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You Don't Need Trillion-Parameter Models: Faster, Cheaper LLMs Are the Key to AI Accuracy

Faster, Cheaper LLMs Are the Key to AI Accuracy
Faster, Cheaper LLMs Are the Key to AI Accuracy

Introduction: The Myth of Bigger Models

For years, the narrative has been that larger models with trillions of parameters are the future of AI. Companies continue to invest billions into building expansive general-purpose models like GPT-4 and Claude3.5. Trained on massive public and proprietary datasets, these monolithic model architectures are designed for high zero-shot accuracy. 

However, with the introduction of AI agents, there has been a noticeable shift in how LLMs are chosen. Initially, the belief was that bigger models provided better accuracy. But in agentic workflows, the model doesn’t just take in an input and generate a response— it has to reason on the task via an iterative process (generating more tokens, a.k.a. reasoning tokens in the process). This iterative technique proves to deliver much better accuracy than reliance on sheer size alone.

In his recent talk on AI Agents, renowned AI pioneer Andrew Ng shared his support on these views. He highlighted two points:

Fast token generation is important. Generating more tokens even from a lower quality LLM can give good results. 

If you’re looking forward to running GPT-5/Claude 4/Gemini 2.0 (zero shot) on your application, you might already be able to get similar performance with agentic reasoning on an earlier model.

Source: What's next for AI agentic workflows ft. Andrew Ng of AI Fund

AI agents don’t need massive models—they need models optimized for speed, cost-efficiency, and iterative reasoning loops. This shift in thinking is reshaping how we approach accuracy in AI systems.

How AI Agents Achieve High Accuracy

The Iterative Strategy of AI Agents

AI agents today rely on a Think/Research <-> Revise iterative approach to refine responses. Here’s how it works:

  1. Generate: The AI generates an initial response (e.g., a plan, function call, or summary).

  2. Critique: A critique agent or an internal process evaluates the output, identifying errors or areas for improvement.

  3. Revise: A new iteration begins to address the critique and refine the response.

Each loop improves the result, creating a progressively refined and accurate output.

Why Speed and Cost Matter in Iterative Loops

  1. Latency is a Bottleneck

    • High latency slows down each iteration of the Think/Research <-> Revise loop, leading to longer response times.

    • Faster LLMs allow for more iterations in less time, enabling higher accuracy without sacrificing user experience.

  2. Cost Accumulation in Iterations

    • Iterative reasoning involves multiple API calls.

    • A cheaper reasoning LLM ensures lower costs per iteration, enabling more iterations at the same price as a single, expensive iteration from a trillion-parameter model.

The Problem with Large Monolithic LLMs

1. Built for Zero-Shot Scenarios

Models like GPT-4o and Claude3.5-Sonnet are optimized for scenarios requiring no iterations, focusing on high accuracy from the first response. While this is impressive, it also:

  • Drives up inference costs.

  • Makes these models ill-suited for iterative agentic workflows.

2. High Training and Inference Costs

  • These trillion-parameter models are trained on massive datasets costing billions, making them expensive to maintain and operate.

  • Running multiple iterations with these models in agentic workflows is prohibitively costly.

3. Iterative Models Achieve Comparable Accuracy

Andrew Ng’s theory highlights that existing models can achieve or even surpass GPT-5-level performance on many tasks through iterative refinement. Instead of relying on a single zero-shot response, they leverage multiple Think/Research <-> Revise loops to refine outputs.

Introducing TheAgentic Reasoning LLM

Faster, Cheaper, Smarter

TheAgentic Reasoning LLM is purpose-built for agentic workflows, offering:

  1. Rapid Inference: Processes iterations faster than GPT-4o, Claude3.5-Sonnet, or GPT-o1.

  2. Cost-Efficiency: 90% cheaper than monolithic models, making iterative loops affordable.

  3. Unmatched Accuracy: With the ability to run more iterations at lower costs, TheAgentic Reasoning LLM achieves better accuracy than trillion-parameter models in agentic workflows.

Why TheAgentic is the Future

  • Iterative Optimization: Designed to excel in multi-step reasoning and function calling, refining outputs with minimal latency.

  • Custom Vertical LLMs: Offers fine-tuned models for specific domains, ensuring contextual accuracy and fewer hallucinations.

  • Accessible AI: 90% cheaper than GPT-4o and Claude3.5-Sonnet, TheAgentic LLMs bring enterprise-grade AI capabilities within reach of startups, SaaS developers, and IT consultancies.

Conclusion: Rethink AI Accuracy

The future of AI isn’t about who can build the largest model—it’s about who can optimize for speed, cost, and accuracy in real-world workflows. With TheAgentic, you get:

  • Affordable, high-accuracy iterative reasoning.

  • Faster response times for better user experiences.

  • A sustainable alternative to costly monolithic models.