Tokenization Disparities Overview

Updated 21 October 2025

Tokenization disparities are systematic differences in how algorithms segment text across languages, leading to varying token counts and performance impacts.
The use of frequency-based subword methods like BPE and SentencePiece results in high efficiency for dominant languages and inflated token counts for low-resource or morphologically complex languages.
Mitigation strategies include parity-aware, adaptive, and morphology-informed tokenization methods aimed at reducing computational costs and promoting inclusivity.

Tokenization disparities refer to systematic differences in how tokenization algorithms segment linguistic input across diverse languages, domains, or dialects, with substantial downstream consequences for model performance, computational efficiency, economic cost, and accessibility. These disparities are especially pronounced in multilingual, morphologically rich, or low-resource settings, where standard subword tokenizers—typically optimized for high-resource, Latin-script languages—exacerbate both technical and social inequities.

1. Sources and Manifestations of Tokenization Disparities

Tokenization disparities arise primarily from the interaction between algorithmic design choices and language-specific characteristics. Standard subword methods such as Byte-Pair Encoding (BPE), SentencePiece, and WordPiece learn vocabularies by maximizing global frequency-based objectives on the dominant languages present in training corpora. As a result, high-resource or Latin-script languages achieve high tokenization efficiency—measured as low tokens-per-sentence (TPS) and low relative tokenization cost (RTC)—while non-Latin and morphologically complex languages suffer from excessive token inflation, fragmented segmentation, and even the presence of <UNK> placeholders (Foroutan et al., 6 Aug 2025, Teklehaymanot et al., 14 Oct 2025, Petrov et al., 2023, Ahia et al., 11 Jul 2024).

Tokenization also amplifies disparities in computational and financial terms. For example, the segmentation of language samples with complex scripts (e.g., Myanmar, Ol Chiki, Oriya) can require 3–5 times as many tokens per equivalent sentence as English (Teklehaymanot et al., 14 Oct 2025), directly impacting the effective context window, latency, and per-token pricing in commercial LLM APIs (Solatorio et al., 14 Oct 2024).

2. Key Algorithmic Factors Driving Disparities

Three interrelated design choices account for the majority of tokenization disparities (Wegmann et al., 21 Feb 2025, Schmidt et al., 28 Feb 2024):

Vocabulary Construction and Fitting Corpus: The language mix and style represented in the fitting corpus determine which word forms and variants constitute atomic tokens. Dominant corpus languages contribute frequent subwords, relegating less frequent or morphologically diverse words (e.g., dialectal or low-resource forms) to fragmented representations.
Pre-tokenizer Design: The pre-tokenizer governs how character categories (letters, numbers, punctuation) are initially segmented. As shown in systematic comparisons between basic, whitespace, “llama3,” and aggressive “gpt2” pre-tokenizers, the manner in which boundaries are introduced (or not) exerts the greatest downstream influence, particularly in applications sensitive to form and orthography (Wegmann et al., 21 Feb 2025).
Vocabulary Size: The level of vocabulary granularity determines whether morphologically rich or regionally variable words are encoded as single or split tokens; larger vocabularies (e.g., 64k vs. 4k) can accommodate more distinct forms, which benefits tasks sensitive to subtle variation (e.g., authorship verification), but smaller vocabularies may provide robustness by forcing character-level composition.

These algorithmic choices are more consequential for tasks that demand sensitivity to stylistic or orthographic cues compared to robust semantic tasks such as natural language inference (NLI), where the meaning is preserved despite tokenization-induced perturbations (Wegmann et al., 21 Feb 2025).

3. Empirical Evidence and Scaling Implications

Large-scale cross-linguistic evaluations provide quantitative confirmation of tokenization disparities. The key metrics employed are:

Metric	Definition	Interpretation
TPS (Tokens Per Sentence)	TPS(L) = (1/N) × Σ₍ᵢ₌₁₎ᴺ Tᵢ	Directly measures token density post-tokenization for language L
RTC (Relative Tokenization Cost)	RTC(L) = TPS(L) / TPS(English)	Tokenization cost relative to English baseline

Empirical results consistently show that Latin-script languages (e.g., English) have lower TPS and RTC, with mean values around 50 tokens/sentence. By contrast, non-Latin languages (e.g., Myanmar, Ol Chiki, Oriya) display TPS values of 334–357, translating into RTCs as high as 7.0, that is, a sevenfold token inflation for equivalent content (Teklehaymanot et al., 14 Oct 2025, Petrov et al., 2023). Morphologically rich African languages, when processed in the AfriMMLU benchmark, exhibit both higher TPS and a negative correlation between token density (“fertility”) and accuracy, with increases in tokens/word leading to systematic reductions in model performance by up to 18 percentage points per additional token (Lundin et al., 5 Sep 2025).

The economic impact of inflated tokenization is quadratic in sequence length for transformer architectures (O(n²)), meaning that a doubling in token count may incur up to 4× higher training and inference cost—for instance, raising a $105M training run to$420M for equivalently longer inputs (Lundin et al., 5 Sep 2025). Combined with context window saturation, this compounds latency and financial access barriers for underrepresented languages (Solatorio et al., 14 Oct 2024).

4. Task Sensitivity and Downstream Impact

Tokenization disparities do not affect all applications uniformly. The impact is highly task-dependent:

Robust Semantic Tasks: For NLI or semantic similarity, model accuracy is less vulnerable to tokenization-induced form variation. Embeddings can still capture semantic content despite orthographic diversity or spelling differences.
Form-Based and Sensitive Tasks: Tasks such as authorship verification, dialect classification, or error detection rely on surface form details and are disproportionately sensitive to the fragmentation or merging of distinctive subword units. The relative performance gap across tokenizer configurations is larger for these applications, with certain vocabulary and pre-tokenizer settings proving essential to preserving stylistic markers (Wegmann et al., 21 Feb 2025).
Symbolic and Numerical Reasoning: In arithmetic, counting, or symbolic sequence tasks, subword merging obscures atomic computation units (e.g., digits, individual letters), fundamentally limiting reasoning performance even with Chain-of-Thought (CoT) prompting. Only when token granularity aligns with the compositional requirements (atomic tokens per basic item) do models achieve high accuracy (Zhang et al., 20 May 2025, Zhang et al., 25 Oct 2024, Singh et al., 22 Feb 2024).
Human Interpretability and ML Performance: Human evaluators often prefer linguistically intuitive, dictionary-based segmentations, whereas ML models benefit from statistically optimal, LLM–oriented token boundaries. There is no guarantee that the tokenization best for humans achieves optimal machine performance (Hiraoka et al., 2023).

5. Structural Inequities and Broader Implications

Tokenization disparities are a source of structural bias and economic inequity in artificial intelligence infrastructure:

Resource Disparity: Speakers of low-resource, non-Latin-script, or morphologically rich languages face higher per-token costs, lower accuracy, and reduced performance—an effect termed the “token tax” or infrastructure bias (Teklehaymanot et al., 14 Oct 2025, Lundin et al., 5 Sep 2025). The economic penalties extend to access in paid LLM APIs (4–6× higher costs for marginalized languages) and compounded environmental costs due to elevated compute and energy consumption (Solatorio et al., 14 Oct 2024).
Data and Security Bias: Tokenizer vocabularies trained on unbalanced or regionally restricted data can amplify privacy and ethical risks by encoding content from low-quality or sensitive sources (e.g., gaming or pornography in Chinese), which further propagates unwanted bias and privacy concerns through the models (Yang et al., 17 Jun 2024).

These inequities highlight the need for intervention at the subword segmentation and vocabulary design level rather than solely at the architectural or training stage.

6. Mitigation Strategies and Fairness-Oriented Proposals

Recent research has advanced algorithmic strategies to address tokenization disparities:

Parity-Aware Tokenization: New variants of BPE, such as Parity-aware BPE, select merges that maximize compression for the worst-compressed language at each step, directly reducing the disparity in token counts with minimal loss in global compression or performance (Foroutan et al., 6 Aug 2025). The objective is formalized as:

$T^* = \arg\max_{T: |T| = K} \ \min_{l \in L} CR(D_l; T)$

where $CR(D_l; T)$ is the compression rate on language $l$ under tokenizer $T$ .

Adaptive and Typology-Aware Methods: Adaptive gradient-based approaches, e.g., MAGNET, assign language/script-specific boundary predictors to tailor token segmentation for each typological class, achieving uniform compression rates and more equitable segmentation granularity (Ahia et al., 11 Jul 2024).
Morphologically Aware Tokenization: Recommendations include designing tokenizers to account for the morphological characteristics of underrepresented languages to reduce unnecessary token inflation (Lundin et al., 5 Sep 2025, Rahman et al., 4 Oct 2024).
Cost-Efficient Evaluation: Task-specific intrinsic evaluation metrics (e.g., logistic regression using bag-of-token features to predict downstream performance) provide efficient means of estimating the practical impact of tokenizer choices before costly large-scale pretraining (Wegmann et al., 21 Feb 2025).
Linguistically-Informed Development and Internationalization: Calls for integration of linguistics expertise in vocabulary construction, development of multilingual benchmarks, and the publication of “model cards” that outline tokenization-induced costs for transparency (Rahman et al., 4 Oct 2024, Teklehaymanot et al., 14 Oct 2025).

7. Open Problems and Research Directions

Current evidence indicates that no single tokenizer can optimize for all languages or task requirements. Remaining challenges and recommended future directions include:

Development of tokenization algorithms that adapt contextually to different scripts, morphologies, or dialectal forms.
Intrinsic and extrinsic measures that more accurately capture the sociotechnical impact of tokenization settings.
Alignment of tokenization strategies with both human interpretability (readability, linguistic naturalness) and machine utility (model performance, efficiency).
Systematic evaluation of the environmental impact of tokenization choices in relation to economic cost and inclusivity on a global scale (Solatorio et al., 14 Oct 2024).
Research into tokenization-free or multi-scale tokenization approaches that jointly optimize for fairness, efficiency, and model capability (Schmidt et al., 28 Feb 2024, Chai et al., 17 Jun 2024).

Tokenization disparities are a fundamental axis of bias and inefficiency in modern NLP infrastructure, impacting linguistic equity, economic accessibility, and technical performance. Addressing these disparities requires algorithmic innovation, typologically informed design, and a holistic approach to evaluation and deployment.