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Choose the least costly, fastest model that meets a measured quality bar for each task—not the most capable model by default. Start with a capable baseline, test smaller or faster candidates on representative work, then route tasks according to results. Keep one model when work is uniform or tightly dependent; add an advisor for occasional hard decisions or an orchestrator when independent tasks genuinely benefit from delegation.
What an executor does—and why the choice matters
An executor is the model that performs a particular step in an agent workflow: extracting fields, editing code, deciding whether a case needs escalation, or summarizing delegated work. A plan may use one executor throughout or assign different models to different steps. The goal is not to maximize model capability at every step; it is to meet each step’s quality and reliability requirements while accounting for cost and latency across the whole task.
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Official guidance points to task complexity, expected performance and latency, inference budget, and the need for human involvement as inputs to this decision. Google Cloud also cautions that a predictable, structured task that fits in one model call may be more cost-effective without an agent architecture at all. Google Cloud’s design-pattern guide was last reviewed on 2026-05-28 UTC.
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Describe the task classes
Break the workflow into distinct kinds of work rather than treating every step as interchangeable. For each class, record the expected input and context size, available tools, what counts as an acceptable result, the consequence of failure, and whether a person must review the output. Include latency expectations and the budget available for a successful completion.
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For example, routine extraction from a known document format may have a clear pass/fail threshold, while interpreting an ambiguous exception may require stronger reasoning and human approval. Those should not automatically share the same executor simply because they appear in one plan.
Measure a capable-model baseline
Build an evaluation set that represents the real range of inputs, including routine cases and difficult or unusual ones. Run a capable candidate against it and record task success, quality, latency, and token use. Keep prompts, tools, and evaluation conditions consistent when comparing models; otherwise, a change in setup can be mistaken for a model improvement.
OpenAI recommends selecting a model based on representative workload performance and comparing task success, latency, input, output, reasoning, and cache-write tokens, then calculating cost per successful task. Its API deployment checklist also advises evaluating reasoning effort against the needs of the workload.
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Test smaller and faster alternatives
Try less capable or faster candidates on the same evaluation set, including different reasoning settings where available. Keep a candidate for a task class only if it clears the predeclared quality threshold. OpenAI describes its model-selection suggestions as a starting point: its guide characterizes Luna as efficient for scoped tasks, triage, and frequent automations; GPT-6.1 Sol for complex work where cost matters; and Astra for ambiguous or demanding analysis. These recommendations and available models, tools, reasoning settings, and usage limits can vary by product and version, so check the current OpenAI model-selection guide and applicable catalog before making a product-specific choice.
Compare the whole task, not just token prices
A cheaper model can become more expensive in practice if it needs retries, uses more reasoning tokens, or fails to recognize when it is stuck. A stronger model can also add needless cost when routine steps do not benefit from its additional capability. Compare candidates using the complete workflow, with a consistent evaluation setup.
- Quality: Does the result meet the threshold for this task class, across both ordinary and difficult examples?
- Latency: How long does the step take, including router, consultation, or orchestration calls on the critical path?
- Cost per successful task: Include input, output, reasoning, and cache-write tokens, as well as retries and calls to other models.
- Reliability: Does the executor handle variation consistently, and can it recognize when it needs help?
- Compatibility: Does the model support the required tools, context, reasoning settings, and provider environment?
- Human review: Does the task’s risk or subjectivity require approval regardless of which model executes it?
OpenAI’s guide puts the principle plainly: “The best way to find the right model for your workflow is to experiment with different models and reasoning settings to see what works.” Treat that as a call to measure your workload, not as a universal ranking of models.
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Choose the control-flow pattern that fits the work
| Pattern | Best fit | Main trade-off |
|---|---|---|
| One executor | Uniform task difficulty or a dependent chain where each step relies on the previous one | Simpler control flow; may spend more than necessary on routine steps or fall short on unusually hard ones |
| Advisor | A mostly serial workflow with occasional decisions that need stronger judgment | Consultation adds cost and potentially latency; a weaker executor may fail to notice that it should ask for help |
| Orchestrator | Independent subtasks that can be delegated and later combined | Planning, dispatch, and synthesis add calls, cost, and coordination overhead |
Use one executor for uniform or dependent work
If every step has similar difficulty, or later steps depend closely on earlier ones, a single well-tuned model is often the sensible choice. Anthropic recommends this simpler design when a workload lacks a meaningful mix of task difficulty or consists of one dependent chain. It also avoids adding coordination machinery where it cannot create much value. Anthropic’s cost-and-intelligence guide discusses this trade-off alongside multi-model patterns.
Use an advisor for occasional hard decisions
In an advisor pattern, a smaller executor handles the main loop and consults a stronger model when it encounters a difficult decision or a recovery problem. This can preserve a fast, economical routine path without asking the stronger model to perform every step. It works only if the executor can identify when to escalate; a low-effort or poorly configured executor may miss that signal. Measure how often advice is requested, whether the answer changes the outcome, and the added cost and delay.
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Use an orchestrator for real parallel work
An orchestrator uses a stronger model to plan and distribute work, then synthesize the results. This is useful when independent files, documents, or cases can be handled separately and the combined result benefits from coordination. It is not automatically faster or cheaper: extra planning, dispatch, and synthesis calls add overhead. Compare the end-to-end outcome with a simpler design to confirm decomposition earns its place.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make routing explicit and repeatable
When a particular agent consistently needs a distinct quality, latency, or cost profile, configure its model explicitly. The OpenAI Agents SDK supports model selection per agent, per run, or as a process-wide default; its models and providers guide explains these options. Explicit choices also avoid silently inheriting whichever default happens to ship with an SDK version.
For routing decisions that should be reproducible, code-based orchestration can be more deterministic and predictable in speed, cost, and performance than asking an LLM to make every routing choice. The OpenAI Agents SDK orchestration guide covers specialized agents, code-based control, monitoring, iteration, and evals. Dynamic judgment still has a role when the task genuinely depends on context; the important distinction is to use it where it adds value rather than making every route an unmeasured model decision.
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Model assignments are not a one-time decision. Log which route ran, whether the task succeeded, latency, token use, escalations, and retries. Review results by task class so a good average does not hide failures in a high-risk category. Re-run evaluations when prompts, tools, models, workload mix, or budgets change, and adjust the routing policy based on measured outcomes.
Google Cloud similarly emphasizes revisiting design choices as requirements and performance evolve. Its guidance identifies complexity, latency, cost, and human involvement as design inputs—not fixed attributes of a particular workflow. Google Cloud’s agentic-system design guide describes these trade-offs.
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