OpenAI: High-Difficulty ChatGPT Reasoning Interactions Rose 4x in 16 Months

OpenAI’s February 13, 2026 analysis focuses on a question many teams care about but rarely measure well: how reasoning quality changes in real user conversations over time. Instead of presenting only static benchmark snapshots, the report tracks model behavior in production-style interactions, then compares outcomes against human-calibrated baselines.

The company says the study covers more than one million ChatGPT conversations and uses weekly snapshots from September 2024 through January 2026. Over that period, the share of high-difficulty interactions that surpassed a human baseline rose about 4x. If that trend holds under independent replication, it suggests that reasoning gains are becoming visible not only in lab settings but in day-to-day workflows.

OpenAI highlights two open-ended task families to illustrate the shift. In management-consultant case interview scenarios, pass-level responses increased from roughly 16% to roughly 55%. In New York Times mini crossword-style tasks, success moved from roughly 2% to roughly 17%. These are useful examples because they require decomposition, hypothesis updates, and error correction rather than single-shot retrieval.

The release also includes conventional benchmark movement. OpenAI reports math AIME performance climbing from around 40% to around 80%, and coding USACO from around 11% to around 70% over the same broad period. The company attributes the gains to combined progress in model training, reasoning strategy, and evaluation loops.

For AI product teams, the practical takeaway is that model evaluation should be split into two layers: standardized benchmarks for comparability and live-task cohorts for operational relevance. A model that improves on both layers can change staffing plans, escalation policies, and the threshold for automation in knowledge-intensive work.

There are still caveats. Conversation distributions can shift over time, user prompts are not controlled experiments, and human-judged quality labels can introduce domain bias. Enterprises should therefore treat vendor analyses as directional evidence and reproduce the methodology on internal logs before making policy-level commitments. Even so, the report sets a useful template for longitudinal reasoning measurement at scale.