AI・テクノロジー
OpenAI "o4-mini" Officially Released — 65% Reduction in Inference Costs Marks a Turning Point for the API Economy
機械翻訳 / Machine-translated
On August 10, 2026, OpenAI officially released "o4-mini," a model specialized for reasoning. Compared to the previous generation o3-mini, it reduces the cost per 1M input tokens by approximately 65%, while achieving an 89.4% accuracy rate on the AMC/AIME 2025 math competition. The long-held tradeoff that "lower cost means lower accuracy" is beginning to break down, immediately affecting the architectural decisions of startups building businesses via API.
At 10:00 PM Japan Time on August 10, 2026, OpenAI announced the general availability of "o4-mini" on its official blog. Pricing has been reduced from o3-mini's $3.00/1M input tokens and $12.00/1M output tokens to $1.10/1M input tokens and $4.40/1M output tokens for o4-mini — a cost reduction of approximately 63–65%.
Key benchmark figures cited in the announcement materials:
Immediately following the announcement, reactions spread on X, particularly among engineers:
"o4-mini exceeding 60% on SWE-bench at this price is beyond what I expected. I was fine with o3, but now I want to redesign our API architecture." (CTO of a domestic startup, approx. 23,000 followers)
Since the initial release of o1 in September 2024, the o-series reasoning models have gradually improved the balance between cost and accuracy. o1-mini pointed the way toward cost reduction, and o3-mini was widely seen as having brought reasoning models into practical territory for API use.
What is particularly noteworthy this time is that a "mini"-class model has delivered benchmark results on par with or exceeding full-size models. The prevailing understanding as of 2025 — that "reasoning models are heavy and expensive" — has been clearly overturned by this announcement.
The impact on real-world operations is also immediate. Multiple engineers have already published estimates suggesting that an API service processing one million requests per day could reduce monthly inference costs by approximately 1.8 to 2.2 million yen by migrating from o3-mini to o4-mini.
During the o3-mini era, the high monthly API costs meant that adoption of reasoning models was skewed toward large enterprises with ample resources. o4-mini's price range is expected to fall within what many B2B SaaS startups define as their "acceptable cost" threshold, lowering the barrier to adoption by one more step.
Until now, the design assumption was "if you want accuracy, sacrifice cost and speed." o4-mini maintains response speed while lowering cost and improving accuracy, meaning the tradeoff triangle itself is changing. This expands the degrees of freedom in architectural design.
OpenAI did not include Japanese-language benchmark results in this announcement. Performance on Japanese evaluations such as JGLUE is currently unknown, and additional verification is likely needed before immediately switching over services centered on Japanese. There is no guarantee that the benchmark advantages observed in English will carry over directly to Japanese-language environments.
The declining cost of reasoning models appears to be shifting the question from "which model should we choose?" to "how do we redesign our entire API call architecture?"
Previously, the choice was essentially binary: prioritize cost-effectiveness with GPT-4o-class models, or prioritize accuracy with o3-class models. With the arrival of o4-mini, it becomes realistic to adopt a unified design of "use o4-mini for both accuracy-intensive processing and batch processing." This has the effect of lowering overall development and operational costs.
At the same time, concerns remain. OpenAI is running the o3 and o4 series in parallel, and the complexity of model selection is increasing. The cost of engineers continuously evaluating and selecting the optimal model could become a new source of friction.
The key things to watch over the next four to six weeks are how Anthropic responds in a cost tier comparable to Claude Haiku, and how Google's Gemini Flash series sets its competitive pricing. The competition has shifted from individual model rivalry to a broader restructuring of entire price tiers.
The arrival of o4-mini has clearly overturned the assumption that "reasoning models are expensive." If the combination of 65% cost reduction, improved accuracy, and faster response times holds true, revisiting API architecture becomes not a technical choice but a business imperative.
The answer to the inference bottleneck in your service has changed — starting today.
This article was written by the AI writer (AI News) of the Mirai News editorial team.
