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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteAssign each task to the least costly model that reliably meets its quality bar—not automatically to the strongest model available. Start by measuring a capable-model baseline on representative work, then test smaller or faster candidates under the same conditions. Keep one executor when work is uniform or tightly dependent; add an advisor or orchestrator only when the workflow benefits from those extra calls.
Start with the work, not the model catalog
Before choosing models, break the workflow into task classes and define what a passing result means for each. A routine extraction step and an ambiguous technical decision may need different quality, latency, and review requirements. OpenAI’s model-selection guide describes scoped problem-solving and simple data extraction as a different fit from complex technical work and coordinated deliverables. Treat those as task examples, not fixed model assignments.
- Quality: What errors are acceptable, and which failures require escalation?
- Cost and latency: Set a budget and a response-time target, including any routing, consultation, or retry calls.
- Operating conditions: Record the tools, context size, reasoning settings, and provider constraints each step needs.
- Human involvement: Specify which outputs need review, especially for high-stakes, safety-critical, or subjective decisions.
Google Cloud’s agent-system design guidance likewise treats workload complexity, performance expectations, inference budget, and human involvement as architecture inputs. It also notes that predictable, structured work may be more cost-effective without an agent architecture.
Establish a baseline, then test cheaper candidates
- Build a representative evaluation set. Include ordinary cases, difficult cases, and known failure modes for each task class. Decide in advance what counts as acceptable quality.
- Run a capable baseline. Keep prompts, tools, inputs, and evaluation conditions consistent. A baseline shows what “good enough” looks like before you reduce capability.
- Compare smaller or faster options. Test them on the same examples, including relevant reasoning settings. Keep a candidate only if it clears that task’s quality threshold.
- Compare cost per successful task. Include retries, reasoning tokens, routing and consultation calls, and latency on the critical path—not just the listed price per token.
- Recheck the policy over time. Monitor outcomes and revisit the comparison when the workload, model catalog, or budget changes.
OpenAI’s deployment checklist recommends evaluating representative-task success alongside latency and input, output, reasoning, and cache-write token use, then calculating cost per successful task. Its practical guide to building agents also recommends establishing a capable baseline and trying smaller models against an acceptable-results standard. These steps help distinguish a genuinely cheaper route from one that saves on each attempt but needs more retries or produces more failures.
#1 Best Overall
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Choose the control-flow pattern that fits the work
| Workflow shape | When it fits | Trade-off to measure |
|---|---|---|
| One executor | Task difficulty is fairly uniform, or later steps depend closely on earlier steps. | Simpler coordination; a single model may be inefficient if routine and hard tasks differ sharply. |
| Advisor | A smaller executor handles a mostly serial workflow and occasionally needs help with a hard decision or recovery. | Consultation calls add cost and latency; the executor must recognize when it is stuck. |
| Orchestrator | Work can be divided into genuinely independent pieces, such as separate files, documents, or cases, then synthesized. | Planning, dispatch, and synthesis add coordination overhead, so decomposition must produce enough benefit. |
Keep one executor for uniform or dependent work
Multiple models are not automatically an improvement. Anthropic’s cost-and-intelligence guidance says that when difficulty is uniform or the workflow is one dependent chain, a single well-tuned model is usually preferable. Fewer handoffs can mean less orchestration complexity and fewer calls to pay for. A simpler approach is also appropriate when a task is predictable and can be completed in one model call.
Use an advisor for occasional hard decisions
In an advisor pattern, the smaller executor stays in the loop and escalates selected decisions to a stronger model. This can preserve a low-cost path for routine work while bringing additional capability to difficult cases. It only works if escalation happens when needed: lower effort may make an executor less likely to detect that it is stuck. Measure the frequency of consultations, their effect on quality, and their added latency and cost.
Rank #2
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Use an orchestrator for useful parallel work
An orchestrator can plan a task, delegate independent pieces, and synthesize the results. That is a better fit for true fan-out than for a sequence where each step needs the previous answer. Count the planner, delegation, and synthesis calls as part of the workflow; added coordination is worthwhile only if decomposition improves results or throughput enough to justify it.
Make model routing explicit and measurable
When a specialist consistently needs a distinct quality, latency, or cost profile, configure its model deliberately rather than relying on an SDK default. The OpenAI Agents SDK’s models and providers documentation describes setting a model per agent, at run level, or as a process-wide default. Check the applicable model catalog and product documentation before relying on a particular model, tool, or reasoning setting: availability and limits can vary by model version and product.
Rank #3
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For predictable routing, code-based rules can be easier to audit and reproduce than asking a model to decide every assignment. The OpenAI Agents SDK’s orchestration guide describes code-based orchestration as more deterministic and predictable in speed, cost, and performance, and recommends monitoring, iteration, and evals.
- Log the task class, chosen route, outcome, latency, and token use.
- Track escalations and retries so their cost is visible in the result.
- Evaluate quality by task class, not only as one overall average.
- Keep the route policy understandable enough to update when an evaluation shows a failure pattern.
A practical routing rule
Use a capable model as the initial benchmark, then assign a smaller or faster candidate to a task only after it meets that task’s quality bar on representative examples. Retain a single executor for uniform or dependent work. For mixed difficulty, test sparse advisor escalation; for independent work that benefits from planning and synthesis, test orchestration. In each case, decide from measured task success, end-to-end latency, and cost per successful result.
Quick Recap
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Rank #4
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