Is Your GPT-5 a Little… Underwhelming? How to Spot if You're Secretly Using GPT-3.5

The Big Red Flag: The Knowledge Cutoff Test

• GPT-3.5’s Knowledge Cutoff: September 2021.
• GPT-5’s Knowledge Cutoff: September 2024.

• “Give me a summary of the main plot points of the movie 'Dune: Part Two'.” (Released in early 2024)
• “Who won the 2023 Super Bowl?”
• “Explain the significance of the first images released from the James Webb Space Telescope.” (The first images came out in July 2022)

• A GPT-3.5 response will be evasive. It will tell you it doesn't have information past its cutoff date. You’ll get a classic line like: "As of my last update in September 2021, I don't have information on events that occurred after that date..." It CANNOT answer the question.
• A GPT-5 response will answer the question directly & accurately. It will know about these events because its training data is much more recent.

Pushing the Limits: The Reasoning & Complexity Challenge

1. Logic Puzzles: Use a classic brain teaser. For example: “There are three boxes. One contains only apples, one contains only oranges, & one contains a mix of apples & oranges. All three boxes are labeled incorrectly. You can only pull out one fruit from one box (without looking inside) to figure out which box is which. How do you do it?”
2. Complex Coding Tasks: Don't just ask for a simple Python script. Ask for something that requires understanding context & dependencies. “Write a Python script that uses the 'requests' library to fetch data from a weather API for three different cities, then uses 'pandas' to put the temperature & humidity into a DataFrame, & finally uses 'matplotlib' to plot the temperatures on a bar chart. Include error handling for API failures.”
3. Scientific Reasoning: Present it with a problem from a high-level benchmark. These are designed specifically to test the limits of AI reasoning. A simplified version could be: “Explain the potential cascading effects on a local ecosystem if a non-native species of insect that preys on bees is introduced. Consider the impact on pollination, plant life, & other animal populations over three years.”

• A GPT-3.5 response will likely get confused.
  • On the logic puzzle, it might give a common but incorrect answer, or it might just fail to grasp the core logic of the problem.
  • On the coding task, it might produce code that has bugs, misses a step, or uses outdated methods. It will probably struggle with combining the different libraries correctly in one go.
  • On the ecosystem question, it will give a very generic, surface-level answer about "negative impacts" without detailing the cascading, multi-step effects.
• A GPT-5 response will EXCEL at this.
  • It will solve the logic puzzle correctly & can explain its reasoning step-by-step.
  • The code it generates will be more robust, more efficient, & likely to run correctly on the first try. GPT-5 shows massive gains on coding benchmarks like SWE-bench & Aider Polyglot.
  • It will provide a deeply reasoned answer to the ecosystem question, discussing second & third-order effects, potential feedback loops, & a more nuanced understanding of ecological principles. This is because GPT-5 posts state-of-the-art results on PhD-level science questions in benchmarks like GPQA.

The Multimodal Test: Can It See?

• Upload a picture of your lunch & ask, “What is this, & can you give me a rough estimate of the calories?”
• Take a screenshot of a chart or graph & ask, “Can you summarize the key trend shown in this data visualization?”
• Upload a picture of a landmark & ask, “Where was this photo taken? What is the historical significance of this place?”

• A GPT-3.5 response is easy to spot: it can't do it. It will tell you something like, "As a large language model, I am unable to process images." GPT-3.5 Turbo has ZERO multimodal capabilities. It’s text-only.
• A GPT-5 response will analyze the image & answer your question. It can identify objects, interpret data, & even understand the context of the photo. Its performance on multimodal benchmarks like MMMU (which tests college-level visual reasoning) is state-of-the-art.

The Context Window Challenge: How Much Can It Remember?

• GPT-3.5 Turbo Context Window: 16,385 tokens.
• GPT-5 Context Window: A MASSIVE 400,000 tokens for input.

1. Find a very long article, a technical report, or even the full text of a short story. Something that is clearly over 20,000 words.
2. Paste the entire text into the chat window.
3. After you've pasted it, ask a very specific question about a detail from the beginning of the text. For example: “In the third paragraph of the document I just gave you, what was the specific name of the project mentioned?”

• A GPT-3.5 response will fail. It will have "forgotten" the beginning of the text because it exceeds its context window. It will likely tell you it can't find the information or give a generic answer based on the last part of the document it could remember.
• A GPT-5 response will answer the question perfectly. Its enormous context window means it can easily handle the entire document & recall specific details from any part of it.

The Subtle Clues: Tone, Caution, & "Laziness"

• GPT-3.5 characteristics:
  • Overly confident & sometimes wrong: It often presents information with a high degree of certainty, even when it's subtly incorrect or completely fabricated ("hallucinating").
  • Faster but more generic: It's known for being quick, but its output can be bland, repetitive, & lack nuance.
  • Less "Refusal": It's often more willing to take a stab at borderline or ambiguous prompts without the layers of safety warnings that newer models have.
• GPT-5 characteristics:
  • More Cautious & Nuanced: It's often more likely to qualify its answers, acknowledging complexity or admitting when a topic is subjective. Some users describe it as more "cautious."
  • Collaborative Feel: When coding or problem-solving, it can feel more like a partner. It might ask clarifying questions or break down the problem in a more structured way, making it feel like you're "getting there together."
  • Prone to "Thinking": Newer models, especially GPT-5, have a "thinking" mode that activates for complex queries. This can result in a slightly longer wait time for an answer, but the result is usually far more accurate & well-reasoned. If you notice the AI taking a moment before responding to a tough question, that's often a good sign you're using a more advanced model.
  • Lower Hallucination Rates: GPT-5 has significantly lower error & hallucination rates compared to older models, especially when its reasoning mode is active.

What To Do If You Suspect You're Getting a Downgrade

1. Check Your Subscription & Settings: If you're using ChatGPT, double-check that you're a Plus/Pro subscriber & that you haven't accidentally selected a legacy model (if the option even exists).
2. Report Bugs: If you see the "GPT-3.5" flash on the mobile app, report it. The more data OpenAI gets, the more likely they are to fix it.
3. Be Specific in Your Prompts: Sometimes, explicitly asking the model to use its most advanced capabilities can help. Start your prompt with "Using your most advanced reasoning and multimodal analysis capabilities..."
4. For Businesses, Control Your Stack: If you're building products on top of AI, don't leave the model choice to chance. Using a platform for conversational AI can give you more control & transparency. For instance, when you build a customer service chatbot, you want to ensure it’s consistently high-quality. A solution like Arsturn allows businesses to create custom AI chatbots that provide instant, reliable support 24/7. You train it on your specific business data, so you know exactly what knowledge it has & can trust it to engage with website visitors, answer questions, & generate leads effectively, without worrying about model-switching shenanigans under the hood.