OpenAI updates o3-mini's chain of thought to help free and paid ChatGPT users “understand how the model thinks”, in response to DeepSeek and other competitors
In response to pressure from rivals including Chinese AI company DeepSeek, OpenAI is changing the way its newest AI model …
TechCrunchKyle Wiggers
Context & Ripple Effects
OpenAI had already moved to broaden access to o3-mini, making reasoning-model capabilities available to free ChatGPT users for the first time in its wider o3-mini rollout. The product was initially framed around a private reasoning process, so this change shifts emphasis from the model merely reasoning to users being able to follow its output more effectively.
Competitive pressure is making the presentation of reasoning a product feature alongside model capability and speed. That matters because OpenAI is extending a differentiated interaction layer across both free and paid ChatGPT tiers rather than reserving it solely for enterprise access.
First-order effects
Free and paid ChatGPT users receive a revised o3-mini reasoning display intended to make the model’s responses easier to interpret.
OpenAI gains an immediately visible way to differentiate o3-mini as rivals challenge its reasoning-model positioning, building on the model’s earlier private reasoning design.
Second-order effects
Competing AI assistants face pressure to improve not only reasoning performance but also how clearly they expose and explain the path to an answer.
Reasoning UX becomes part of the value comparison between free and paid AI tiers, alongside access limits, speed, and model quality.
Third-order effects
If this pattern holds, reasoning models will compete increasingly on interface design and user trust, not just on opaque benchmark claims.
The market may move toward a layered AI offering in which the cost of a useful task depends on both underlying inference and the product controls that help users validate results.
The trend: Competitive AI products are turning reasoning from a back-end model attribute into a user-facing experience that must be understandable as well as capable.
@OpenAI o3-mini is exceptionally great, but I do worry that summarized chain-of-thought is actually worse than nothing at all. True CoT exposure acts as a prompt debugger. It helps us steer the model. Summarized CoT obfuscates this and potentially adds errors - makes it hard to d…
are y'all using those “reasoning” models that are pretrained to do chain-of-thought, like openai's o1/o3? I haven't tried these, and I'm a little sus that it's a straightforwardly beneficial thing you should use gpt4 instead of gpt3 for any question, but should you now use o1?
When we briefed people on 🍓 before o1-preview's release, seeing the CoT live was usually the “aha” moment for them that made it clear this was going to be a big deal. These aren't the raw CoTs but it's a big step closer and I'm glad we can share that experience with the world.
o3-mini is the first LLM released that consistently gets this tic-tac-toe question correct. The summarized CoT is pretty unhinged but you can see on the right that by the end it figures it out. [image]
chains of thought for o3-mini! (we try to organize the raw CoT to make it more readable, and optionally to translate languages, but we try to keep it quite faithful to the raw one) great work from @mia_glaese, @joannejang, @akshaynathan_ , and their teams! [image]