Xiaomi MiMo-V2.6 Becomes Top Open-Source AI Model, Beats DeepSeek and Rivals Claude at $0.13 Per Task

Xiaomi MiMo-V2.6 Claims Top Open-Weight AI Model Position

Xiaomi released MiMo-V2.6-Pro and MiMo-V2.6-Flash, positioning its flagship as the highest-ranking open-source AI model on the Artificial Analysis Intelligence Index with a score of 461

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. The smartphone and electric vehicle manufacturer's latest generative AI models surpass fellow Chinese open-weight AI models including Moonshot's Kimi K3 and Alibaba's Qwen3.8 Max, which previously traded the open-weight lead throughout 20262

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. MiMo-V2.6-Pro ties with xAI's newly released Grok 4.7 and outperforms Google's Gemini 3.8 Flash, currently at 41, along with DeepSeek V4.1 Flash at 39 and DeepSeek V4.1 Pro at 363

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. Released under an MIT license via Hugging Face, both omnimodal AI models accept text, image, video, and audio inputs with a 1-million-token context window2

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Cost Efficiency Drives Production Adoption

Artificial Analysis rates MiMo-V2.6-Pro as the cheapest model it tracks at $0.13 per task, with API pricing at $0.435 per million input tokens and $0.87 per million output tokens2

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. MiMo-V2.6-Flash targets high-volume production workloads at $0.14 per million input tokens and $0.28 per million output tokens—roughly one-third of Pro's cost while maintaining the same 1-million-token context window and multimodal capabilities2

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. Xiaomi claims Pro costs one-twentieth to one-sixtieth as much as comparable overseas models, particularly against OpenAI's GPT-6 Astra and Anthropic's Claude Fable 5.1, which charge $10 to $50 per million tokens4

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. For developers and enterprises running millions of agentic AI calls, the operating cost gap between $0.87 per million output tokens versus $20 for Claude Opus 5.5 becomes substantial2

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. The Flash variant is available free through OpenRouter, democratizing access for small-scale projects and indie developers5

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. A third variant, Pro-UltraSpeed, generates up to 20 times faster than Pro at the same quality but costs ten times more at $4.35 input and $8.70 output per million tokens2

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Reinforcement Learning Breakthrough in Six Days

Xiaomi's gains stem from one large reinforcement learning run completed in under six days, with Pro costing approximately $2.62 million to train and Flash around $0.85 million2

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. The company streamed the training process live, showing Pro and Flash each completing 30 training steps across roughly 750,000 trajectories2

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. On DeepSWE, a held-out software engineering benchmark, Pro climbed from 58.4 to 72.57 across the run while Flash improved from 48.8 to 65.682

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. Unlike most labs that run separate training jobs per domain, Xiaomi mixed coding, general agents, visual work, and cybersecurity into one reinforcement learning run so gains in each domain would reinforce the others2

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. The company detailed its defences against reward hacking after early agents solved assigned bugs by fetching fixes from later package versions rather than writing them, prompting Xiaomi to strip build caches and future Git history from training environments2

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Sparse Mixture-of-Experts Architecture Powers Performance

MiMo-V2.6-Pro operates as a sparse mixture-of-experts model with 1.02 trillion parameters total, of which 42 billion activate for any given token—keeping inference costs low at that scale2

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. The published architecture includes a 681-million-parameter vision encoder with a 308-million-parameter AudioTokenizer and 127-million-parameter audio patch encoder capable of distinguishing speech4

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. This design enables agentic AI use cases where a computer-use agent reads task instructions, processes screenshots or video, reasons about context, and decides next actions within one model loop4

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. Coding agents can combine source code and lengthy repository context with screenshots, UI mockups, and reference images in the same session without requiring tools to break down transcripts or orchestrate each modality4

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. Artificial Analysis measures output speed at roughly 134 tokens per second, confirming the 1-million-token context window with multimodal inputs3

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Benchmark Performance Shows Strengths and Gaps

On agentic AI tasks, V2.6-Pro scored 53.1 on AutomationBench versus Claude Opus 5's 50.3, tied Opus 5 at 31.6 on Agents' Last Exam, and reached 89.9 on Terminal Bench 2.1, slightly ahead of Opus 5's 89.14

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. Flash trails Pro by four points or fewer on most agent benchmarks, with DeepSWE at 67.9 against Pro's 71.9, AutomationBench at 52.3 against 53.1, and JobBench at 61.2 against 62.02

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. On one cybersecurity test, CyberGym, Flash beats Pro at 95.1 to 94.0, though Pro leads on three other cyber benchmarks2

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. However, Claude Opus 5 beats Pro on DeepSWE and ProgramBench, with a wide gap on Terminal Bench 4.0 at 49.0 against 34.9, where GPT-6 Astra reaches 59.62

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. Artificial Analysis scores Opus 5.5 at 58 against MiMo's 46, indicating Xiaomi leads the open-weight field but not the overall frontier2

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Open-Sourcing Complete Training Infrastructure

Beyond releasing model weights, Xiaomi open-sourced over 7,000 reinforcement learning task environments with automatic graders, the end-to-end RL framework, composable mini-harnesses, reward design, hyperparameters, data mixtures, and training costs1

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. The tasks cover software development, cybersecurity, office work, web design, and roughly a thousand for music composition2

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. Xiaomi stated the goal is reproducibility and invited researchers to verify results2

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. This published RL framework enables smaller labs to run experiments on smaller models, potentially changing what well-funded research teams can attempt2

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. Both models run in Xiaomi's AI Studio, MiMo Code and MiMo Desktop apps, through its API platform, and on OpenRouter, while weights are available on Hugging Face for self-hosting2

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3D Spatial Reasoning Enables Creative Applications

Xiaomi demonstrated MiMo-V2.6's 3D spatial reasoning capabilities through "Vibe World," where the model builds interactive 3D environments such as games, generates models for Blender, or uses multi-camera feeds to control a Franka Panda robotic arm using just image, video, or text prompts1

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. Beyond visual assets, the model generates music and composes demo-quality pieces1

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. Game designers can create complex and immersive 3D environments while artists craft intricate Blender scenes with minimal manual intervention5

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. The model automates repetitive desktop tasks and excels in multimodal AI applications combining inputs from various modalities for video analysis and cross-sensory data interpretation5

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. For robotics control, MiMo-V2.6-Pro enhances precision and adaptability in autonomous systems supporting manufacturing and logistics applications5

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