Claude Opus vs Sonnet: Which Model Should You Use? (2026)
Founder of Goodspeed
If you are building on Claude, one of the first practical decisions is which model to use. Opus and Sonnet are the two you will reach for most, and choosing between them is not about which is better in the abstract. It is about matching the model to the job so you get the quality you need without paying for capability you do not.
At Goodspeed we build production AI on Claude, and we run both of these models every day across client systems. The teams that get the best results rarely pick one and use it for everything. They understand what each is for and route work accordingly, which gives them strong quality where it counts and low cost everywhere else.
This guide gives you the short version, then goes deeper on capability, speed and cost, when to use each, and how to use both together through model routing so your system is smart and economical at the same time.
The short version
Opus is the most capable model in the Claude family, built for the hardest reasoning, the most complex code and the tasks where getting the answer exactly right is worth a premium. Sonnet is the balanced workhorse, fast and much cheaper, strong enough to handle the large majority of production tasks without breaking a sweat.
The rule of thumb is simple. Reach for Sonnet by default, and escalate to Opus when a task genuinely needs its extra depth. Most teams discover that Sonnet covers far more of their workload than they first assumed, and that Opus earns its place on a specific, valuable minority of tasks. Getting that split right is most of the game.
Capability
Opus sits at the top of the range for raw capability. It handles multi step reasoning, intricate code, nuanced judgement and ambiguous instructions with more depth and consistency than smaller models. When a task has many moving parts, or a wrong answer is expensive, that extra headroom is exactly what you are paying for.
Sonnet is not a weak model, and framing it that way misleads. It is highly capable in its own right and handles most real tasks cleanly, from drafting and summarising to a great deal of coding and analysis. The difference shows up at the hard end of the spectrum, where problems are genuinely complex or where the last few percent of accuracy really matters. For everything short of that, Sonnet's capability is more than sufficient.
Speed and latency
Sonnet is the faster of the two, which makes it the natural choice for anything a user is waiting on. In interactive products, chat interfaces, autocomplete, live assistance, responsiveness is part of the experience, and a snappy Sonnet response often feels better than a slower, marginally more polished one.
Opus, being larger and doing more work, is slower. For deep, offline or non interactive tasks that is a fair trade, because you care about the quality of the result more than the seconds it took. But for high frequency, latency sensitive paths, Sonnet's speed is a feature in itself. Matching model speed to the interaction pattern is as important as matching capability to the task.
Cost
Cost is where the choice becomes concrete. Opus is considerably more expensive per token than Sonnet, reflecting its greater capability. Used on every request, that premium adds up fast and can make a product uneconomical at scale. Used selectively on the tasks that need it, the same premium is money well spent.
Sonnet's lower price is what makes it the sensible default for high volume work. When you are processing thousands or millions of requests, the per token difference dominates your bill. The art is to spend Opus tokens only where they change the outcome, and let Sonnet carry the everyday load. That single discipline often separates an AI feature that is profitable from one that quietly loses money.
When to use Opus
Opus is the right call when the task is genuinely hard or the stakes are high. Complex code generation and refactoring across many files, intricate multi step reasoning, subtle analysis where nuance matters, and tasks where a mistake is costly all justify its capability. If you would want your most senior engineer or analyst on the problem, that is a signal for Opus.
It also shines as the escalation target in a routed system. When a cheaper model flags low confidence or a task turns out to be more involved than expected, handing it to Opus gives you a strong safety net. The mental model is to treat Opus as the specialist you bring in for the difficult cases, not the generalist you use for everything.
When to use Sonnet
Sonnet should be your default for the broad middle of your workload. Summarisation, extraction, classification, routing, drafting, question answering and a large share of coding tasks all sit comfortably within its capability. Because it is fast and affordable, it is ideal for anything high volume or latency sensitive, which is most of what a typical product does.
Starting with Sonnet and only escalating when you have a reason keeps your system lean. Many teams over specify, reaching for the top model out of caution, and pay heavily for capability they never use. Beginning with Sonnet and measuring where it genuinely falls short is a more disciplined and far cheaper way to build, and it usually reveals that Sonnet handles more than expected.
Quality where it counts, cost everywhere else
The reason this choice matters is that it lets you place quality precisely where it earns its keep. You do not have to accept a single quality level and a single price across your whole product. You can give the hardest, most valuable tasks your best model and run everything else on a fast, cheap one, getting the best of both.
This is the opposite of picking one model and hoping it is the right compromise. A single choice is always a compromise, too expensive for the easy work or not strong enough for the hard work. Splitting the workload removes the compromise. Each task gets the model that suits it, and your overall system is both better and cheaper than any one model could make it.
Using both together with model routing
Model routing is how you get both models working as a team. Instead of a single model for the whole product, you classify each incoming task and dispatch it to the right one. Simple, high volume work goes to Sonnet. Hard, high stakes work goes to Opus. The routing logic can be as simple as rules based on task type, or a fast model making the call.
A common and powerful pattern is a Sonnet first pass that either answers directly or, when it detects difficulty or low confidence, escalates to Opus. This gives you Sonnet's economics on the bulk of traffic and Opus's depth exactly when it is needed. It is the architecture we build into client systems, because it makes cost scale gently while keeping quality high on the tasks that matter.
Testing the split on real tasks
Do not guess where the boundary between the two models lies. Measure it. Take a representative set of your real tasks, run them through both models, and compare the outputs. You will usually find a clear line, tasks where Sonnet's answer is indistinguishable from Opus's, and tasks where Opus is clearly worth the premium.
That evaluation is what turns routing from a hunch into a decision. It tells you exactly which task types to send where, and it gives you a baseline to re check when new model versions ship. Without it you are either overspending on Opus for work Sonnet handles fine, or underserving hard tasks with a model that cannot quite manage them. The test removes the guesswork.
Keeping routing maintainable
Routing adds a little complexity, so keep it clean. Put the model choice behind a single layer in your code rather than scattering model names through the application. That way, adjusting the split, or adopting a new model version, is a change in one place rather than a hunt across the codebase.
Log which model handled each request alongside quality and cost, so you can see whether the split is still right as your traffic evolves. Task mixes shift, and a boundary that was correct at launch may need tuning later. Treating routing as a living part of the system, monitored and adjustable, is what keeps it delivering its benefit rather than silently drifting out of date.
Choosing between Opus and Sonnet
The decision is not Opus or Sonnet as a permanent allegiance. It is Opus and Sonnet, each doing the work it is best suited to. Default to Sonnet for speed, cost and the broad sweep of your workload. Escalate to Opus for the hard, high value tasks where its capability changes the outcome. Route between them deliberately and measure the boundary on your own data.
Done well, this gives you a system that feels premium where users notice and stays cheap where they do not. That combination is hard to beat, and it is only available to teams who understand that the two models are partners rather than competitors. Pick both, and let each play to its strengths.
Route the task to the right model
Opus and Sonnet are not rivals to choose between once and for all. They are two tools for two kinds of work. Sonnet is the fast, affordable workhorse that should carry most of your traffic. Opus is the specialist you bring in for the hardest, highest stakes tasks. Route between them based on real evaluations, keep the routing logic clean and monitored, and you get strong quality exactly where it matters and low cost everywhere else.
Most well built Claude systems use both, and getting the split right is one of the highest leverage decisions you will make. If you want a team that builds production AI on Claude, see our AI work, or book a free call with our Claude team.
Written By
Founder of Goodspeed