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MiMo V2.6 Flash RL

MiMo V2.6 Flash RL is a text-generation model from XiaomiMiMo released under the MIT license. Its official model card describes it as the efficiency-balanced checkpoint of the MiMo-V2.6 series, built to scale reinforcement learning toward self-improvement. The card lists native omnimodal support for text, image, video, and audio, long-context capabilities stated as 1M tokens, and a mixed RL training approach covering coding, general agents, visual, and cybersecurity domains. It also describes groupwise agentic grading and multi-prefix multi-teacher on-policy distillation. The repository tags include safetensors and text-generation.

EstimatedRepository opened Sep 21, 2026Source checked 9/23/2026Version: 5711b268
LOCALRENTED GPUOPEN WEIGHTS
Freshness 91 out of 100. Repository age: 24 days old, Sep 21, 2026. Measures age, not quality.
Popularity 67 out of 100. 84,797 downloads in 30 days; 557 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source
Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source
Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

At a glance

Parameters
310.76B
Architecture
mimo_v2
Context length
1,048,576
License
mit
Software
SGLang, vLLM
View the model source
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Best for

  • Long-context text generation and processing of repositories or tool traces
  • Multi-session agent runs and general agent tasks
  • Coding-related language tasks
  • Visual, video, and audio understanding tasks as described in the model card
  • Cybersecurity-related tasks as described in the mixed RL training domains

Tradeoffs

  • The provided content does not include verified evaluation results, performance benchmarks, or hardware requirements
  • No compatibility or deployment details beyond transformers and safetensors tags are provided
  • No independent testing or validation is described
  • The model card is partially truncated, so the full details of some training methods are unavailable
  • Capabilities such as multimodal and long-context support are stated in the source model card and are not independently confirmed here

Ways to run it

SGLang · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

sglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --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 \
Official instructions

vLLM · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

vllm serve XiaomiMiMo/MiMo-V2.6-Flash-RL --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
Official instructions

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