[2601.14470] Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering

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Computer Science > Software Engineering

arXiv:2601.14470 (cs)

[Submitted on 20 Jan 2026]

Title:Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering

Authors:Mohamad Salim, Jasmine Latendresse, SayedHassan Khatoonabadi, Emad Shihab<br>View a PDF of the paper titled Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering, by Mohamad Salim and 3 other authors

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Abstract:LLM-based Multi-Agent (LLM-MA) systems are increasingly applied to automate complex software engineering tasks such as requirements engineering, code generation, and testing. However, their operational efficiency and resource consumption remain poorly understood, hindering practical adoption due to unpredictable costs and environmental impact. To address this, we conduct an analysis of token consumption patterns in an LLM-MA system within the Software Development Life Cycle (SDLC), aiming to understand where tokens are consumed across distinct software engineering activities. We analyze execution traces from 30 software development tasks performed by the ChatDev framework using a GPT-5 reasoning model, mapping its internal phases to distinct development stages (Design, Coding, Code Completion, Code Review, Testing, and Documentation) to create a standardized evaluation framework. We then quantify and compare token distribution (input, output, reasoning) across these stages.

Our preliminary findings show that the iterative Code Review stage accounts for the majority of token consumption for an average of 59.4% of tokens. Furthermore, we observe that input tokens consistently constitute the largest share of consumption for an average of 53.9%, providing empirical evidence for potentially significant inefficiencies in agentic collaboration. Our results suggest that the primary cost of agentic software engineering lies not in initial code generation but in automated refinement and verification. Our novel methodology can help practitioners predict expenses and optimize workflows, and it directs future research toward developing more token-efficient agent collaboration protocols.

Subjects:

Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

Cite as:<br>arXiv:2601.14470 [cs.SE]

(or<br>arXiv:2601.14470v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2601.14470

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arXiv-issued DOI via DataCite

Submission history<br>From: Mohamad Salim [view email]<br>[v1]<br>Tue, 20 Jan 2026 20:52:14 UTC (302 KB)

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