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Production multimodal systems must serve complex vision-language tasks under strict latency, memory, and cost constraints. Frontier multimodal large language models (MLLMs) deliver strong reasoning but are prohibitively expensive for online serving; compact alternatives fit deployment budgets but lack structured reasoning ability. We present OmniThoughtVis, a scalable distillation pipeline that converts open-source multimodal corpora into deployment-ready chain-of-thought (CoT) supervision for compact MLLMs. Starting from a diverse seed pool, the pipeline generates structured reasoning traces from a high-capacity teacher, jointly annotates reasoning difficulty, answer quality, and semantic task tags, and applies difficulty-aware filtering with tag-based diversity sampling to produce a reusable 1.8M-sample candidate pool. Practitioners construct budget-specific training subsets from this pool without re-running distillation. A key practical finding is that reasoning difficulty is a far more effective data selection signal than answer quality, which becomes uninformative when teacher outputs are already strong. Distilled Qwen3-VL models (2B-8B) consistently improve over undistilled baselines across nine benchmarks, with the distilled 4B model matching or exceeding the undistilled 8B on multiple tasks. In production, replacing a large-model serving pipeline with the distilled compact model reduces end-to-end response latency by 79% while maintaining comparable downstream quality.