MiMo-V2.6-Flash-RL-GGUF Released with MTP and MMproj Support

September 22, 2026

At a Glance

Values determined by this site’s code at collection time. Dates are JST.

Overview

ggml-org has released “ggml-org/MiMo-V2.6-Flash-RL-GGUF", a GGUF quantized version of the multimodal model “MiMo-V2.6-Flash-RL" developed by XiaomiMiMo. Created using automated conversion tools, this model includes quantization formats such as MXFP4 and Q2_K, alongside MTP sidecars (MXFP4 and Q8_0) for utilizing speculative decoding features (--mtp), as well as a Q8_0 mmproj for image and audio encoders.

The original model, “XiaomiMiMo/MiMo-V2.6-Flash-RL“, was built to scale reinforcement learning (RL) for self-improvement. It adopts a native omni-modal configuration that handles text, images, video, and audio within a single model, complexly enhancing coding, general agents, visual tasks, and cybersecurity through a single RL run.

Specifications

• Parameters: 309B total / 15B active parameters
• Architecture: Sparse MoE (Mixture of Experts)
• Context Length: 1M tokens

Performance

The evaluation results below are taken from the model card of the original model “XiaomiMiMo/MiMo-V2.6-Flash-RL". Since this model is a GGUF quantized version, slight accuracy variations may occur due to quantization. Note that due to the high number of columns in the table, it has been narrowed down to focus on the original model and major comparative models (numerical values are kept as in the original text).

According to measurements by the publishers, MiMo-V2.6-Flash-RL scored 95.1 on CyberGym and 52.3 on AutomationBench v1.0.6, showing results that surpass other models in certain areas. It also achieved high-level scores of 87.6 on Terminal Bench 2.1, which measures whether commands can be actually executed in a terminal to complete tasks to the end, and 80.8 on OSWorld-Verified, which measures the ability to operate actual OS screens to complete tasks. On the other hand, it falls short of other large frontier models in cybersecurity fields such as ExploitBench (25.3) and ExploitGym (6.0), as well as Terminal Bench 4.0 (28.8), clearly dividing its strengths and weaknesses.

Strengths and Use Cases

The areas of strength and intended use cases for this model are based on the capabilities and training design of the original model, “XiaomiMiMo/MiMo-V2.6-Flash-RL".

1. Long-Context Processing with Integrated Multimodality
   It supports native omni-modality, handling text, images, video, and audio within a single model. Furthermore, leveraging a context length of up to 1M tokens, it excels at analyzing vast software repositories as a whole, understanding lengthy tool execution histories (tool traces), or processing multi-session dialogues and agent operation logs without information loss.

2. Autonomous Agent Operation and Environmental Adaptability
   Through a batch reinforcement learning method called “You Only RL Once", coding, general agents, visual tasks, and cybersecurity are trained in a complex manner within a single batch. This enables flexible adaptation and task execution in unfamiliar environments and tools, such as completing tasks via command execution in a terminal or visually recognizing and operating GUI screens of an OS.

3. Fast Inference via Speculative Decoding
   This model is equipped with a drafter for multi-token prediction (MTP) (utilizing a 5-layer SWA MTP structure) and supports speculative decoding to predict multiple tokens in a single forward pass. It is suitable for conversational assistants and agent executions where response generation speed and efficiency are required.

The main supported languages are English (en) and Chinese (zh), with expected use cases including conversational tasks, advanced tool calling, and complex multimodal analysis.

Hardware Requirements

Estimated requirements (calculated by Local Model Watch) — 159.4B parameters (taken from the base model XiaomiMiMo/MiMo-V2.6-Flash-RL)

Memory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference. What the quantization names mean: glossary.

Recent Models in the Same Size Class

Models with over 40B parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.

How to Get It

ggml-org/MiMo-V2.6-Flash-RL-GGUF is available in GGUF format on Hugging Face. The model is released under the MIT license.

Various quantized files and additional modules tailored to different use cases and environments are provided in the repository:

• MXFP4: A configuration that maintains the routed expert sections at native MXFP4 precision.
• Q2_K: A configuration where expert down projections are kept in MXFP4, and gate and up projections are quantized to Q2_K (Note: imatrix calibration is not applied at this stage).
• MTP sidecars (MXFP4 / Q8_0): Sidecar configurations for utilizing the speculative decoding feature (--mtp).
• Q8_0 mmproj: Components for the image encoder (MiMo ViT) and audio encoder.

Setup and server startup using llama.app can be performed by running the following command:

llama serve -hf ggml-org/MiMo-V2.6-Flash-RL-GGUF

Related Articles

• MiMo-V2.6-Distill-Qwen-9B GGUF Released
• ggml v0.24.0 Released with Backend Improvements and API Updates

Sources

• https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF
• https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL