OpenAI's o3-mini costs $1.10 per 1M input tokens and $4.40 per 1M output tokens, cheaper than GPT-4o, which costs $2.50 and $10, and o1, which costs $15 and $60
Simon Willison's Weblog : X: @simonw and @daniel_mac8 . Forums: Hacker News X: Simon Willison / @simonw : Has anyone seen anything interesting done with that increased output limit yet? o1 has 100,000 as well, o1-mini is 65,536 and o1-preview was only 32,768 (Reasoning tokens come out of that same budget, but I imagine there are prompts that can reason briefly and then output long) Dan Mac / @daniel_mac8 : the fact that o3-mini has a 100,000 token output length, and that output length is so much larger in comparison to previous models lends a lot of credence to Ben's idea that the ‘o’ series reasoning models are best seen as ‘brief’ generators rather than chatbots [image] Forums: Hacker News : Notes on OpenAI o3-mini
Context & Ripple Effects
OpenAI’s reasoning line had previously been framed as a cost-and-performance trade-off relative to GPT-4o, rather than a simple upgrade path, in coverage of o1’s reasoning trade-offs.
The listed o3-mini rates sharpen that positioning: the newer reasoning model is priced below both GPT-4o and o1 on input and output tokens. Related launch coverage also characterized o3-mini as a faster, lower-cost model near o1-class capability at launch.
First-order effects
- For API buyers, o3-mini’s stated $1.10 input and $4.40 output rates lower the direct token bill versus the listed GPT-4o and o1 rates.
- OpenAI now has a lower-priced reasoning option, giving customers a clearer cost-based choice between o3-mini, GPT-4o, and o1.
Second-order effects
- Teams that had treated reasoning as too costly for token-heavy workflows can reassess model routing, especially where output-token charges materially affect total spend.
- The pricing gap increases pressure on competing model offerings to justify their own price-performance position; earlier low-cost models had already pushed token pricing downward in the GPT-4o mini comparison.
Third-order effects
- If lower-priced reasoning models continue to approach higher-tier capability, model selection will shift further from a single flagship choice toward workload-level routing based on effective cost per useful result.
- The market may increasingly segment between economical reasoning tiers and premium, compute-intensive tiers, with published token rates serving as a more visible product differentiator.
The trend: This is one data point in the move from premium reasoning models toward tiered inference pricing that makes advanced reasoning viable for more production workloads.