Context Is What You Need: The Maximum Effective Context Window for Real World Limits of LLMs

Type: academic-paper

Author: Norman Paulsen (Denver, Colorado, USA) Source: https://arxiv.org/pdf/2509.21361 Date: 2025

Abstract

Large language model (LLM) providers boast big numbers for maximum context window sizes. To test the real world use of context windows, we 1) define a concept of maximum effective context window, 2) formulate a testing method of a context window's effectiveness over various sizes and problem types, and 3) create a standardized way to compare model efficacy for increasingly larger context window sizes to find the point of failure. We collected hundreds of thousands of data points across several models and found significant differences between reported Maximum Context Window (MCW) size and Maximum Effective Context Window (MECW) size. Our findings show that the MECW is, not only, drastically different from the MCW but also shifts based on the problem type. A few top of the line models in our test group failed with as little as 100 tokens in context; most had severe degradation in accuracy by 1000 tokens in context. All models fell far short of their Maximum Context Window by as much as >99%. Our data reveals the Maximum Effective Context Window shifts based on the type of problem provided, offering clear and actionable insights into how to improve model accuracy and decrease model hallucination rates.

Keywords: Large Language Models, Context Window, Inference Tokens, Hallucination Rates, LLM Accuracy

1 Introduction

The rise of LLMs such as ChatGPT, Claude, Gemini, and LLaMA has reshaped NLP, enabling increasingly sophisticated contextual understanding, summarization, coding, and dialog capabilities. Central to these advancements is the context window — the max number of input tokens a model can consider at once.

Model specifications cite maximum context windows of 128k, 1 million or even as much as 10 million tokens (Meta 2025a), but these numbers reflect architectural or implementation limits, not necessarily the model's practical capacity for handling or retaining that full input context. Empirical evidence increasingly suggests a divergence between the maximum context window (MCW) and the maximum effective context window (MECW) — the point beyond which additional tokens no longer meaningfully contribute to model output quality.

This paper proposes that while LLM architecture permits long-sequence processing, practical limitations constrain the usable span of context in real-world inference tasks. MECW is defined as the longest span of token input, for a given problem type, for which incremental tokens degrade the model's output with measurable effect. This notion reframes the context window not as a flat max input capacity, but as a spectrum of values dependent on the task at hand.

Key Contributions

• A Formal Definition of MECW: Grounded in informational, theoretical and behavioral criteria. Defines effectiveness in terms of fluctuating measurable influence on model predictions, rather than static inclusion limits like that of MCW.
• Empirical Analysis Across Tasks and Models: Evaluation across several state-of-the-art LLMs on a battery of tasks (Needle-in-a-Haystack, Needles-in-a-Haystack, summarization, Needles-in-a-Haystack with sorting) using controlled token context intervals. Includes both open-source models (Mistral, LLaMA, Deepseek) and proprietary APIs (GPT-4o-mini, GPT-5, Claude 3, Gemini 2.5, Gemini 2.0).
• Recommendations for Design and Deployment: Practical guidelines for model architects, prompt engineers, and application developers. Strategies to optimize RAG pipelines, truncate or summarize distant context, and more realistically estimate context window limits based on MECW rather than MCW.

Broader Implications

Understanding the gap between MCW and MECW is not just a technical nuance — it is fundamental to how we effectively use and leverage AI in real world applications. Misinterpretation of context capacity can lead to inefficient system designs, overinvestment in retrieval techniques that yield diminishing returns, or misaligned user expectations. It can also skew benchmarking results, especially when models are assumed to have uniform memory over arbitrarily long sequences.

In cognitive science terms, MECW may be more analogous to "working memory" than to "long-term memory" — recognizing that distinction can lead to more robust, interpretable, and grounded model responses.

2 Related Work

2.1 Tokens Matter

Prior research shows models suffer from a placement of data issue. Successful retrieval drops from 76% to 66% by moving the key information from position 1 to position 2, and falls under general model performance when key information is moved to position 7. Patterns in attention have shown to improve data retrieval (Liu et al., 2023; Hsieh et al., 2024).

The size of relevant information matters — the relevant information token count compared to total context token count impacts successful retrieval rate by up to 25% (Bianchi et al., 2025).

Notable research shows models handle context window lengths greater than their training-time sequence length poorly. When using context lengths greater than training-time sequence length, a U-shaped performance curve emerges based on critical information placement. Additional research shows models start to degrade at half their training length (An et al., 2024; Liu et al., 2023; Press et al., 2022).

Relatively new research poses that model performance on novel tasks, like math and logic problems, suffers from the number of steps needed to complete. The paper looks to show that it's not the number of steps but the token length that causes a breakdown in performance (Xu et al., 2025).

2.2 Settings Impacting Performance

Model performance relies on several factors including max allowed output tokens, temperature, top_p, and even the Python frameworks used (Hochlehnert et al., 2025; Zhao et al., 2025).

Higher temperatures, approaching 1, lead to increased model performance with a tradeoff in reproducibility. Higher top_p values also lead to improved model accuracy but without the detriment to stability. All settings are left constant during tests to remove as many outside variables as possible.

Max token values have an outsized impact on long context query performance. As models approach set max token limits, they begin to truncate responses and provide unfinished answers. All token limits are set to maximum values.

Reasoning and non-reasoning models work in distinctly different ways, leading to large performance gains from reasoning models. The research focuses on top performing models from various providers, which were mostly reasoning models.

Fine tuning models on specific tasks, like data extraction from large documents, increases performance for said tasks. Studies found a 10.5% improvement in data retrieval questions on long context windows by fine tuning models on synthetic large context window tasks (Xiong et al., 2024).

2.3 Novel Question Performance

Standard model performance frameworks are not built to evaluate long context windows. Existing frameworks like AIME24, AIME25 and GPQA Diamond all suffer from random seeding volatility, wide fluctuations in scores due to small number of questions, and variability across different versions.

The seed parameter, if not explicitly set, is automatically generated dynamically per inference. This was shown to vary model performance on the same dataset significantly higher than the baseline. Coupled with small datasets, this can result in large fluctuations in standardized model performance frameworks.

2.4 Other Frameworks

Other frameworks for testing long context windows have been developed in the last 12 months. Several have focused on the Needle-in-a-Haystack problem (Gao et al., 2025; Ling et al., 2025; Nelson et al., 2024). Others focus on complex tasks on a fixed dataset (Bogomolov et al., 2024; Cui et al., 2025; Jacovi et al., 2025; Zhuang et al., 2025). None focus on incrementally testing model effectiveness on various tasks as token count increases.

Key frameworks reviewed: - ETHIC: Tests long context tasks to see how well LLMs cover provided material (Lee et al., 2024). - DocPuzzle: 100 multi-domain cases with verification mechanisms (Zhuang et al., 2025). - CURIE: Scientific long-Context Understanding, Reasoning, and Information Extraction benchmark (Cui et al., 2025). - FACTS Grounding Leaderboard: Ongoing benchmark testing with documents up to 32k tokens (Jacovi et al., 2025). - Long Code Arena: 6 aspects of code processing (Bogomolov et al., 2024). - LaRA: Benchmarks RAG vs long-context windows, finds inconclusive results (Li et al., 2025a). - U-NIAH: Unified Needle-in-a-Haystack comparing LLM long contexts to RAG (Gao et al., 2025). - HELMET: Tests models with various tasks and context sizes (Yen et al., 2024). - BABILong: Tests model retrieval capacity exceeding 11 million tokens (Kuratov et al., 2024). - LongReason: Adds artificial context to test models at various context sizes (Ling et al., 2025). - NoLiMa: Tests increasing context lengths using Needle-in-a-Haystack style questions requiring inference (Modarressi et al., 2025). - FLenQA: Shows model performance degradation over increasingly large context windows (Levy et al., 2024).

None of the existing frameworks provide data sufficient for testing incrementally increasing context lengths for real world use cases.

3 Methodology

3.1 Model Selection

11 models selected spanning open and closed weight:

• Open weight: Deepseek.r1-v1:0 (DeepSeek-AI et al., 2025), Meta.llama3-3-70b-instruct-v1:0 (Meta 2025b)
• Closed weight: claude-3-5-sonnet-20241022 (Anthropic 2024), gemini-2.0-flash (Gemini 2025a), gemini-2.5-flash-preview-05-20 (Gemini 2025b), GPT-4.1 (OpenAI 2025a), GPT o4-mini (OpenAI 2025b), GPT-5 (OpenAI 2025c), Grok-3-latest (xAI 2025), mistral-medium-2505 (Mistral AI 2025), Qwen-plus (Quen Team 2025)

3.1 Framework Design

Dataset: 10,000 unique names of individuals, each assigned a random number 1-20, a random item from a list of 15 possibilities, and a random color out of 9 possibilities. Example data row: "Abigail Holmes has 19 red balloons."

Question types (four distinct types): 1. Needle-in-a-Haystack: Search for a single data point — "How many objects does {person} have?" 2. Needles-in-a-Haystack: Search for multiple data points and sum — "How many {color} objects are there?" or "How many {object} are there?" 3. Summarization: Full sum of all data points — "How many objects are there total?" 4. Find and Sort: Search for multiple data points then sort alphabetically by name — "Find all people with {color} objects. Sort them by first and last name. Concatenate the number of objects they have into one long string value in the order they were sorted."

For each question and data size, the dataset order was randomized to negate data positioning effects and guarantee an even distribution of data placement throughout the context.

3.2 Study Setup

Connected via APIs to every model using Python, storing initializing dataset and model responses in a Postgres database. For each value in a pre-selected range of data points, the framework would concatenate that many data points from the dataset, formulate a question, randomize the order of the dataset, then feed the sample dataset and question into each model. Results were captured and compared to the correct answer.

3.3 Analysis Procedure

Collected over 66k rows of data, capturing model name, question type, input token count, and whether the correct answer was achieved. For each question and model combination, they validated sufficient data by measuring the p-value of the relationship between input token count and correct answer (1 for true, 0 for false). P-values found at this step were always extremely low (<1.0e172).

To better tie token input count to correct answer rate, input token counts were bucketed into ranges and correct answers averaged over the range for each model: - Needle in a Haystack: buckets of 5000 tokens - All other question types: buckets of 100 tokens

Datapoints falling into a bucket with only 1 or 2 datapoints were removed (usually at high end where most tests fell in preceding buckets).

4 Findings for Q1: Does MECW Differ from MCW

Using buckets, clear data patterns emerged. Low levels of token counts improved upon published model hallucination rates with high confidence levels (Hughes 2023). As token count increased, all models' accuracy diverged from their published hallucination rates, providing increasingly erratic results. Model performance, in most cases, could be consistently forced to near 0% accuracy levels if provided too large a context. These findings indicate that there is a need for a MECW measure across models.

5 Findings for Q2: Do Different Types of Questions Change the MECW

Models perform vastly differently to the type of question asked. However, researchers expected model rankings across tasks to remain relatively stable. This was not the case — some models handled the Needle in the Haystack question far better than their peers but well underperformed their peers on other question types. This provides an avenue for further research on model performance across task types: coding, scientific research, general Q/A, mathematics, etc.

6 Additional Findings

6.1 Model Accuracy Using RAG

All models outperformed their standard hallucination rates for questions at a certain context size. As context size increased, hallucination rates exceeded base hallucination rates for all models. For the worst performing models, hallucination rates reached near 100%.

Since the line of questioning provided both the facts and the question, it was a simple form of RAG, suggesting that RAG increases model accuracy (Li et al., 2025a). The research expands on this to show accuracy using RAG can reach near 100% levels, if utilized under the MECW. It also shows RAG can worsen model performance when exceeding the MECW.

6.2 Model Selection

Existing production agentic frameworks tend to utilize the best model or multiple models to guarantee accuracy. Understanding the specific use case and MECW for that use case allows a better weighing of cost and speed when making model selection. While OpenAI o4-mini performed at the top in the needles problem, if only utilizing 500 input tokens or less, DeepSeek r1 could be used at a fraction of the price with no reduction in accuracy.

The MECW is designed as an effective way to increase LLM accuracy by measuring, understanding and working within the limits of a given model and problem. Each agent is designed with a specific task in mind and the MECW can improve each agent's performance to near flawless levels.

Model rankings also changed across tasks. OpenAI's o4-mini was a top performer in the Needles in a Haystack problem but one of the worst at the Needle in a Haystack problem. This reinforces the need for an MECW measurement to help select the correct model for the correct task.

6 Discussion

6.1 Implications for GenAI Use

More important than temperature, top_p, seed parameters and other settings, context window size is the most important factor for determining model accuracy. Context window size can vary a model's performance from near 100% accuracy to near 0% accuracy.

Model Context Windows have grown to outsized amounts — as high as 1 million and 10 million tokens. These published limits lead to a false promise of model performance up to that amount. Real world use cases for LLMs should focus on limiting token count in tasks for best results.

6.2 Need for New Testing Frameworks

Existing testing frameworks like AIME24, AIME25 and GPQA provide limited value on model performance in real world use cases. They provide wide swings in measured accuracy because of small sample rates.

Most applications of Generative AI do not use an LLM alone and leverage some kind of context extension, like RAG. Better testing frameworks are needed for showcasing model performance with more complex use cases.

6.3 Impact on RAG Systems

Data supports the notion that RAG systems improve hallucination rates. As an example, GPT-5 did not hallucinate once in the dataset when asked a question with under 500 tokens. The problem becomes that as input token amounts increase, the hallucination rate increases. As input token counts reach as little as 2000 tokens, some models' hallucination rates go as high as 99%.

Because of the drastic decline in model performance when using larger context windows, RAG systems leveraging higher token counts decline model performance instead of improving it.

Overall, this leads to cascading failure rates when LLMs are chained together in agentic frameworks (Meimandi et al., 2025; Xu et al., 2025). The idea of a near limitless context window leads developers to believe that an agentic system chaining multiple agents with large context windows will perform reasonably well under most situations. As shown through the research, large context windows degrade model performance so agentic systems relying on large context windows for purposes of RAG will see cascading failures.

Model accuracy can improve above standard hallucination rates simply by providing context windows at the correct size for the model and problem type. This prevents cascading model failure by decreasing hallucination rates to a point where chaining agents together will not fail at massively increased rates.

Anyone leveraging large context windows and/or RAG systems should be aware of the kinds of questions being posed to models and the limits of context windows around those questions to prevent or reduce hallucination rates and improve overall model accuracy.

6.4 Limitations

• Multivariate testing: The study focuses on one variable — token count. Further testing could be done on token counts tied to other variables, like top-p, to see if another variable allows for larger MECWs.
• Real world problems: Questions and dataset are very simple. Real world problems might have more structured data input or attached documents like PDF or Excel. Testing the effects of data format could lead to more understanding in how to effectively use a model's context window.

7 Conclusion

The Maximum Context Window does vary widely from the Maximum Effective Context Window (MECW) for all models tested. Additionally, MECW changes with the type of problem presented to the model. Results suggest effectively using a model's context window is the highest contributing factor to the hallucination rate of the model.

Appendix

A.1 Survey Questions

Needle-in-a-Haystack: "How many objects does {person} have?"

Needles-in-a-Haystack: "How many {color} objects are there?" or "How many {object} are there?"

Summary: "How many objects are there total?"

Sorted Needles-in-a-Haystack: "Find all people with {color} objects. Sort them by first and last name. Concatenate the number of objects they have into one long string value in the order they were sorted." or "Find all people with {object}. Sort them by first and last name. Concatenate the number of objects they have into one long string value in the order they were sorted."

A.2 Definitions

1. Maximum Effective Context Window: The maximum token count, for a given problem type, before the model performance begins to degrade in a measurable fashion.

2. Cascading Failures: Where an agentic framework consisting of multiple agents fails most of the time because each agent has a mediocre success rate. A 3-agent system with 70% success per agent results in a system with a 34.3% success rate.