The remarkable success of Large Language Models (LLMs) across diverse tasks has driven the research community to extend their capabilities to molecular applications. However, most molecular LLMs employ adapter-based architectures that fail to equally integrate molecule and text modalities and lack explicit supervision signals for the molecular modality. To address these issues, we introduce UniMoT, a Unified Molecule-Text LLM adopting a tokenizer-based architecture that expands the vocabulary of LLMs with molecule tokens. Specifically, we introduce a Vector Quantization-driven tokenizer that incorporates a Q-Former to bridge the modality gap between molecule and text. This tokenizer transforms molecular structures into sequences of tokens exhibiting causal dependency, thereby encapsulating both high-level molecular features and textual information. Equipped with this tokenizer, UniMoT unifies molecule and text modalities under a shared token representation and an autoregressive training paradigm. This enables the model to process molecular structures as a distinct linguistic system and generate them in textual form. Through a four-stage training scheme, UniMoT functions as a multi-modal generalist capable of performing both molecule-to-text and text-to-molecule tasks. Extensive experiments demonstrate that UniMoT achieves state-of-the-art performance across a wide range of molecule comprehension and generation tasks.