Reach for Haiku on high-volume, low-complexity jobs where speed and cost matter, Sonnet for the everyday middle where most production work lives, and Opus when a task genuinely needs the deepest reasoning. The trick is not picking one model forever but routing each job to the cheapest tier that still clears your quality bar.
Think in tiers, not favorites
When I set up a new project, I stop asking “which Claude model is best” and start asking “what is the smallest model that does this job well enough.” That reframing saves real money and keeps latency down. The three tiers exist because most workloads are not uniform: a support inbox, a coding agent, and a research summarizer all have different tolerance for cost, speed, and mistakes.
Map your tasks first, then assign a tier to each one. The assignment is rarely permanent, and that is fine.
Workflow one: high-volume classification and support
If you are tagging tickets, routing emails, moderating comments, or answering repetitive FAQs, Haiku is usually the right call. These jobs run thousands of times a day, the individual answers are short, and the reasoning is shallow. Paying for a heavier model here burns budget for no visible gain. In the support pipelines I have shipped, Haiku handled the bulk of first-pass triage and only escalated the genuinely ambiguous cases upward.
Set a confidence threshold. When Haiku is unsure, pass the item to Sonnet rather than forcing a weak answer.
Workflow two: everyday content, code, and analysis
Sonnet is the workhorse for a reason. Drafting documents, writing and reviewing typical code, summarizing meetings, and doing structured analysis all sit comfortably in its range, and it does them fast enough to feel interactive. For most teams, Sonnet ends up being the default that touches eighty percent of daily requests, with the other tiers handling the extremes. When I benchmarked a mixed workload, Sonnet cleared the quality bar on far more tasks than I expected before I ever needed Opus.
Workflow three: deep reasoning and agentic code
Some jobs earn Opus. Multi-file refactors, long-horizon agent runs, tricky debugging, dense research synthesis, and anything where a subtle logical slip is expensive are where the top tier pays for itself. The cost per call is higher, so I gate it: Opus runs when a task fails a Sonnet attempt, or when the stakes justify the spend from the start. Used deliberately, it is not extravagant, it is insurance on the hard problems.
Building a routing layer that switches for you
The cleanest setups do not make a human choose the model each time. Instead they route by task type, input length, or a quick complexity check, then fall back upward when a cheaper model returns low confidence. Start simple: a rules table that sends known job types to a default tier, plus an escalation path. You can get sophisticated later once you have logs showing where the cheap tier actually falls short.
Log the model, the cost, and whether the output was accepted. Those three fields tell you within a week where your routing is wrong.
Switching tiers without breaking your app
Because the three models share a family and API shape, moving a task from Sonnet to Haiku or up to Opus is mostly a config change, not a rewrite. Keep your prompts model-agnostic where you can, and test each prompt against the tier you plan to use, since a prompt tuned for Opus sometimes needs more explicit instructions to work well on Haiku. For the full tier-by-tier breakdown of capabilities and pricing, this this Claude Opus, Sonnet and Haiku comparison comparison lays out where each one earns its place. Re-run your own representative prompts after any switch, because your prompts predict your results far better than a generic chart.
A repeatable way to assign models this week
List your recurring AI tasks, estimate the volume and the cost of a wrong answer for each, and start every task on Sonnet. Push the cheap, high-volume, low-stakes ones down to Haiku and confirm quality holds. Push the rare, high-stakes, reasoning-heavy ones up to Opus. Watch your logs for a week and adjust. That loop gets you a cost-efficient setup without guessing.
Frequently asked questions
Can I mix all three models in one application?
Yes, and most efficient setups do. A common pattern uses Haiku for cheap classification, Sonnet for the main work, and Opus for the occasional hard case. Because they share an API shape, wiring a router that picks a tier per request is straightforward, and it usually cuts cost while keeping quality where it matters.
How do I know when Sonnet is not enough?
Watch for tasks where Sonnet’s output needs frequent human correction, stalls on multi-step reasoning, or loses the thread on long agent runs. Those are your Opus candidates. Rather than guessing, run the failing cases through Opus and compare accepted-answer rates. If Opus clearly clears a bar Sonnet keeps missing, the extra cost is justified.
Is Haiku accurate enough for customer-facing replies?
For scoped, repetitive replies with clear patterns, often yes. The risk is edge cases where shallow reasoning produces a confident wrong answer. Mitigate it with a confidence threshold that escalates uncertain items to Sonnet, plus a review step for anything sensitive. Used inside those guardrails, Haiku handles a large share of routine customer messages well.
Will switching models change my prompts?
Sometimes. A prompt tuned for the strongest model may assume reasoning that a lighter tier needs spelled out. When moving down a tier, add explicit steps and examples; when moving up, you can often simplify. Always test the prompt against the specific model you intend to deploy rather than assuming it transfers unchanged.
Does routing add much engineering overhead?
Less than people expect. A basic router is a lookup table mapping task types to tiers plus an escalation rule for low-confidence results. The bigger effort is logging outcomes so you can tune the routing over time. Start with static rules, add data-driven escalation once you see where the cheap tier underperforms.
What to do next
Pick your three most frequent AI tasks, assign each a starting tier, and instrument the calls so you can see cost and accept rates. Let the data move tasks up or down over the first week. The goal is a workflow where Haiku, Sonnet, and Opus each carry the work they are suited to, so you are never overpaying for simple jobs or under-powering the hard ones.
By Daniel Okoro, backend engineer who builds LLM routing systems for production apps. Last updated July 2026.