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Daily blended price ($/1M) — recorded each day, builds into a trend over time.
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.
o4-mini-deep-research is OpenAI's faster, more affordable deep research model—ideal for tackling complex, multi-step research tasks. Note: This model always uses the 'web_search' tool which adds additional cost.
o4-mini-deep-research is a Reasoning model from OpenAI (US). HotON.ai tracks it at $2.00 per 1M input tokens and $8.00 per 1M output tokens, with a 200K-token context window. Its composite efficiency score is 86/100 at an estimated $0.008 per successful task.
o4-mini-deep-research is tracked at $2.00 per 1M input tokens and $8.00 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $6.50 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.
o4-mini-deep-research 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, o4-mini-deep-research is cheaper than 15% of models on input price and ranks #448 of 535 by overall efficiency.
Yes — qwen-plus-2025-07-28:thinking is a lower-cost option at $0.78 per 1M output tokens, while still covering similar Reasoning use cases. Compare them side by side on HotON.ai.
Ready to paste into articles, papers or AI prompts — prices and date refresh with the live data.
HotON.ai — o4-mini-deep-research (OpenAI): $2.00/1M input, $8.00/1M output, as of 2026-04-28. https://hoton.ai/en/models/openai-o4-mini-deep-researchPricing 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.