When is an extra round of AI review worth its cost?

Research shows a trade-off, not a universal discount

FrugalGPT (2023), Table 3, reports 98.3% cost savings on HEADLINES while matching GPT-4 accuracy. It learned cascades of up to three models using a random train/test split. This historical task-specific result concerns selective escalation, not multi-agent discussion. FrugalGPT, 2023, Table 3

RouteLLM v4 (2025), Tables 1 and 6, reports a 3.66 cost-saving ratio on MT Bench: score 8.8 versus GPT-4’s 9.3. Routing used GPT-4-1106-preview and Mixtral-8x7B with historical token-price assumptions. The reported 95% is relative benchmark score, not correctness. RouteLLM, ICLR 2025, Tables 1 and 6

These are different interventions from asking every participant to answer and then debate. For your workflow, compare alternatives separately: one answer, one answer with a targeted check, selective escalation, and a full discussion. Otherwise a benefit from choosing a cheaper model can be mistakenly credited to collaboration, or a useful review can be dismissed because its first response costs more.

Build a small decision model

Our illustrative model is total expected cost = execution cost + error probability × consequence of an error. It deliberately leaves out many real details so the break-even condition is visible. The numbers below are invented teaching inputs, expressed in dollars for arithmetic. They are not Colay prices, measured Colay error rates, or estimates for your work.

Assume a single-answer workflow costs $0.02 and has a 12% error probability. Assume a reviewed workflow costs $0.08 and has an 8% error probability. If an error causes $5 of rework, expected costs are $0.02 + 0.12 × $5 = $0.62 and $0.08 + 0.08 × $5 = $0.48. Under these assumptions, review saves $0.14 per task.

The extra execution cost is $0.06 and the assumed error reduction is 0.04. Break-even rework cost is therefore $0.06 ÷ 0.04 = $1.50 per error. Below that threshold, the cheaper workflow wins in this simplified model. If review does not reduce errors at all, this calculation provides no error-prevention benefit to pay for it.

Replace invented inputs with observations

The hardest number is usually the change in error probability. Do not infer it from how persuasive the revised answer sounds. Run both workflows on the same representative tasks, score the final results without revealing the workflow, and record corrections that someone actually had to make. Keep uncertain cases visible rather than silently counting them as successes.

Estimate consequences by task category. A wrong heading in a private draft and a wrong quantity in an operational plan require different repairs. Record reviewer time, waiting time, repeated attempts, and downstream correction effort. Some consequences should be handled through explicit constraints and expert review rather than compressed into one speculative monetary number.

Spend review effort where it can change the result

Before launching a discussion, name an unresolved issue. For a proposal, it might be a missing dependency. For a calculation, it might be a unit conversion. Give the additional participant a check that can change the decision and ask what evidence would justify that change. A request to make the answer better is much harder to evaluate.

Set a stopping rule in advance: finish when the stated acceptance checks pass, escalate to a person when an important disagreement remains, and stop repeating equivalent arguments. A response can become longer without becoming more useful. Track accepted deliverables per unit of budget alongside response quality, so presentation polish does not absorb the whole allowance.

Apply the calculation to Colay usage

Colay Consensus has agents discuss a request before a coordinating agent synthesizes the conclusion. Colay subscriptions use credits and limits. Measure the actual credits consumed by your chosen models and workflow; do not translate the hypothetical dollar example into a promised number of Colay messages. Include unsuccessful attempts when estimating a normal week.

Keep a simple weekly ledger: task type, selected workflow, credits spent, human correction time, and whether the output met its requirements. After enough comparable examples, reserve discussion for categories where it earns its extra effort. Recheck that choice when models, tasks, or subscription terms change. The result is a practical spending rule rather than a claim that more agents are always cheaper or always better.