# MiMo-V2.6-Pro-RL ![Organization](https://img.shields.io/badge/Org-Xiaomi-FF6900) ![License](https://img.shields.io/badge/License-MIT-2da44e) ![Params](https://img.shields.io/badge/Params-1.02T%20(42B%20active)-8957e5) ![Context](https://img.shields.io/badge/Context-1M%20tokens-blue) ![Released](https://img.shields.io/badge/Released-Sep%202026-555) > **Quick answer:** MiMo-V2.6-Pro-RL is the flagship checkpoint of Xiaomi's MiMo-V2.6 series: a 1.02T-parameter sparse MoE (42B active) with text, image, video and audio input and a 1M-token context, released September 21, 2026 under the MIT license. It was trained with one mixed, large-scale RL run across coding, general agents, vision and cybersecurity, and reports 89.9 on Terminal-Bench 2.1, 71.9 on DeepSWE v1.1 and 76.9 on Toolathlon-Verified. ## At a Glance **Where it leads:** Open-weight agentic coding and tool use close to frontier closed models — within about 2 points of Claude Opus 5 on DeepSWE (74.0) and ahead of GPT-5.6 Sol on Terminal-Bench 2.1 (89.9 vs 88.8) in Xiaomi's table. **Where it lags:** Terminal-Bench 4.0 (34.9 vs Opus 5's 49.0), ExploitBench (47.9 vs 70.0-78.5) and ExploitGym trail the closed frontier models. **Best for:** Self-hosted, long-horizon coding and computer-use agents that need an MIT-licensed omnimodal model. ## What MiMo-V2.6-Pro-RL Is MiMo-V2.6 scales reinforcement learning toward self-improvement: fully asynchronous GRPO on very large batches (1,568 prompts x 16 rollouts per step), groupwise agentic grading that builds task-specific rubrics from contrasting rollouts, and multi-prefix multi-teacher on-policy distillation to extend capabilities to hard-to-verify tasks. The backbone interleaves sliding-window (60 layers) and global attention (10 layers) over 70 layers with 384 routed experts (8 active), plus a 681M-parameter vision encoder, audio encoders and a 5-layer multi-token-prediction drafter. It is available as open weights (FP8) and through the Xiaomi MiMo API platform, AI Studio, MiMo Code and OpenRouter. A smaller sibling, [MiMo-V2.6-Flash-RL](/models/xiaomi/mimo-v2-6-flash-rl), has 309B total and 15B active parameters. ## Specifications | Field | Value | |---|---| | Organization | Xiaomi | | Hugging Face | [XiaomiMiMo/MiMo-V2.6-Pro-RL](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) | | Architecture | Sparse MoE, 1.02T total / 42B active | | Context length | 1M tokens | | Modalities | Text, image, video, audio | | License | MIT | | Release date | 2026-09-21 | ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [DeepSWE](/benchmarks/datacurve/deepswe) (v1.1) | 71.9 | [Model card](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) | 2026-09 | | [Terminal-Bench 4.0](/benchmarks/anthropic/terminal-bench-40) | 34.9 | [Model card](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) | 2026-09 | | [Terminal-Bench 2.1](/benchmarks/tbench/terminalbench-21) | 89.9 | [Model card](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) | 2026-09 | | [Toolathlon-Verified](/benchmarks/hkust-nlp/toolathlon-verified) | 76.9 | [Model card](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) | 2026-09 | | [OSWorld-Verified](/benchmarks/osworld/osworld-verified) | 82.0 | [Model card](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) | 2026-09 | | [JobBench](/benchmarks/li-et-al/jobbench) | 62.0 | [Model card](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) | 2026-09 | | [GDPval-AA v2](/benchmarks/artificial-analysis/gdpval-aa-v2) (v2.1) | 1673 | [Model card](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) | 2026-09 | | [ExploitBench](/benchmarks/seunghyun-lee-david-brumley/exploitbench) | 47.9 | [Model card](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) | 2026-09 | | [SEC-Bench Pro](/benchmarks/sec-bench-pro) | 66.3 | [Model card](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) | 2026-09 | *Self-reported by Xiaomi; not independent Benchgen measurements. Also reported but not added: ProgramBench 26.5 and Agents' Last Exam 31.6 (metric differs from the Benchgen pages), AutomationBench v1.0.6 53.1 (version-mismatch precedent), CyberGym 94.0 and ExploitGym 17.8 (scale differs from the Benchgen pages), and Xiaomi-internal MiMo Code Bench, MiMo Cyber Bench and MiMo VisualCoding.* ## Frequently Asked Questions

What is MiMo-V2.6-Pro-RL?

Xiaomi's flagship MiMo-V2.6 checkpoint: a 1.02T-parameter omnimodal MoE (42B active) with a 1M-token context, released September 21, 2026 under the MIT license.

Is MiMo-V2.6-Pro-RL open source?

Yes, the weights are published on Hugging Face under the MIT license; it is also available via the Xiaomi MiMo API platform and OpenRouter.

How does it compare with Claude Opus 5 and GPT-5.6 Sol?

In Xiaomi's own table it is within a few points on DeepSWE v1.1 (71.9 vs 74.0 and 73.0) and Terminal-Bench 2.1, but behind on Terminal-Bench 4.0 and the cybersecurity benchmarks.

MiMo-V2.6-Pro-RL — Training jobs

Training jobs