Don’t use a sledgehammer to crack a nut

Token Optimization: Why the Most Powerful Model Might Be the Worst Choice

Most AI products use models that are far too powerful for simple tasks. It seems like a safe decision… until the bill grows and latency starts affecting the user experience.

At NEXAGON, we see this pattern constantly: architectures where everything goes through the most advanced model, even tasks that could be solved at a fraction of the cost.

It’s like trying to use a sledgehammer to crack a nut.

The Paradigm Shift: Multi-Model Architecture

With the evolution of:

• Gemini (Flash Lite, Flash, Pro)
• Claude (Haiku, Sonnet, Opus)
• OpenAI (GPT-4.1 nano/mini, GPT-4.1, GPT-4o, o1)

it no longer makes sense to design systems based on a single model.

Modern architectures are vendor-agnostic: they combine models based on cost, speed, and cognitive capability.

NEXAGON Token Efficiency Framework

Instead of thinking in terms of models, we think in terms of types of work.

1. The Worker: Extraction and Formatting

Repetitive and structured tasks:

• document parsing
• JSON extraction
• data normalization

Ideal models:

• gemini-2.0-flash-lite
• gpt-4.1-nano

Extra power adds no value here.

2. The Analyst: Classification and Summarization

Medium-complexity language tasks:

• summaries
• sentiment analysis
• contextual classification

Ideal models:

• claude-3-haiku
• gemini-flash
• gpt-4.1-mini

The perfect balance between cost and quality.

3. The Expert: Code and Advanced Reasoning

Tasks where errors are expensive:

• software architecture
• refactoring
• multi-step reasoning

Ideal models:

• claude-3-5-sonnet
• gpt-4.1
• gpt-4o (multimodal)

Here, cost translates directly into human savings.

4. The Architect: Critical Decisions

Reserved for:

• strategic analysis
• complex synthesis
• multidisciplinary decisions

Ideal models:

• claude-3-opus
• o1 (reasoning models)
• gemini-pro

These models should not be used by default.

The Intelligent Router

Optimization happens when selection is automatic.

User Request
      ↓
 Complexity Analyzer
      ↓
 ┌───────────────┬───────────────┬────────────────┐
 Lite Models     Mid Models      Frontier Models
 (Flash/Nano)    (Haiku/Mini)    (Sonnet/o1/Opus)

async function processTask(payload) {
  const complexity = analyze(payload);

  if (complexity === "LOW") return geminiFlashLite.generate(payload);
  if (complexity === "MEDIUM") return gpt41Mini.generate(payload);
  if (complexity === "HIGH") return claudeSonnet.generate(payload);

  return o1.generate(payload);
}

Real ROI at Scale

When a product grows from hundreds to thousands of users, the difference becomes massive.

Additional benefits:

• lower latency (TTFT)
• higher throughput
• better user experience

The NEXAGON Way

This is the core pattern we use to design cost-efficient AI architectures:

THINK
↓
Classify the task by complexity and risk.

BUILD
↓
Assign the most efficient model for each type of work.

SCALE
↓
Automate routing and measure cost + performance.

This way of working allows us to make technical decisions aligned with business goals from day one.

Conclusion

Modern AI engineering is not about using the biggest model.

It’s about using the right model for each decision.

The difference between an expensive prototype and a profitable product lies in orchestration.

The 3 key takeaways:

1. Not every task needs frontier models.
2. Intelligent routing reduces costs and improves UX.
3. Multi-model architecture is the new standard.

At NEXAGON, we believe that:

AI does not scale when you buy more intelligence.
It scales when you design better architecture.

Designed for operational efficiency at NEXAGON.