Unverified60% confidenceFactExact time
Different models use different tokenization schemes (such as BPE, SentencePiece, etc.), and the same piece of text may be counted as a different number of tokens across different models.
2
Sources
60%
Confidence
Long-term
Relevance
8/28/2026
First Seen
Sources
Related Entities
Related Claims
Unverified不同模型使用不同的分词算法，如 OpenAI 的 tiktoken、Google 的 SentencePiece，导致同一段文字在不同模型中消耗的 token 数量可能相差 10%-30%74% similarVerifiedModern large models use tokenization algorithms such as BPE or SentencePiece to build vocabularies, with mainstream models having vocabulary sizes ranging from 30,000 to 150,000 tokens.73% similarUnverifiedSynonym normalization can be achieved by using sentence embedding models such as Sentence-BERT to map words into dense vectors, and then computing semantic proximity via cosine similarity.73% similarVerifiedMainstream large models generally use Byte Pair Encoding (BPE) or its variants (such as SentencePiece and tiktoken) for tokenization.72% similarUnverified仅仅改变输入文本的措辞和结构，就能让同一个模型的性能产生数十个百分点的差异70% similar
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