Claude Opus 5.5 and Claude Sonnet 5.5: Queue Budgets

01 · Choose a queue policy, not a model winner

For a team processing a steady backlog, choosing between Claude Opus 5.5 and Claude Sonnet 5.5 is an operating decision. The relevant question is not which model produces the most impressive demonstration. It is which assignment policy clears work at an acceptable quality, cost, and delay.

Start with a testable policy: evaluate Claude Sonnet 5.5 as the default worker and Claude Opus 5.5 as a candidate for difficult jobs or escalation. Treat that arrangement as a hypothesis, not an established performance ranking. Your own workload must show whether either model deserves those roles.

02 · Separate routine jobs from expensive mistakes

A production queue rarely contains one uniform task. Document extraction, code changes, incident summaries, and ambiguous investigations have different acceptance criteria. A single average score can hide a failure pattern that matters to the team owning the result.

• › Routine, verifiable work: Favor the model that passes explicit checks with the lowest total processing cost.
• › Ambiguous work: Compare both models on cases where requirements are incomplete or several answers appear plausible.
• › High-consequence work: Require appropriate validation or human approval regardless of which model generates the answer.

Define these classes before testing. Otherwise, leads can end up routing by intuition and discovering only later that easy jobs subsidized an unreliable tail.

03 · Run both models against the same acceptance checks

Build an evaluation set from representative queue items, including long inputs, malformed requests, difficult exceptions, and previously rejected outputs. Remove sensitive material where necessary. Keep prompts, tool access, output limits, and grading rules comparable so that configuration differences do not masquerade as model differences.

Measure first-pass acceptance, completion latency, token consumption, and the amount of human repair. For code tasks, run tests and inspect the change. For structured extraction, validate required fields and factual correspondence. For summaries, check omissions and unsupported statements.

Compare cost per accepted result, not cost per request alone. A seemingly economical response can become expensive after retries and review. Conversely, a more expensive call earns its place only when its additional value survives measurement.

04 · Budget for retries, output length, and cache behavior

Forecast spending from the queue’s actual token profile: input size, generated output, repeated context, and expected attempts per completed job. Track cache writes and cache reads separately rather than treating all reused context as free. Verify cache behavior under your actual request patterns.

Quanta fits teams that want pay-as-you-go access to both models through one balance. Calls are metered on real tokens, with cache writes and reads priced separately. That shared balance can simplify funding a comparison, but your application should still attribute usage by model, queue, and task class.

Keep output limits deliberate. Asking for an exhaustive explanation on every background job can consume budget without improving acceptance. Request the artifact the downstream system actually needs.

05 · Escalate on observable failure, not model confidence

If testing supports a two-tier policy, make escalation explicit. Failed schema validation, unresolved test failures, missing evidence, or a defined ambiguity flag can trigger another attempt. Do not rely solely on a model saying that it is confident.

Set a retry ceiling and a deadline for each job class. When a job exceeds either, send it to a review or failure queue rather than creating an unlimited chain of model calls. Preserve enough context for diagnosis without repeatedly resubmitting unnecessary material.

06 · Roll out with separate quality and capacity gates

Begin with shadow evaluation or a bounded production slice. Expand only after checking acceptance, spending, queue age, and latency under realistic concurrency. Confirm applicable limits before committing to a drain-time target; model choice alone does not guarantee throughput.

Open a Quanta account to run a bounded comparison using the same queue samples and acceptance checks. Keep routing in your application, record the outcome of each policy, and revisit the split as workload composition changes. The durable decision is a measured operating policy, not permanent loyalty to either model.