OpenAI's o1 is a notable departure from older models, representing the AI industry's shift to reasoning models to overcome the limits of prediction-based LLMs
This week, openai launched what its chief executive, Sam Altman, called “the smartest model in the world”—a generative-AI program …
The AtlanticMatteo Wong
Context & Ripple Effects
OpenAI’s o1 arrived amid early evidence that its reasoning-oriented approach could outperform prior models on some problem-solving tasks, while still showing notable weaknesses in areas such as spatial reasoning. Coverage also stressed that it was not a simple replacement for GPT-4o: the reasoning gains came with meaningful cost and performance trade-offs.
The launch therefore matters less as a blanket claim of intelligence than as a product and technical fork: model providers can optimize for deliberate, higher-cost inference on tasks where a stronger result justifies the added expense.
First-order effects
OpenAI gains a distinct reasoning-model tier alongside its earlier general-purpose models, giving developers and users a new option for tasks where step-by-step problem solving is more valuable than speed or low cost.
Competing AI providers face pressure to demonstrate reasoning performance, not just broad benchmark gains, while also explaining the latency and pricing trade-offs of their approaches.
Application builders are pushed toward routing work between lower-cost general models and reasoning models, making inference cost and response time more central to product design.
Third-order effects
If reasoning models continue to improve through additional inference-time compute, frontier AI product lines are likely to segment more clearly by task criticality and willingness to pay rather than converge on one default model.
The later introduction of a more compute-intensive o1-pro tier suggests that better reasoning may increasingly be packaged as premium capacity, reinforcing AI industrialization around compute allocation and pricing.
The trend: AI is moving from one-size-fits-all predictive language models toward tiered systems that trade more compute, time, and cost for stronger performance on difficult reasoning tasks.
Is OpenAI's latest AI “reasoning” model a genuine step forward—or just another of the company's magic tricks? @matteowong.bsky.social on why it might be both:
Is OpenAI's latest AI “reasoning” model a genuine step forward—or just another of the company's magic tricks? @matteowong.bsky.social on why it might be both:
When preparing for the o1 demo, I tried this problem (screenshot), which is the only problem that o1-preview got wrong from this year's Korean SAT exam (more info in 🧵) The prompt was simply “Read the passage in the first image and solve problem 8 in the second image” o1 got [ima…
That's what I have been saying. The marketing on release day doesn't matter. In the long term it's just about how good the product is. It's just extremely easy for people to hate OpenAI as the market leader. Especially when your own benchmarks are somewhat cooked.
OpenAI finally released full o1 And people are already doing wild use cases with it. 10 examples: (proceeds to spit out prompts that llama-1B can solve)
OpenAI o1 pro is *significantly* better than I anticipated. This is the 1st time a model's come out and been so good that it kind of shocked me. I screenshotted Coinbase and had 4 popular models write code to clone it in 1 shot. Guess which was o1 pro. [image]
I've been throwing a bunch of coding tasks at the new o1 and it feels like it may be equivalent to Claude 3.5 Sonnet for the kind of code I write... but noticeably faster Might become a daily driver for me