The Real AI Race May Not Be 关于 Bigger Models It May Be 关于 Cheaper 智能
时间 I look at this comparison from my 财务 and accounting role at AINNA, the model name is not the line item that moves the business case.
The bigger story is the cost curve.
In this setup, pricing drops from roughly $0.20/M input and $1.20/M output to $0.10/M input and $0.50/M output, while retaining the same stated context window and tool capabilities.
If that trend holds, it changes the economics of building AI 系统 — not just the technical benchmark.
从 my side, this is another strong reason to bulk-distill local LLMs and possibly SLMs as well.
Instead of carrying expensive external inference as a permanent operating cost, we can use increasingly optimized models as teachers to generate training data, refine 工作流, build domain-specific reasoning patterns, and continuously improve our own local models.
The goal is not necessarily to build the biggest model.
The goal is to build a model that is optimized enough for its actual job.
For Malaysian 中小企业, that could mean smaller models handling accounting, asset management, inventory, operations, customer service, machinery control, document processing, or internal automation, while larger external models are only used when truly necessary.
I would like this pricing trend to continue.
At least until our own local LLMs are mature enough to handle most of the workload independently — and at a cost per transaction the business can defend.
Use optimized models to build. Distill what matters. Reduce dependency over time.
