It handles text, image, and video inputs, produces text output, and works with a context window of up to 1M tokens — making it a strong fit for extended agentic workflows, coding, and tool use. Under the hood it uses MiniMax Sparse Attention (MSA), replacing full attention with KV-block selection to significantly reduce per-token compute at long contexts — roughly 1/20 the cost of the previous generation at 1M tokens, with notably faster prefill and decode while preserving quality across most tasks.
The model was trained as a natively multimodal system on interleaved data and fine-tuned for multi-turn, production-style collaboration through an interactive user-simulator framework. It's designed for sustained, multi-step tasks rather than one-shot execution.
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