M

MiMo-V2.6-Flash-MOPD

All-rounder

4.3(6)
Prose
4.3(3)
Intelligence
2.3(3)
Knowledge
3.7(3)
Instruction Following
3.0(3)
Coding
3.0(2)
Steerability
3.7(3)
Sign in to vote

MiMo-V2.6-Flash-MOPD is a text generation model available on Arli AI. It offers 310B parameters, 524K token context, reasoning support. Recommended use: All-rounder. Run it through the OpenAI-compatible chat completions API with unlimited requests and zero-log privacy.

View on Hugging FaceBack to Models

Hourly Usage

Token Usage

Input (24H)

358M

Output (24H)

318K

Input (7D)

2.7B

Output (7D)

4M

Performance Metrics

Typical Total Time

2.73s

Typical TTFT

0.67s

Typical Input TPS

994.49

Typical Output TPS

40.34

Model Information

Context Size

524288

Quantization

FP8-FP4 Native

Engine

vllm

Creation Method

Release Model

Model Class

MiMo-V2.6

Chat Template

MiMo

Reasoning

Yes

Vision

No

Parameters

310B

Added At

9/27/2026

Use MiMo-V2.6-Flash-MOPD with the Arli AI API

MiMo-V2.6-Flash-MOPD is available through an OpenAI-compatible chat completions endpoint. Point any OpenAI SDK at https://api.arliai.com/v1, pass MiMo-V2.6-Flash-MOPD as the model, and requests are served without logging.

cURL

curl https://api.arliai.com/v1/chat/completions \
  -H "Authorization: Bearer $ARLIAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MiMo-V2.6-Flash-MOPD",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.arliai.com/v1",
    api_key="YOUR_ARLIAI_API_KEY",
)

response = client.chat.completions.create(
    model="MiMo-V2.6-Flash-MOPD",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

See the text generation docs for every parameter, or read the quick start guide to make your first request.

license: mit language:

  • en
  • zh tags:
  • text-generation
  • multimodal
  • vision-language
  • audio
  • agent
  • video-understanding
  • long-context
  • mimo_v2
  • transformers library_name: transformers



Xiaomi-MiMo


Community
WeChat Group  |  Discord  |  Telegram  |  Reddit

MiMo-V2.6-Flash-MOPD

Technical Report

[!IMPORTANT] This is the MOPD upgrade of the MiMo-V2.6-Flash-RL checkpoint.

  • MOPD2 (👉 Technical Report §5.6)
    Fuses several domain-specialized teachers into one model, extending to domains where reliable training-time verification is hard, such as long-horizon game development, scientific research and embodied intelligence.
  • Diagnosing and Mitigating Tool-Call Repetition in MiMo-V2.6 (👉 Technical Blog)
    An easy-to-overlook failure mode in which the model keeps issuing the same or highly similar tool calls, appearing busy while making no progress. Nothing fails outright, so it tends to go unnoticed. The MOPD stage handles it efficiently, with a short specialized-teacher run that converges quickly.

1. Introduction

How MOPD2 works

MOPD2 distills several domain-specialized teachers into the student on-policy. The teachers fall into two families: mixRL teachers, trained on verifiable tasks, and SFT teachers, trained on synthetic demonstrations for open-domain tasks where a reliable reward is hard to design. Three streams contribute to a single update:

  • Standard MOPD: mixRL teachers supervise full autonomous rollouts.
  • Teacher-Prefix OPD: prefixes come from teacher rollouts. A trajectory with k assistant turns yields k history prefixes, one per turn. The model generates a single new turn from each, and the teacher scores it against the same history.
  • SFT-Prefix OPD: prefixes come from SFT demonstrations. The demonstration supplies the history, and the model writes its own continuation.

Method details are in Technical Report §5.6.

Tool-call repetition

Following the release of MiMo-V2.6, tool-call repetition emerged as one of the most noticeable issues in agentic settings: the model would sometimes issue the same or highly similar tool calls repeatedly, consuming time and context without making progress. This checkpoint mitigates it.

Tool-call repetition rate on MiMo-V2.6-Flash before and after MOPD

Figure: response-level repetition rate on MiMo-V2.6-Flash, RL-stage versus this checkpoint, across context lengths and agent harnesses.

The technical blog has the full diagnosis. The fix is lightweight to train: a short specialized-teacher run that folds into the normal MOPD pass.

Model Summary

  • Architecture: Sparse MoE (Mixture of Experts), 309B total / 15B activated parameters
  • Context Length: 1M tokens
  • Modalities: Text, Image, Video, Audio
  • Vision Encoder: 681M-param MiMo ViT (28 layers: 24 SWA + 4 Full)
  • Audio Encoder: 308M AudioTokenizer + 127M audio patch encoder
  • Multi-Token Prediction (MTP): 5-layer speculative decoder

Figure 1: MiMo-V2.6 architecture — omni encoders, hybrid SWA backbone, and MTP blocks

Figure 1. MiMo-V2.6 architecture.

2. Downloads

ModelDownload
MiMo-V2.6-Pro-RL🤗 HuggingFace · 🤖 ModelScope
MiMo-V2.6-Flash-RL🤗 HuggingFace · 🤖 ModelScope
MiMo-V2.6-Pro-MOPD🤗 HuggingFace · 🤖 ModelScope
MiMo-V2.6-Flash-MOPD🤗 HuggingFace · 🤖 ModelScope

3. Model Architecture

LLM Backbone

ComponentMiMo-V2.6-Flash-MOPD
Layers (Total / SWA / GA)48 / 39 / 9
Hidden Size4096
SWA Heads (Q/KV)64 / 8
GA Heads (Q/KV)64 / 4
Head Dimensions (QK / V)192 / 128
Sliding Window Size128
Routed Experts (Total / Activated)256 / 8
Max Context Length1M
MTP / Speculative Decoder5 SWA layers, window 1024

The first Transformer block uses global attention with a dense FFN. Remaining blocks interleave local SWA and GA; both use sparse MoE FFNs without shared experts.

Vision Encoder (MiMo ViT)

ConfigurationValue
Layers (Total / SWA / GA)28 / 24 / 4
Hidden Size1280
Attention Heads (Q / KV)32 / 8
Head Dimension64
Patch Size (T × H × W)2 × 16 × 16
Sliding Window (Left / Right)64 / 64
Spatial Merge Size2 × 2
Parameters681M

Audio Encoders

AudioTokenizer encoder: 24 layers (12 SWA / 12 GA), hidden 1024, 20 RVQ codebooks, 308M parameters. Audio patch encoder: 6 layers, 127M parameters; four frames per patch (25 Hz → 6.25 Hz).

Speculative Decoder

5-layer SWA MTP drafter (DFlash-style). Predicts 7 subsequent tokens per forward pass for parallel verification.

4. Deployment

For best performance, follow the SGLang MiMo cookbook. Docker image: lmsysorg/sglang:latest.

SGLang

sglang serve \
  --trust-remote-code \
  --model-path XiaomiMiMo/MiMo-V2.6-Flash-MOPD \
  --tp 8 \
  --dp 2 \
  --enable-dp-attention \
  --enable-dp-lm-head \
  --mm-enable-dp-encoder \
  --mem-fraction-static 0.65 \
  --chunked-prefill-size 16384 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --enable-multi-layer-eagle \
  --reasoning-parser mimo \
  --tool-call-parser mimo \
  --host 0.0.0.0 \
  --port 30000

vLLM

Follow the vLLM MiMo-V2.5 recipe. Stable vLLM may lag; pre-built image: docker pull vllm/vllm-openai:mimov25-cu129.

vllm serve XiaomiMiMo/MiMo-V2.6-Flash-MOPD \
  --tensor-parallel-size 4 \
  --trust-remote-code \
  --gpu-memory-utilization 0.95 \
  --max-model-len auto \
  --reasoning-parser mimo \
  --tool-call-parser mimo \
  --enable-auto-tool-choice \
  --generation-config vllm

Recommended sampling: temperature=1.0, top_p=0.95.

Also available in AI Studio, MiMo Code, Xiaomi MiMo Desktop, Xiaomi MiMo Open Platform API, and OpenRouter.

Citation

@misc{mimo2026v26flashmopd,
  title={MiMo-V2.6-Flash-MOPD},
  author={{Xiaomi MiMo Team}},
  year={2026},
  howpublished={\url{https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD}},
}

Contact

For questions or feedback, reach us at mimo@xiaomi.com or join our community:

Use Restrictions

You are solely responsible for your use of XiaomiMiMo/MiMo-V2.6-Flash-MOPD and any outputs, actions, or consequences arising therefrom, and you agree not to use the XiaomiMiMo/MiMo-V2.6-Flash-MOPD or any derivatives thereof:

  • For any unlawful, fraudulent, or malicious purpose, or in any manner that violates any applicable laws or regulationsï¼›
  • To infringe upon the intellectual property rights, privacy rights, publicity rights, or other lawful rights or interests of any third party;
  • To exploit, harm, harass, defame, unlawfully discriminate against, or otherwise adversely affect any individual or group, including minors or vulnerable persons;
  • For any military purpose or application;
  • To engage in or facilitate malicious cyber activities, including but not limited to malware, ransomware, phishing, credential theft, or unauthorized access, or any activity intended to compromise or unlawfully control computer systems, networks, or encryption systems;
  • To access, collect, process, disclose, or disseminate personal data or other protected information without appropriate authorization or lawful basis; or to access, interact with, scrape, or automate any third-party platform or service in violation of any applicable laws, policies, or authorization requirements;
  • To use the XiaomiMiMo/MiMo-V2.6-Flash-MOPD in a manner that autonomously executes high-risk actions without appropriate human oversight or authorization.

This notice is an informational reminder only and does not amend the applicable model licence or impose additional licence conditions. The permissions granted under that licence remain unaffected. Nothing in this notice restricts uses permitted by applicable law, including applicable exceptions and limitations.

Usage enquiries and complaints

mimo-legal@xiaomi.com. For information on submitting a complaint, please visit the https://mimo.xiaomi.com.