native image, audio or video output need a separate generation system or a model designed for that purpose.
Self-hosting is another important limitation. The MIT license makes the weights available for use and modification, but it does not remove the hardware, memory, orchestration and maintenance requirements associated with a trillion-parameter sparse MoE checkpoint. Hosted API access may be simpler for teams that do not operate large-scale inference infrastructure.
Tool use and computer operation introduce safety concerns. Applications should isolate credentials, restrict available functions, log actions and require confirmation for irreversible operations. The model’s ability to produce an action-oriented response is not evidence that an action is correct or safe.
Finally, the supplied Xiaomi API example identifies December 2024 as the model’s knowledge cutoff. Web search and external tools can help with information beyond that cutoff, but only when the application enables and correctly handles those capabilities. The model should not be treated as automatically current simply because it can use tools.
When to choose MiMo-V2.6-Pro
Choose MiMo-V2.6-Pro when the task benefits from a large context, multimodal understanding, extended reasoning and tool-connected workflows. It is a strong candidate for analyzing large technical collections, building research agents, reviewing complex codebases, coordinating multi-step cybersecurity investigations, or creating computer-use systems that need to interpret visual and written information together.
The downloadable weights are particularly relevant to teams that need more control over deployment, want to conduct research on the model or prefer an open-source checkpoint. The hosted API is more practical when the team wants to avoid operating the required multi-GPU infrastructure.
Another option may be more appropriate for simple chat, high-throughput classification, strict low-latency requirements, lightweight local deployment or native media generation. MiMo-V2.6-Pro’s value is concentrated in difficult, long-running and multimodal tasks; using it for every request may cost more or add unnecessary complexity. Its standard output is also text and tool calls, so media-creation workflows will require additional models.
Bottom line
MiMo-V2.6-Pro is Xiaomi MiMo’s most technically ambitious offering in the supplied MiMo-V2.6 lineup: an open-source, multimodal reasoning model with a 1-million-token context, approximately 1.02 trillion total parameters, tool support and a focus on long-running agent work. Its combination of downloadable MIT-licensed weights and hosted API access gives developers both deployment flexibility and a managed option.
Its main qualification is scale. The model is best suited to organizations that can justify the infrastructure or API cost of advanced reasoning, coding and multimodal analysis. It is less suitable as a lightweight everyday model or as a standalone image, audio or video generator.