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The $1 vs. $100 AI Decision: Model Selection for Every Task

By Weavecode Team
Published 2026-07-14
2 min read
The $1 vs. $100 AI Decision: Model Selection for Every Task

The $1 vs. $100 AI Decision: Model Selection for Every Task

For startups building AI products, model selection is a primary cost driver. A mismatch here directly impacts your gross margins.

Choosing a model is not a simple choice between "best" and "worst." It is a multi-dimensional design decision where you must balance Cost, Speed, and Accuracy for every specific query.

We call this The $1 vs. $100 Decision: routing simple tasks to cheap, high-speed models, and reserving expensive premium models strictly for high-value reasoning.

1. Defining the Task Tiers

               ┌───────────────────────┐
               │    Incoming Request   │
               └───────────┬───────────┘
                           ▼
               ┌───────────────────────┐
               │   Complexity Router   │
               └─────┬───────────┬─────┘
         ┌───────────┘           └───────────┐
         ▼                                   ▼
┌──────────────────┐               ┌──────────────────┐
│  Tier 1: Simple  │               │ Tier 2: Complex  │
│ (Llama/Qwen $1)  │               │ (Claude/GPT $100)│
└──────────────────┘               └──────────────────┘

1.1 The $1 Task Tier (High-Speed / Low-Cost)

  • Tasks: Sentence classification, sentiment checks, formatting, extraction, language translation.
  • Requirements: Low latency (< 1s), cheap token rates.
  • Best Models: Qwen 2.5 72B, Llama 3 70B.

1.2 The $100 Task Tier (High-Reasoning / Premium)

  • Tasks: Writing multi-file code updates, auditing complex contracts, executing multi-agent planning loops.
  • Requirements: Complex logic, zero-shot accuracy.
  • Best Models: Claude 3.5 Sonnet, GPT-4o.

2. Implementing the Router Matrix

To implement this dynamically, your gateway router should evaluate the task complexity programmatically.

import os
from openai import OpenAI
 
client = OpenAI(base_url="https://api.weavecode.ai/v1", api_key=os.getenv("WEAVECODE_KEY"))
 
def route_task(task_type: str, prompt: str) -> str:
    # 1. Simple queries go to cheap open-source models
    if task_type in ["translation", "summarization", "sentiment"]:
        model = "qwen/qwen-2.5-72b-instruct"
    # 2. Medium tasks go to Gemini
    elif task_type in ["rag_search", "classification"]:
        model = "google/gemini-1.5-flash"
    # 3. Complex tasks go to Claude
    else:
        model = "anthropic/claude-3-5-sonnet"
        
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

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