Open models in the 25B–34B parameter range are handling 90% of complex agent tasks at a fraction of the cost, emerging as an efficient alternative for enterprise AI projects.
Imagine your company wants to automate secretarial tasks using AI. You decide to adopt an ‘agent’—AI that sets its own goals and carries out work through complex steps, going beyond simply answering questions—to check emails, coordinate schedules, and find and organize necessary materials. However, when you calculate the implementation costs, they are astronomical. Naturally, you might start to question, “Is this really worth implementing?”
Recently, an amazing shift has been occurring in the AI industry to lower this ‘barrier of cost.’ The era where bigger and more expensive models are the only answer is coming to an end, and small but efficient ‘open models’ (AI 공개 to the public for free use) are rapidly taking their place.
Why is this important?
The biggest obstacles for companies pursuing AI projects are the gap between ‘cost’ and ‘value.’ In fact, according to research by Gartner, about 40% of current agentic AI projects are at risk of being canceled by the end of 2027. This isn’t just because the AI lacks intelligence. It’s because of excessively high operational costs, unclear business outcomes, and the absence of proper risk management Source: Agentic AI Readiness Is the Reason Gartner Says 40 Percent of….
In other words, many companies are struggling while using heavy models that lack ‘cost-effectiveness.’ In this climate, the emergence of cost-efficient models will be a crucial turning point, helping companies generate tangible benefits without giving up on AI.
Easy Understanding: The Era of ‘Efficiency,’ Not ‘Bulk’
To use a simple analogy, if past AI development was like hiring a ‘superhero’ capable of doing everything for an enormous salary, it is now changing to a system where you build a team of ‘specialists’ perfectly suited for your specific tasks at a reasonable cost.
Checking data as of September 2026 reveals a surprising result. Open models in the 25B to 34B parameter range (a value determining the size of the AI’s ‘brain’) can now perform most of the work that previously required 70B-class hardware. This means a single 24GB graphics card (GPU) is sufficient to handle over 90% of modern agentic tasks Source: The Efficient Frontier of Open Models: Finding the Sweet Spot in 2026.
Even more surprising is the price. For example, ‘GPT-6 Luna,’ which has recently emerged as a replacement model among agent platforms, costs 1/20th the price for input and 1/24th the price for output compared to the existing high-performance ‘GPT-5.6 Terra’ Source: GPT-6 Luna Is Becoming the Default: What a Dime Buys. It delivers the same work performance as before at a near-negligible cost.
Current Situation: How far have we come?
Currently, many open-source projects and agent platforms are focusing on this efficiency. With technologies like OpenSandbox—which helps ensure safe task execution—joining foundation projects, companies can now utilize affordable open models in verified environments Source: Open Sandbox Joins AAIF as Open… - Agentic AI Foundation (AAIF).
However, there are points to be aware of. Simply because a model has fast performance (such as token processing speed) does not mean all agent tasks are automatically efficient. Because there are many variables to consider beyond speed during training or complex reasoning processes, voices are calling for considering the efficiency of the entire system rather than just assuming faster is always better Source: In agentic RL, faster tokens are not enough.
What will happen in the future?
Moving forward, the core of tech competition will shift from ‘who built the larger model’ to ‘who combined these smaller models more efficiently.’ Instead of building complex infrastructure for enterprise-wide AI adoption, services that provide workspaces optimized for each client using agent platforms will likely become more active Source: Agentic Workers: the back office for AI service providers.
As Gartner fears, to prevent agentic AI projects from being canceled due to unbridled expectations, what is needed right now is not a ‘grand myth’ but the discernment to find ‘realistic cost-effectiveness.’ With efficient models now ready, the popularization of AI agents may be approaching us faster than we think.
MindTickleBytes’ AI Reporter’s Perspective: Ultimately, AI is a business. No matter how smart a model is, if it is 20 times more expensive, a company cannot sustain it. AI technology has now moved beyond the laboratory goal of ‘how much smarter can we get’ into the practical stage of ‘how much tangible value can we provide to our lives at a lower cost.’
References
- The Efficient Frontier of Open Models: Finding the Sweet Spot in 2026
- GPT-6 Luna Is Becoming the Default: What a Dime Buys
- Agentic AI Readiness Is the Reason Gartner Says 40 Percent of…
- Open Sandbox Joins AAIF as Open… - Agentic AI Foundation (AAIF)
- In agentic RL, faster tokens are not enough
- Agentic Workers: the back office for AI service providers
- Below 10B
- 25B to 34B
- Above 70B
- Input costs are 1/20th the level
- Training speed is twice as fast
- Parameters are 10 times larger
- Lack of AI performance
- Increased costs and unclear business value
- Data security issues