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The thing to understand about o1 is that it was a research release that happened to ship as a product, and its limits come from that status. OpenAI's own wording is that the work needed to make this model as easy to use as current models was still ongoing, and they released o1-preview for immediate use while that work continued. Read that sentence again and the whole value proposition falls into place: o1 is a demonstration of a capability, not a polished assistant. Where it genuinely wins is verifiable-reasoning work. The AIME progression is the clearest evidence — 74% on a single sample, 83% with consensus across 64 samples, 93% when re-ranking 1000 samples with a learned scoring function. That last figure puts it in the top 500 students nationally and above the USA Mathematical Olympiad cutoff, and the gap between 74% and 93% tells you something practical: if you can afford the compute, sampling and re-ranking buys you real accuracy. The same pattern showed up in competitive programming. The IOI-trained variant of the model competed under human contest conditions, and submitting 50 candidate solutions chosen by a test-time selection strategy — model-generated test cases plus a learned scorer — was worth nearly 60 points over random submission. On relaxed constraints at 10,000 submissions, it cleared the gold medal threshold. That is also the weakness, dressed up as a strength. Every one of those gains costs latency and money, and the 89th percentile Codeforces result came with an unusual amount of compute per submission. Nothing in o1's design is cheap. If your task rewards deliberation, you are getting what you pay for. If your task is a lookup, a rewrite, or an open-ended conversation, you are paying a reasoning tax for nothing. OpenAI was unusually direct about the natural-language problem. o1 significantly outperforms GPT-4o on the vast majority of reasoning-heavy tasks, but the human preference evaluation does not carry across to open-ended chat, and the vendor stated it plainly rather than burying it. That is why the current ChatGPT lineup is arranged the way it is: GPT-6 Astra and GPT-5.6 Sol handle advanced reasoning in the Plus tier, GPT-5.6 Luna and GPT-5 Thinking Mini cover lighter work, and Legacy models is where o1 lives — reachable on Plus and Pro only, and not on Free or Go at all. Practically, there are two ways to use this. The first is via the OpenAI API, which is where the seed data points and where a reasoning-heavy pipeline can still call the model directly. The second is inside ChatGPT, which for new work is the wrong door — you are on a legacy tier-gated slot while the default models keep moving forward. Vision is worth flagging: o1 scored 78.2% on MMMU with vision perception enabled, the first model OpenAI described as competitive with human experts on that benchmark, so multimodal reasoning problems are in scope. Keep the GPQA caveat in mind too, because OpenAI published it for a reason: beating PhD experts on