Coding agents such as Claude Code and OpenAI Codex CLI work in tasks. A developer asks for a change, and the agent reads files, edits code, and runs tests until the work is done. One task can take a few model calls or dozens. What the developer pays for is the task, not one call.
The price per token alone doesn’t tell you what a whole task costs. The total depends on how many turns a model needs, how much history each turn replays, and how often a task has to be retried or never finishes. You pay for every attempt, including attempts on tasks that never finish. So when you route a coding agent’s requests between models, judge the routing by cost per solved task: the total cost of your runs divided by the number of runs whose tests pass.
NVIDIA NeMo Switchyard is an open source library that helps an AI agent choose which model handles each request. You define the models that it can use and the strategy that it follows. Its LiteLLM integration supports two strategies. Stage routing reads the recent tool activity and results in the request history, and then picks one of two candidate models. Random routing picks from the configured models, with optional weights, for traffic splits and A/B comparisons. Another option you can explore is LiteLLM, who also recently shipped a native built-in Switchyard stage router as an auto-router strategy.
In this post, you use the AWS Cloud Development Kit (AWS CDK) to deploy LiteLLM, an open source gateway for large language models (LLMs), with the Switchyard routing plugin in your AWS account. LiteLLM calls the models on Amazon Bedrock. You then use the sample’s benchmark runners to measure cost per solved task for a routed model group and for single-model baseline groups, so that you can evaluate routing against your own workloads.
Source: AWS Machine Learning Blog — Read the full article on the original site ↗