Researchers show that ChatGPT 3.5 outperforms ChatGPT 4 in many tasks, including solving math problems, highlighting the issue of “drift” in improving AI models
Josh Zumbrun / Wall Street Journal :
Context & Ripple Effects
OpenAI introduced GPT-4 as surpassing ChatGPT on advanced reasoning, making this finding a meaningful qualification of the assumption that a newer flagship model is uniformly better. The reported gap instead puts task-level evaluation alongside broad capability claims.
Later coverage reinforces that performance is uneven by task and vintage: a GPT-3.5 coding assessment found stronger results on problems predating 2021 than on newer ones. Model updates, including GPT-4 Turbo’s revised response style, can therefore alter the practical behavior customers depend on.
First-order effects
- Teams using ChatGPT for math or other affected workflows cannot treat GPT-4 as an automatic upgrade over GPT-3.5; they need to test the versions against their own task sets.
- OpenAI’s headline reasoning comparison becomes less sufficient for buyers deciding which model to deploy, because reported performance varies across tasks.
Second-order effects
- Application vendors face pressure to add regression tests and potentially route different requests to different model versions rather than standardize on a single newest release.
- Benchmark results and product-update messaging become less decisive for customers, increasing the value of evaluations tied to a customer’s specific workload and quality threshold.
Third-order effects
- If drift persists, frontier-model competition will be measured less by a simple version ladder and more by reliability, reproducibility, and performance on defined use cases.
- The market may move toward task-specific model portfolios and evaluation infrastructure, with the economically relevant metric becoming useful output rather than nominal model generation.
The trend: Generative-AI adoption is shifting from choosing the newest general model to continuously validating the model-version combination that performs best for each production task.