o3-mini-high

A peer-reviewed Scientific Reports article systematically analyzes reasoning chain length across o1-mini and o3-mini variants on the Omni-MATH benchmark, with explicit comparison of o3-mini (m) versus o3-mini (h). The paper finds that o3-mini (medium) achieves superior accuracy over o1-mini without requiring longer rea Critically for the o3-mini-high subject, the study reports that o3-mini (high) achieves only a marginal accuracy gain over o3-mini (medium) by allocating substantially more reasoning tokens across all problems, including ones the medium setting can already solve. The accuracy drop with longer chains is significantly sm

Artificial Analysis' provider dashboard ranks 61 OpenAI models and explicitly lists "o3-mini (high)" as the fastest OpenAI model at 237 tokens per second output speed, with "o3-mini" at 207 t/s as the second fastest. This provides the most concrete quantitative throughput measurement for the high variant among the cand The page also documents that o3-mini-high falls into a pricing tier well above the cheapest models (GPT-5 nano at $0.05 blended per 1M tokens versus o3-mini variants priced higher), reflecting its premium reasoning positioning. The aggregator uses the variant naming "o3-mini (high)" rather than Poe's slug "o3-mini-high

This third-party recap (metadata dated August 13, 2026) covers OpenAI's January 31, 2025 announcement of o3-mini and its reasoning-effort tiers, explicitly naming "o3-mini-high" as the highest-accuracy variant for demanding tasks requiring greater reasoning depth. The article cites OpenAI's official X post confirming a The piece describes o3-mini-high's positioning within a three-tier reasoning-effort system (low, medium, high) where the medium setting matches o1 performance with faster responses, while high offers greater accuracy for precision-critical workloads. It contextualizes the release alongside competitor developments inclu