Choosing the Right AI Model: When Speed Beats Reasoning

Описание и применение

Динамический роутинг моделей снижает задержку задач на 75 % и расходы на токены на 60 %, сохраняя корректность сложных задач.

Задача

Developers often default to the most powerful LLM, wasting time and tokens on simple tasks while risking errors on complex ones.

Результат

Dynamic routing cuts task latency by 75% and token usage by 60%, while preserving correctness for complex tasks.

Рабочий процесс

• 1. Classify task scope and complexity using file count, migration flag, and concurrency flag.
• 2. Route to Tier 1 (speed) for mechanical edits, syntax, linting.
• 3. Route to Tier 2 (balanced) for single‑file logic and tests.
• 4. Route to Tier 3 (reasoning) for architecture, migrations, concurrency.
• 5. Verify output with tests; if fail, retry or escalated to higher tier.
• 6. Commit if tests pass.

Доказательства

Establishing a tiered model router reduces end‑to‑end task latency by 75% and token spend by 60% compared to monolithic model usage.

Источник

Fast models (such as Claude 3.5 Haiku, GPT-4o-mini, or Gemini Flash) complete the token stream in 450ms, producing exact matches without hallucinated architectural redesigns.

Источник