MiMo-V2.6-Pro vs. Inkling 2 Small

Inkling 2 Small

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What is MiMo-V2.6-Pro?

MiMo-V2.6-Pro is Xiaomi MiMo’s flagship open-source omnimodal model for software engineering, agentic automation, multimodal reasoning, visual design, research, and creative production. The model was developed through large-scale reinforcement learning on heterogeneous tasks spanning coding, general agents, visual workflows, and cybersecurity. Xiaomi trained MiMo-V2.6-Pro across roughly 750,000 trajectories using large asynchronous batches, long-context training, multi-task environments, and expanded grader compute. The resulting model is designed to plan, execute, verify, and refine complex work across multiple tools and interaction environments. In software development, MiMo-V2.6-Pro supports long-horizon coding, terminal work, automation, debugging, and other agent-driven engineering tasks. Its multimodal capabilities allow it to generate interactive 3D worlds, create Blender assets from text or reference images, and control simulated robotic systems using continuous visual feedback. The model can also build frontend interfaces, design slide decks, work with Figma and media-generation tools, and automate portions of video production from concept through editing and narration. Creative capabilities extend to music composition, including generating arrangements, musical scores, and MIDI output. For research, MiMo-V2.6-Pro has been demonstrated performing literature searches, generating scientific hypotheses, running computational tools, screening materials, and assisting with formal mathematical proofs. Xiaomi has open-sourced the model family together with its technical report, reinforcement learning environments, and training code to support reproducibility and further research. MiMo-V2.6-Pro is available through Xiaomi MiMo’s desktop and developer products, OpenRouter, Hugging Face, and an API, with an UltraSpeed version offered for workflows that require much faster generation.

What is Inkling 2 Small?

Inkling 2 Small is an advanced multimodal large language model developed by Thinking Machines Lab to support autonomous coding, complex reasoning, visual understanding, and customizable AI applications. The model uses a sparse mixture-of-experts transformer architecture containing 594 billion total parameters and 17 billion active parameters, with 48 decoder layers and 384 specialized experts. Its architecture combines local and global attention mechanisms with a vision transformer that processes images alongside text within a shared representation. Inkling 2 Small supports a context window of up to one million tokens, making it suitable for analyzing extensive documents, large software repositories, and long-running agent workflows. Configurable reasoning effort enables users to adjust the computational resources devoted to individual tasks based on their complexity and performance requirements. Its agentic software engineering capabilities include generating code, debugging applications, implementing features, verifying changes, and operating development tools across extended workflows. The model can also create interactive websites, games, three-dimensional simulations, and visual applications while using image-based feedback to evaluate and improve its results. Advanced research capabilities allow autonomous agents to conduct machine learning experiments, optimize GPU kernels, evaluate training strategies, and iteratively refine technical solutions. Thinking Machines Lab trained the model using 8.4 trillion pretraining tokens and a large reinforcement learning process involving more than 17 million rollouts across coding, scientific reasoning, visual tasks, knowledge work, and tool use. Published evaluation results include 25.4% on FrontierCode 2.0, 72.1% on DeepSWE 1.1, 86.3% on SWE-Bench Pro v2 Hard, and an Artificial Analysis Intelligence Index score of 31.