Xiaomi Releases MiMo-V2.6 Open-Source AI Models Topping Benchmarks
AI News: Xiaomi has released the MiMo-V2.6 family of open-source artificial intelligence models, introducing Pro and Flash variants designed for advanced reasoning, coding, cybersecurity and visual tasks.
According to the information provided, MiMo-V2.6 Pro achieved a score of 46 on the Artificial Analysis Intelligence Index, making it the highest-scoring open-source model on that index at the time of the reported release.
The release is also notable for its reported training scale. Xiaomi's team says the Pro model was developed using large-scale reinforcement learning (RL), with more than 750,000 training trajectories and reported training costs of approximately $2.62 million.
Introducing Xiaomi MiMo-V2.6 — Pro & Flash.
— Xiaomi MiMo (@XiaomiMiMo) September 21, 2026
Frontier intelligence, all the modalities, built in public.
🔹 Two omnimodal models, advancing through scaled reinforcement learning
🔹 Pro performs on par with Claude Opus 5 and GPT-5.6 Sol across most agent benchmarks
🔹 Pro scores… pic.twitter.com/oqfYPC00uK
What Is Xiaomi MiMo-V2.6?
MiMo-V2.6 is a new family of AI models developed by Xiaomi. The series includes two primary variants: MiMo-V2.6-Pro and MiMo-V2.6-Flash.
The models are aimed at demanding AI workloads rather than simple conversational applications. Their reported capabilities include software development, mathematical and logical reasoning, cybersecurity-related tasks and visual understanding.
The release follows the broader industry trend toward increasingly capable open-weight AI systems that developers can download, inspect, customize and run within their own infrastructure.
MiMo-V2.6 Pro Scores 46 on Artificial Analysis Intelligence Index
One of the headline results reported for MiMo-V2.6 Pro is its 46-point score on the Artificial Analysis Intelligence Index.
According to the supplied information, this was the highest score achieved by an open-source model on the index at the time of the announcement.
Benchmark results should nevertheless be interpreted in context because different AI evaluations measure different capabilities and may use different testing methodologies.
Strong Coding Performance
MiMo-V2.6 Pro reportedly achieved 71.9 on DeepSWE v1.1, highlighting its performance on software-engineering-related tasks.
AI coding benchmarks are increasingly important because modern models are being used for programming, debugging, repository analysis, code generation and software maintenance.
The reported result suggests that Xiaomi is targeting developers and engineering workflows in addition to conventional chatbot applications.
Cybersecurity Benchmark Score of 94.0
The model also reportedly achieved 94.0 on CyberGym, a benchmark associated with cybersecurity tasks.
Strong performance on cybersecurity evaluations can be relevant to tasks such as code analysis, vulnerability research and security-oriented reasoning. However, benchmark performance alone does not establish that a model is reliable or safe for deployment in real-world security environments.
MiMo-V2.6 and Visual Tasks
In addition to text-based reasoning and coding, the MiMo-V2.6 series reportedly demonstrates capabilities in visual tasks.
This reflects the growing importance of multimodal AI systems capable of working with more than just text. Visual reasoning can be useful for analyzing screenshots, diagrams, interfaces and other visual information.
1.02 Trillion Total Parameters
MiMo-V2.6 Pro reportedly contains approximately 1.02 trillion total parameters using a Mixture-of-Experts (MoE) architecture.
In an MoE model, not every parameter necessarily participates in processing every input. Instead, specialized expert networks can be selectively activated depending on the task.
This approach can allow models to have very large total parameter counts while keeping the computational requirements for individual requests more manageable than a traditional dense model with the same total number of parameters.
More Than 750,000 Training Trajectories
Xiaomi's team reportedly trained MiMo-V2.6 Pro through a large-scale reinforcement-learning process involving more than 750,000 trajectories.
In reinforcement learning for AI models, trajectories can represent sequences of interactions or attempts through which a model receives signals about the quality of its actions or results.
Scaling the number of training trajectories can provide models with more opportunities to learn from diverse tasks and outcomes, although the effectiveness depends heavily on the quality of the training environments and reward systems.
Reported Training Cost: $2.62 Million
The supplied information states that training MiMo-V2.6 Pro cost approximately $2.62 million.
Large-scale AI training can require substantial computing resources, particularly when reinforcement learning is performed over hundreds of thousands of trajectories.
The reported figure provides an indication of the resources involved in developing the model, although comparisons between training costs from different organizations can be difficult because accounting methods, hardware costs and included expenses may vary.
Open Weights, Training Code and RL Environments
One of the notable aspects of the release is the reported availability of more than just model weights.
The project reportedly provides:
- Open model weights
- Reinforcement-learning environments
- Training code
- Resources for developers and researchers to experiment with the models
The materials are reportedly released under the MIT License, making the project particularly relevant to developers interested in experimenting with open AI systems.
Why the MIT License Matters
The MIT License is a permissive open-source software license that generally allows users to use, modify and redistribute licensed software subject to its conditions.
For AI developers, a permissive license can make it easier to experiment with a model and incorporate associated software into different projects.
Users should still review the specific license terms and any model-specific documentation before deploying the models commercially.
Fuli Luo Highlights the Scale of the Project
Fuli Luo, identified in the supplied information as the team's lead, reportedly described the project as one of the largest reinforcement-learning efforts carried out by an open-source group.
The statement reflects the emphasis Xiaomi's team has placed on the scale of the training effort and the decision to release the resulting resources publicly.
Low API Pricing Draws Attention
The release has also attracted attention because of reported API pricing of approximately $0.13 per task.
Low-cost access can make advanced AI capabilities more accessible to independent developers, startups and researchers who may not have the resources to operate very large models themselves.
Actual costs can vary depending on the provider, task definition, usage limits and infrastructure used to access the model.
Why MiMo-V2.6 Matters for Open-Source AI
The release illustrates the increasing competition between proprietary AI systems and open-weight models.
Open models give researchers and developers greater opportunities to examine model behavior, customize systems and experiment with different deployment strategies. At the same time, large-scale open models can require substantial computing resources and technical expertise to operate efficiently.
MiMo-V2.6's combination of a large MoE architecture, reinforcement-learning training and open release makes it a notable development in the rapidly evolving AI model ecosystem.
MiMo-V2.6 Key Specifications
| Specification | Reported Details |
|---|---|
| Model family | MiMo-V2.6 |
| Variants | Pro and Flash |
| Architecture | Mixture-of-Experts (MoE) |
| Total parameters | Approximately 1.02 trillion |
| Artificial Analysis Intelligence Index | 46 reported for Pro |
| DeepSWE v1.1 |
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