OpenAI script rates question complexity to reduce LLM inference costs
Why does it matter whether a question is simple or tangled? For anyone paying for every millisecond of model compute, the answer can translate into dollars saved. OpenAI’s recent guidance on trimming inference expenses hinges on a single, pragmatic step: ask the model to self‑assess how hard a prompt will be for it to answer.
While the idea sounds modest, the impact is measurable—by flagging high‑complexity queries, developers can route them to larger, more expensive models only when necessary, or batch them for later processing. Here’s the thing: the script below does exactly that. It pulls in OpenAI’s Python client, defines a helper that sends a short instruction to the model, and expects a lone digit in return.
The function is deliberately lean; it asks the LLM to “Rate the complexity of the question from 1 to 10 … Provide only the number.” The result lets you decide whether to fire up a heavyweight model like gpt‑5.1 or keep costs low.
```python 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", "cont ```
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.
While the piece offers ten concrete tactics for trimming OpenAI inference spend, the real impact of each remains unclear. The author stresses that rating a question’s difficulty on a one‑to‑ten scale can guide model selection, hoping to avoid over‑engineered responses for simple prompts. Short code snippets illustrate the approach, yet no benchmark data are provided to verify cost savings.
Moreover, the guide assumes the OpenAI API as the baseline, acknowledging that the methods could be ported to other providers without detailing any required adjustments. Cost versus quality trade‑offs are repeatedly mentioned, but the article stops short of quantifying how much quality might suffer under tighter budgets. The suggestion to embed complexity checks into pipelines feels pragmatic, though it hinges on the model’s ability to produce a single numeric rating reliably.
Ultimately, the recommendations are practical in theory, but without empirical evidence the extent to which they will reduce expenses while preserving system performance stays uncertain.
Further Reading
- OpenAI unveils new tools to cut AI costs by routing and compressing prompts - TechCrunch
- Smarter prompt routing: How OpenAI’s new APIs decide when you really need a big model - The Verge
- Question complexity classifiers for cost‑efficient LLM inference - arXiv
- Reducing LLM compute with difficulty‑aware routing and early exiting - arXiv
- How AI companies are triaging ‘easy’ vs ‘hard’ queries to slash inference costs - InfoWorld
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Common Questions Answered
What is the purpose of rating question complexity in OpenAI's new guidance?
The rating helps developers identify high‑complexity queries that may require larger, more expensive models. By flagging these prompts, they can route them selectively, which reduces overall LLM inference costs while preserving answer quality for simpler questions.
How does the Python script shown in the article determine a question's complexity score?
The script constructs a prompt that asks the model (gpt-5.1) to rate the question on a 1‑to‑10 scale and sends it via the OpenAI chat completions API. It then extracts the numeric response from the model's reply and returns it as an integer.
What complexity rating did the example script assign to "Explain convolutional neural networks," and what does that rating indicate?
The script returned a rating of 4 for that question, suggesting it is relatively low‑complexity. A score of 4 implies the query could be answered adequately by a smaller, cheaper model without needing the most powerful LLM.
Does the article provide benchmark data to confirm cost savings from using the complexity‑rating approach?
No, the article explicitly states that while ten concrete tactics are listed, no benchmark data are included to verify actual inference‑spend reductions. The author acknowledges the lack of empirical evidence for the claimed savings.