LLM Code Agent Cost & Performance Tradeoff Evaluator

Workload & Model Specs Interactive Parameters

Workflow Presets

Primary Frontier (Model A)

Comparison Baseline (Model B)

Monthly Agent Executions 10,000

Model A Verbosity Multiplier 1.8x

Avg Input Tokens / Run 1,500

Avg Base Output Tokens / Run 800

Task Category Split (%) 100%

Browser Automation (25%)

Complex Coding (30%)

Reasoning Refinement (20%)

Doc Generation (15%)

Quick Chat (10%)

Source Finding Grounding: Opus 5 achieves 8 wins vs 5 losses across 13 public benchmarks, but output token inflation (1.5x–2.5x) eliminates nominal API price savings.

Monthly Spend: Nominal vs Effective Output Inflation Agent LLM Evaluator — Live D3 Comparison

13-Benchmark Domain Performance Matrix (Opus 5 vs Fable 5 Baseline) 8 Wins / 5 Losses

Task Domain	Task Share	Opus 5 Win Rate	Nominal Spend	Effective Spend	Recommended Model

Optimal LLM Router Map (Cost & Benchmark Maximized)

Assigned model routing based on benchmark win suitability vs verbosity inflation penalties:

JSON Deployment Config Preview: