Editorial illustration for OpenAI Develops Script to Gauge Question Complexity for AI Efficiency
OpenAI's Smart Script Measures Question Complexity for AI
OpenAI script rates question complexity to reduce LLM inference costs
In the high-stakes world of artificial intelligence, computational efficiency isn't just a luxury, it's a necessity. OpenAI has quietly developed a clever script designed to assess the complexity of questions before they consume valuable computing resources.
The challenge facing large language models (LLMs) is managing computational costs while maintaining response quality. Some queries demand intensive processing, while others require minimal effort. OpenAI's new approach tackles this problem head-on by creating a mechanism to pre-evaluate question complexity.
Their new script aims to rate questions on a numerical scale, allowing AI systems to strategically allocate computational power. By understanding the potential difficulty of a query upfront, developers can potentially reduce inference costs, a critical consideration as AI systems become increasingly sophisticated and expensive to operate.
The script represents a pragmatic solution to a growing technical challenge: how to make AI more economically sustainable without sacrificing performance. Developers and researchers are watching closely, sensing this could be a significant step toward more intelligent resource management in machine learning.
Example: from openai import OpenAI client = OpenAI() def get_complexity(question): prompt = f"Rate the complexity of the question from 1 to 10 for an LLM to answer. Provide only the number.\nQuestion: {question}" res = client.chat.completions.create( model="gpt-5.1", messages=[{"role": "user", "content": prompt}], ) return int(res.choices[0].message.content.strip()) print(get_complexity("Explain convolutional neural networks")) Output: 4 So our classifier says the complexity is 4, don't worry about the extra LLM call as this is generating only a single number. This complexity number can be used to route the tasks, like: complexity < 7 then route to a smaller model, else a larger model.
OpenAI's latest script offers an intriguing approach to managing AI inference costs by dynamically assessing question complexity. The tool allows developers to quickly determine how resource-intensive a query might be before processing, potentially simplifying computational efficiency.
By creating a simple function that rates questions on a 1-10 scale, OpenAI provides a pragmatic method for predicting computational load. The script uses a self-referential prompt to have the AI evaluate its own potential challenge level, returning a numerical complexity rating.
In the demonstrated example, a technical query about convolutional neural networks received a complexity score of 4, suggesting a moderate level of difficulty for the language model. This indicates the script can provide rapid, nuanced insights into query complexity.
While the buildation seems straightforward, it represents a clever optimization strategy. Developers could potentially use such a tool to route more complex queries to more powerful models or allocate computational resources more strategically.
The script hints at OpenAI's ongoing efforts to make AI interactions more efficient and cost-effective. Still, its broader implications for large language model management remain to be seen.
Further Reading
- Welcome to LLMflation - LLM inference cost is going down fast ⬇️ - a16z
- DeepSeek Cuts Inference Costs, OpenAI Tightens Ties with AMD - DeepLearning.AI
- LLM inference prices have fallen rapidly but unequally across tasks - Epoch AI
- AI API Pricing Trends 2026: What Every Enterprise Needs to Know - Swfte
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Common Questions Answered
How does OpenAI's script determine the complexity of a question for large language models?
The script uses a self-referential prompt that asks the AI to rate the complexity of a given question on a scale from 1 to 10. By leveraging the language model itself, the script can quickly assess the potential computational resources required to answer a specific query.
What is the primary goal of OpenAI's complexity assessment script?
The main objective is to manage computational efficiency by predicting the resource intensity of different questions before processing them. This approach helps developers optimize AI inference costs by identifying which queries might require more intensive computational resources.
What example does the script provide for assessing question complexity?
In the example, the script rates the complexity of the question 'Explain convolutional neural networks' as a 4 out of 10. This demonstrates how the tool can quickly estimate the computational load required to generate a response for a specific type of query.