OpenAI o1, o3 and the Reasoning-Model Family

In September 2024 OpenAI shipped o1-preview and o1-mini and re-anchored what a frontier model could do on hard reasoning. By the end of 2024 o3 had posted numbers on ARC-AGI that the field had been calling out of reach for at least a year. The o-series is the most visible artefact of the test-time-compute era. It is also a model line whose internals are partially opaque by design - this section keeps the public/speculated line clear because that distinction is load-bearing for any engineer using the API.

What is publicly known about o1

From OpenAI's "Learning to reason with LLMs" launch post and the o1 system card:

• o1 was trained with large-scale reinforcement learning on chains of thought. The RL teaches the model to "think productively" before answering - to plan, decompose, check, backtrack.
• o1 generates a long internal reasoning trace before its final answer. This trace is the source of the accuracy gain over GPT-4o on competition maths, programming and PhD-level science.
• Accuracy scales with both more train-time RL compute and more test-time thinking tokens. OpenAI showed the two scaling curves alongside each other - the first time test-time compute was presented as a first-class scaling axis.
• The model achieved IMO-qualifier-level scores (~83% on AIME 2024 vs GPT-4o's ~13%) and competitive-programming Codeforces percentiles around the 89th when given a thinking budget.

What is publicly known about o3

o3 (announced December 2024, full release through 2025) extended the same recipe:

• The headline result was 87.5% on ARC-AGI-1 in a high-compute configuration, with a low-compute setting at 75.7%. Human baseline on the same set is ~85%. Prior best for a non-specialist model was in single digits.
• ARC Prize Foundation noted the high-compute result used thousands of dollars of compute per task. This is the caveat that drove most of the post-launch discussion: o3 demonstrated capability, not deployable efficiency, at the headline number.
• o3 also posted strong numbers on FrontierMath, GPQA Diamond, SWE-bench Verified. Most numbers were paired with multiple compute settings, making the cost/accuracy frontier explicit.

The o3-mini follow-up (released early 2025) targeted the cost-efficient point of the same curve, with reasoning_effort levels (low/medium/high) the developer chooses per call. Same family, different operating point.

What is speculated, not confirmed

• Exact RL algorithm. OpenAI has not published whether o1 uses PPO, GRPO, a variant, or something proprietary. Independent guesses lean toward an RLVR-style setup on maths and code, possibly with a learned PRM for partial credit, but this is inference from public numbers, not disclosure.
• Whether reasoning is one-shot or includes inner search. It is plausible that o1 and especially o3 run some form of inference-time search (beam, MCTS) under the hood. OpenAI describe it as the model "thinking", which is consistent with either pure long-CoT generation or with internal search. The distinction matters for cost modelling.
• Whether the o3-on-ARC pipeline includes task-specific tuning. ARC's high-compute setting reportedly involved generation of many candidate programs per task. How much of that is intrinsic to o3 vs a wrapper around it is not fully disclosed.