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Blended $/1M across tracked versions of this line.
Typical 3:1 output-to-input mix, per 1M tokens
Price as of 2026-04-28 · Source: legacy_model_catalog
Complex reasoning, analysis, planning and multi-step problem solving where answer quality matters more than raw cost.
OpenAI o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and coding. This model supports the `reasoning_effort` parameter, which can be set...
o3-mini is a Reasoning model from OpenAI (US). HotON.ai tracks it at $1.10 per 1M input tokens and $4.40 per 1M output tokens, with a 200K-token context window. Its composite efficiency score is 88/100 at an estimated $0.004 per successful task.
o3-mini is tracked at $1.10 per 1M input tokens and $4.40 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $3.58 per 1M tokens. Figures are illustrative demo data.
Complex reasoning, analysis, planning and multi-step problem solving where answer quality matters more than raw cost.
o3-mini supports up to a 200K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, o3-mini is cheaper than 26% of models on input price and ranks #364 of 535 by overall efficiency.
Yes — grok-4-1-fast-reasoning is a lower-cost option at $0.50 per 1M output tokens, while still covering similar Reasoning use cases. Compare them side by side on HotON.ai.
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HotON.ai — o3-mini (OpenAI): $1.10/1M input, $4.40/1M output, as of 2026-04-28. https://hoton.ai/en/models/openai-o3-miniPricing is real (via the TestKey catalog, updated daily). Quality (Arena Elo) is real where the model is ranked on LMArena. Efficiency is a modeled composite of real price and context.