Q&A with OpenAI Chief Research Officer Mark Chen about GPT-4.5, why it is not GPT-5, how it differs from a reasoning model, the AI scaling wall, DeepSeek, more
Alex Kantrowitz / Big Technology :
Context & Ripple Effects
OpenAI launched GPT-4.5 as a research preview while cautioning that it was not a frontier model and could trail its reasoning-focused offerings; subsequent coverage tested that positioning against coding benchmarks in a look at GPT-4.5's benchmark results.
Chen’s distinction between GPT-4.5 and GPT-5 matters because OpenAI’s later product direction moved toward a unified GPT-5 system that routes tasks between efficient and reasoning models. This interview captures the earlier product and research framing behind that split.
First-order effects
- The Q&A sharpens OpenAI’s positioning of GPT-4.5 as distinct from both a next-generation flagship and a reasoning model, giving users a clearer basis for matching model choice to task type.
- Discussion of scaling limits and DeepSeek puts the research conversation around GPT-4.5 beyond a simple version-number upgrade, emphasizing trade-offs among model approaches.
Second-order effects
- Model vendors face greater pressure to explain capability differences in practical terms—not just publish a new model name—when reasoning-oriented systems may outperform general models on some tasks.
- Enterprise buyers and developers are more likely to compare models by workload and evaluation results, reinforced by GPT-4.5’s reported performance relative to other OpenAI systems.
Third-order effects
- If this product framing persists, frontier AI competition will center less on a single successor model and more on portfolios that combine general-purpose and reasoning systems.
- The industry’s scaling debate may increasingly shift investment and differentiation toward post-training, routing, and task-specific performance, though this interview alone does not establish how quickly that transition will occur.
The trend: AI labs are moving from monolithic model releases toward differentiated systems whose value depends on selecting the right model and inference approach for each task.