# Researchers model how LLM input context affects compute time and energy [Canonical reading](https://escapemeridian.com/readings/researchers-model-how-llm-input-context-affects-compute-time-90c2787a) In an arXiv paper, Lars Krupp and coauthors examine how input and output token lengths relate to compute time and energy use in large language models (LLMs). They argue that estimates focused on generated output miss an important part of agentic workflows: the large input contexts those systems use. The authors report that decode compute time and energy scale linearly with output length, while prefill compute time and energy show both linear and quadratic scaling with input length. In other words, their measurements suggest that longer inputs can add costs in ways that output-only estimates do not capture. They also report that compute time is a reliable proxy for energy consumption when direct energy measurements are unavailable. The paper presents its mapping framework as a way to estimate energy use and support more sustainable deployment decisions for agentic AI. The abstract does not specify the tested models, hardware, or measurement conditions, so it is unclear how broadly the reported relationships apply. How well would the framework’s estimates carry over to other models and deployment settings? Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Oct 29, 2026 (2026-10-29). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T19:17:01.952Z. First observed: 2026-10-30T06:45:29.570Z. Retrieved: 2026-10-31T08:31:28.600Z. - [The Real Cost of Context: Predicting Compute Time and Energy Consumption in LLM Prefill and Decode Phases](https://arxiv.org/abs/2610.39621) ## Limits The abstract does not specify the tested models, hardware, or measurement conditions, so the scope and transferability of the reported relationships cannot be assessed from this evidence alone. ## Open question How well would the framework’s estimates carry over to other models and deployment settings? ## Source-stated The authors say existing approaches primarily estimate energy from generated output tokens, overlooking large input contexts common in agentic workflows. > existing approaches focus primarily on generated output tokens while overlooking the impact of the large input contexts characteristic of agentic workflows - [Supporting source](https://arxiv.org/abs/2610.39621) ## Source-stated The authors report that decode compute time and energy scale linearly with output length, while prefill compute time and energy show both linear and quadratic scaling with input length. > decode compute time and energy scale linearly with output length, whereas prefill compute time and energy exhibit both linear and quadratic scaling with input length - [Supporting source](https://arxiv.org/abs/2610.39621) ## Source-stated The authors say compute time can serve as a proxy for energy consumption when direct energy measurements are unavailable. > compute time serves as a reliable proxy for energy consumption when direct energy measurements are unavailable - [Supporting source](https://arxiv.org/abs/2610.39621) ## Source-stated The authors say their mapping framework supports energy estimation and more sustainable deployment decisions for agentic AI. > Our mapping framework enables precise energy estimation and supports more sustainable deployment decisions for agentic AI. - [Supporting source](https://arxiv.org/abs/2610.39621) ## Escape Meridian interpretation The abstract does not identify the tested models, hardware, or measurement conditions, limiting assessment of how broadly its findings apply. > we systematically profile compute time and energy consumption across a wide range of prefill (input) and decode (output) token lengths. - [Supporting source](https://arxiv.org/abs/2610.39621) ## Source and contributor context Krupp, Lars, Geißler, Daniel, Calatrava-Nicolas, Francisco M., Fritsch, Stefan Gerd, Lukowicz, Paul, Karolus, Jakob · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## Agentic AI AI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer. - [Definition reference](https://developers.google.com/machine-learning/glossary#agentic) ## TIME TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services. - [Explore this context](/entities/time) - [Reference](https://time.com/) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 47113fdb-16ae-4714-89f6-41618e30a8a9. Prepared by server-excerpt-repair/stream-excerpt-repair-v1 (original draft: gpt-6-luna); grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T19:17:01.952Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 730c91619b5fe4a95a85a8c2375192d40a23e6f48b9ec851f16d5292811ae71c. Source text hash: 73a51df7d9b84f4a08df174c98e44d4e09d6a64b91a7c78290fc1f9a993b2324. ## Revision and source-check history ### Version 1 — 2026-11-10T19:17:01.952Z In an arXiv paper, Lars Krupp and coauthors examine how input and output token lengths relate to compute time and energy use in large language models (LLMs). They argue that estimates focused on generated output miss an important part of agentic workflows: the large input contexts those systems use. The authors report that decode compute time and energy scale linearly with output length, while prefill compute time and energy show both linear and quadratic scaling with input length. In other words, their measurements suggest that longer inputs can add costs in ways that output-only estimates do not capture. They also report that compute time is a reliable proxy for energy consumption when direct energy measurements are unavailable. The paper presents its mapping framework as a way to estimate energy use and support more sustainable deployment decisions for agentic AI. The abstract does not specify the tested models, hardware, or measurement conditions, so it is unclear how broadly the reported relationships apply. How well would the framework’s estimates carry over to other models and deployment settings? ## Original source arXiv cs.AI · Article Original source date: Oct 29, 2026 (2026-10-29). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T19:17:01.952Z. First observed: 2026-10-30T06:45:29.570Z. Retrieved: 2026-10-31T08:31:28.600Z. - [The Real Cost of Context: Predicting Compute Time and Energy Consumption in LLM Prefill and Decode Phases](https://arxiv.org/abs/2610.39621) ## Limits The abstract does not specify the tested models, hardware, or measurement conditions, so the scope and transferability of the reported relationships cannot be assessed from this evidence alone. ## Open question How well would the framework’s estimates carry over to other models and deployment settings? ## Source-stated The authors say existing approaches primarily estimate energy from generated output tokens, overlooking large input contexts common in agentic workflows. > existing approaches focus primarily on generated output tokens while overlooking the impact of the large input contexts characteristic of agentic workflows - [Supporting source](https://arxiv.org/abs/2610.39621) ## Source-stated The authors report that decode compute time and energy scale linearly with output length, while prefill compute time and energy show both linear and quadratic scaling with input length. > decode compute time and energy scale linearly with output length, whereas prefill compute time and energy exhibit both linear and quadratic scaling with input length - [Supporting source](https://arxiv.org/abs/2610.39621) ## Source-stated The authors say compute time can serve as a proxy for energy consumption when direct energy measurements are unavailable. > compute time serves as a reliable proxy for energy consumption when direct energy measurements are unavailable - [Supporting source](https://arxiv.org/abs/2610.39621) ## Source-stated The authors say their mapping framework supports energy estimation and more sustainable deployment decisions for agentic AI. > Our mapping framework enables precise energy estimation and supports more sustainable deployment decisions for agentic AI. - [Supporting source](https://arxiv.org/abs/2610.39621) ## Escape Meridian interpretation The abstract does not identify the tested models, hardware, or measurement conditions, limiting assessment of how broadly its findings apply. > we systematically profile compute time and energy consumption across a wide range of prefill (input) and decode (output) token lengths. - [Supporting source](https://arxiv.org/abs/2610.39621) ## Source and contributor context Krupp, Lars, Geißler, Daniel, Calatrava-Nicolas, Francisco M., Fritsch, Stefan Gerd, Lukowicz, Paul, Karolus, Jakob · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## Agentic AI AI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer. - [Definition reference](https://developers.google.com/machine-learning/glossary#agentic) ## TIME TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services. - [Explore this context](/entities/time) - [Reference](https://time.com/) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 47113fdb-16ae-4714-89f6-41618e30a8a9. Prepared by server-excerpt-repair/stream-excerpt-repair-v1 (original draft: gpt-6-luna); grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T19:17:01.952Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 730c91619b5fe4a95a85a8c2375192d40a23e6f48b9ec851f16d5292811ae71c. Source text hash: 73a51df7d9b84f4a08df174c98e44d4e09d6a64b91a7c78290fc1f9a993b2324. ### uncertain — 2026-11-10T19:17:01.952Z — anthropic/claude-opus-5.5 Publication: 47113fdb-16ae-4714-89f6-41618e30a8a9 attribution — pass: The reading correctly attributes the arXiv paper to Lars Krupp and coauthors. It keeps the findings framed as the authors' reports and arguments rather than established fact. > Authors:Lars Krupp, Daniel Geißler, Francisco M. Calatrava-Nicolas, Stefan Gerd Fritsch, Paul Lukowicz, Jakob Karolus summary — uncertain: The reading is faithful but has two gaps. First, it drops the paper's stated motivation: energy use of proprietary API-based models is hard to estimate. That is why the compute-time-as-proxy finding matters. Second, the gloss of quadratic scaling (longer inputs add costs in ways output-only estimates miss) does not say that prefill cost can grow faster than input length itself. A non-specialist would also need tooltips for prefill, decode and agentic workflows to follow the central result. Smallest repairs: add one clause on proprietary API models, and add a plain-language note that quadratic means costs can rise disproportionately as inputs grow. > estimating the energy consumption of proprietary API-based models remains challenging date — pass: The displayed date, 2026-10-29, matches the v1 submission date on the arXiv page. It is not the later feed date of 2026-10-31. > \[Submitted on 29 Oct 2026\] support — pass: Each claim matches the abstract's wording. Softening 'precise energy estimation' to 'estimate energy use' does not overstate the source. The limitation about unspecified models, hardware and measurement conditions is an accurate description of what the abstract contains. The closing question follows from that gap and does not assert anything new. > These findings demonstrate that input context becomes a relevant factor for large-context workloads. media — pass: The selected media is the verified direct link to the original arXiv abstract page. That is the most direct way for readers to access the source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. 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