pith. machine review for the scientific record. sign in

arxiv: 2604.14210 · v1 · submitted 2026-04-06 · 💻 cs.CL · cs.SE

Recognition: 1 theorem link

· Lean Theorem

Chinese Language Is Not More Efficient Than English in Vibe Coding: A Preliminary Study on Token Cost and Problem-Solving Rate

Ankit Raj, Dennis (Tsang) Ng, Simiao Ren, Xingyu Shen, Yuchen Zhou

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:16 UTC · model grok-4.3

classification 💻 cs.CL cs.SE
keywords chinese promptstoken efficiencyllm codingswe-benchsuccess ratecost per taskmultilingual promptingvibe coding
0
0 comments X

The pith

Empirical tests find no token efficiency advantage for Chinese prompts in LLM coding tasks, with lower success rates than English.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests the circulating claim that Chinese prompts save tokens and reduce costs for large language model coding tasks compared to English. Using controlled experiments on software engineering problems, it measures both token usage and whether tasks are actually solved. No general efficiency gain appears, and success rates fall when prompts switch to Chinese. Token behavior varies by model, with some using more tokens for Chinese and others fewer. Developers considering language switches for cost savings in AI coding should instead track full cost per completed task, as the expected benefits do not materialize.

Core claim

A direct comparison on SWE-bench Lite shows that Chinese prompts produce no consistent token savings over English across tested models. One model incurs 1.28 times higher token costs with Chinese, while another uses fewer tokens; success rates on the same tasks are lower for Chinese in every case. When cost is measured as expected expense per successfully solved task, the joint metric does not favor Chinese. The authors present these outcomes as preliminary evidence that language effects on token cost are model-dependent rather than a general rule.

What carries the argument

Controlled side-by-side measurement of token counts and task resolution rates on identical SWE-bench Lite coding problems, using both Chinese and English prompts across multiple models.

Load-bearing premise

The results assume that the small set of models and the narrow software engineering tasks examined are representative of broader LLM coding use.

What would settle it

A larger evaluation across more models and diverse coding benchmarks that finds consistent 20-40 percent token reductions plus equal or higher success rates with Chinese prompts would falsify the main claim.

Figures

Figures reproduced from arXiv: 2604.14210 by Ankit Raj, Dennis (Tsang) Ng, Simiao Ren, Xingyu Shen, Yuchen Zhou.

Figure 1
Figure 1. Figure 1: Graphical abstract showing the research workflow and key findings. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The popular social media claim that Chi [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comprehensive results visualization show [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: ZH/EN token ratio by model and token type (English = 1.0 baseline). Values above 1.0 indicate [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Token count ratio (Chinese/English) across [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Character efficiency (chars/token) for En [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

A claim has been circulating on social media and practitioner forums that Chinese prompts are more token-efficient than English for LLM coding tasks, potentially reducing costs by up to 40\%. This claim has influenced developers to consider switching to Chinese for ``vibe coding'' to save on API costs. In this paper, we conduct a rigorous empirical study using SWE-bench Lite, a benchmark of software engineering tasks, to evaluate whether this claim of Chinese token efficiency holds up to scrutiny. Our results reveal three key findings: First, the efficiency advantage of Chinese is not observed. Second, token cost varies by model architecture in ways that defy simple assumptions: while MiniMax-2.7 shows 1.28x higher token costs for Chinese, GLM-5 actually consumes fewer tokens with Chinese prompts. Third, and most importantly, we found that the success rate when prompting in Chinese is generally lower than in English across all models we tested. We also measure cost efficiency as expected cost per successful task -- jointly accounting for token consumption and task resolution rate. These findings should be interpreted as preliminary evidence rather than a definitive conclusion, given the limited number of models evaluated and the narrow set of benchmarks tested due to resource constraints; they indicate that language effects on token cost are model-dependent, and that practitioners should not expect cost savings or performance gains just by switching their prompt language to Chinese.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The paper conducts an empirical evaluation on SWE-bench Lite to test the circulating claim that Chinese prompts are more token-efficient than English for LLM coding tasks, potentially saving up to 40% in costs. Using a small set of models, it measures token consumption and task success rates for equivalent prompts in each language, finding no general Chinese efficiency advantage (with model-dependent ratios, e.g., 1.28x higher cost for Chinese in MiniMax-2.7 but lower in GLM-5), consistently lower success rates for Chinese prompts, and lower overall cost efficiency when measured as expected cost per successful task. The authors present the work as preliminary due to resource constraints on model and benchmark coverage.

Significance. If the measurements are robust, the study supplies direct counter-evidence to an influential but unverified practitioner claim, showing that prompt-language choice for coding tasks does not produce simple token savings and can degrade resolution rates. It introduces a joint cost-success metric that is more relevant than token count alone and underscores the model-specific nature of tokenization effects, which is useful for the multilingual prompting literature in NLP and software engineering.

minor comments (3)
  1. The abstract and introduction cite a 'circulating claim' of up to 40% savings but provide no specific source, social-media post, or practitioner reference; adding one or two concrete citations would strengthen the motivation without altering the empirical core.
  2. The methods section should explicitly state the procedure used to create 'equivalent' Chinese and English prompts (e.g., human translation protocol, back-translation check, or use of a fixed translator) so that readers can assess whether prompt quality differences could contribute to the observed success-rate gap.
  3. Table or figure captions for token-ratio and success-rate results should include the exact number of tasks per model-language pair and any statistical test (or lack thereof) for the reported differences, given the small sample implied by the 'preliminary' framing.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary and significance assessment of our work, as well as the recommendation for minor revision. The report accurately captures our empirical findings on SWE-bench Lite regarding the lack of general Chinese token efficiency, model-dependent effects, and lower success rates for Chinese prompts. Since the report lists no specific major comments, we have no points to address point-by-point and see no need for changes to the manuscript at this stage.

Circularity Check

0 steps flagged

No circularity: purely empirical token and success-rate measurements

full rationale

The paper reports direct experimental measurements of token consumption and task success rates for Chinese versus English prompts on SWE-bench Lite across a small set of models. No equations, derivations, fitted parameters, ansatzes, or uniqueness theorems appear. Claims follow immediately from the observed counts and rates; authors explicitly flag the narrow scope as a limitation rather than deriving broader conclusions. No self-citation load-bearing steps or reductions by construction exist.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical benchmark study with no new free parameters, axioms, or invented entities; relies on existing models and SWE-bench Lite.

pith-pipeline@v0.9.0 · 5566 in / 915 out tokens · 46776 ms · 2026-05-10T19:16:32.583917+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

Reference graph

Works this paper leans on

18 extracted references · 9 canonical work pages · 3 internal anchors

  1. [1]

    Chinese is more efficient for llm coding - try it now!https://www

    AI Advocate. Chinese is more efficient for llm coding - try it now!https://www. youtube.com/shorts/tbfCOa3XRFc, 2024

    2024

  2. [2]

    Raising the bar on swe- bench verified with claude 3.5 sonnet

    Anthropic. Raising the bar on swe- bench verified with claude 3.5 sonnet. https://www.anthropic.com/ engineering/swe-bench-sonnet, 2025

    2025

  3. [3]

    Japanese is the most expensive language in terms of input to- kens.https://dylancastillo.co/ til/counting-tokens.html, 2025

    Dylan Castillo. Japanese is the most expensive language in terms of input to- kens.https://dylancastillo.co/ til/counting-tokens.html, 2025

    2025

  4. [4]

    Evaluating Large Language Models Trained on Code

    Mark Chen, Jerry Tworek, Heewoo Jun, Qim- ing Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Eval- uating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  5. [5]

    Efficient and effective text encoding for chinese llama and alpaca

    Yiming Cui, Ziqing Yang, and Xin Yao. Ef- ficient and effective text encoding for Chi- nese LLaMA and Alpaca.arXiv preprint arXiv:2304.08177, 2023

    work page arXiv 2023

  6. [6]

    Swe-bench: Can lan- guage models resolve real-world bugs?arXiv preprint arXiv:2211.15553, 2024

    Carlos E Jimenez, Xinyu Yang, Alexander Wet- tig, Shunyu Jiang, Kexin Yao, Jindi Pei, Orr Zheng, Pulli Chen, et al. Swe-bench: Can lan- guage models resolve real-world bugs?arXiv preprint arXiv:2211.15553, 2024

    work page arXiv 2024

  7. [7]

    Sentence- piece: A simple and language independent sub- word tokenizer and detokenizer for neural text processing.arXiv preprint arXiv:1808.06243, 2018

    Taku Kudo and John Richardson. Sentence- piece: A simple and language independent sub- word tokenizer and detokenizer for neural text processing.arXiv preprint arXiv:1808.06243, 2018

    work page arXiv 2018

  8. [8]

    Introducing swe-bench ver- ified.https://openai.com/ index/introducing-swe-bench- verified/, 2024

    OpenAI. Introducing swe-bench ver- ified.https://openai.com/ index/introducing-swe-bench- verified/, 2024

    2024

  9. [9]

    Humaneval-xl: A multilingual code gen- eration benchmark for cross-lingual natural language generalization.arXiv preprint arXiv:2402.16694, 2024

    Qiwei Peng, Yekun Chai, and Xuhong Li. Humaneval-xl: A multilingual code gen- eration benchmark for cross-lingual natu- ral language generalization.arXiv preprint arXiv:2402.16694, 2024

    work page arXiv 2024

  10. [10]

    Torr, and Adel Bibi

    Aleksandar Petrov, Emanuele La Malfa, Philip H.S. Torr, and Adel Bibi. Language model tokenizers introduce unfairness between languages. InAdvances in Neural Information Processing Systems, volume 36, 2023

    2023

  11. [11]

    How multilingual is multilingual BERT? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4996–5001, 2019

    Telmo Pires, Eva Schlinger, and Dan Garrette. How multilingual is multilingual BERT? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4996–5001, 2019

    2019

  12. [12]

    Neural Machine Translation of Rare Words with Subword Units

    Rico Sennrich, Barry Haddow, and Alexan- dra Birch. Neural machine translation of rare words with subword units.arXiv preprint arXiv:1508.07909, 2015

    work page internal anchor Pith review arXiv 2015

  13. [13]

    SNS Insider. AI code tools market to hit USD 37.34 billion by 2032.https: //www.globenewswire.com/news- release/2025/09/26/3157060/ 0/en/AI-Code-Tools-Market- to-Hit-USD-37-34-Billion-by- 2032-Driven-by-Rising-Demand- for-Automation-Globally-SNS- Insider.html, 2025. 11

    2032

  14. [14]

    AI — 2025 stack Over- flow developer survey.https://survey

    Stack Overflow. AI — 2025 stack Over- flow developer survey.https://survey. stackoverflow.co/2025/ai, 2025

    2025

  15. [15]

    Who is using AI to code? Global diffusion and impact of gen- erative AI.Science, 2026

    Georg Tamm et al. Who is using AI to code? Global diffusion and impact of gen- erative AI.Science, 2026. arXiv preprint arXiv:2506.08945

    work page arXiv 2026

  16. [16]

    Eight months in, Swedish unicorn Lovable crosses the $100M ARR milestone

    TechCrunch. Eight months in, Swedish unicorn Lovable crosses the $100M ARR milestone. https://techcrunch.com/2025/ 07/23/eight-months-in-swedish- unicorn-lovable-crosses-the- 100m-arr-milestone/, 2025

    2025

  17. [17]

    Qwen2 Technical Report

    An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Li, Dayiheng Liu, Fei Huang, Guanting Wei, Huan Lin, et al. Qwen2 technical report.arXiv preprint arXiv:2407.10671, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  18. [18]

    arXiv preprint arXiv:2306.05179 , year=

    Wenxuan Zhang, Sharifah Mahani Aljunied, Chang Gao, Yew Ken Chia, and Lidong Bing. M3exam: A multilingual, multimodal, multi- level benchmark for examining large language models.arXiv preprint arXiv:2306.05179, 2023. 12

    work page arXiv 2023