Claude's Haiku, Sonnet and Opus Models: Which Tasks Suit Each?
Haiku suits high-volume work that needs speed and low cost, such as chatbots that answer repetitive questions or data classification. Sonnet is the main choice for everyday work that needs both speed and quality, such as writing content or helping review code. Opus suits complex, high-risk work such as analysing contracts or planning business strategy, where a higher cost is accepted in exchange for accuracy.
Many organisations are running into AI spending that exceeds budget because they chose the wrong model size for the task. According to a McKinsey report from July 2026, many organisations send simple jobs through frontier models priced for complex work, which inflates costs unnecessarily, and 93% of the organisations surveyed in May 2026 reported spending more on AI than they had budgeted (McKinsey, July 2026). Knowing which model fits which task is therefore not just a technical question but a direct matter of cost management.
Which tasks suit Haiku
Haiku is the fastest and cheapest model in the Claude family. It suits high-volume tasks that do not need deep reasoning, such as chatbots that handle first-line customer questions, sorting emails or support tickets into categories, and simple data extraction from large numbers of documents.
Businesses with a high volume of low-complexity requests, such as customer service centres or document screening systems, should always start with Haiku, because it has the lowest cost and the fastest response. If some tasks turn out to need more accuracy, move up to Sonnet only where it is needed.
Which tasks suit Sonnet
Sonnet is the balance point between speed, cost and reasoning ability. It is a good default model for most of an organisation's everyday work, such as writing marketing content, summarising reports, helping check and fix code, or doing basic data analysis.
From our direct experience running an AI for Marketing training for Pisona Group in Phuket in May 2026, most marketing teams found that Sonnet covered all of their content-writing work without needing a more expensive model, because the quality was sufficient for real use and costs were easier to control.
Which tasks suit Opus
Opus is the model with the deepest reasoning among those in general use. It suits work where a wrong answer carries high risk, such as analysing legal contracts, planning business strategy, or writing code that has to solve several steps at once.
Organisations should use Opus only for tasks that have been screened and confirmed as genuinely complex, not as the default for every task, because its cost per request is much higher than Sonnet's and Haiku's. Using Opus on simple tasks is one reason an organisation's AI spending balloons unnecessarily, as McKinsey notes above.
Frequently Asked Questions
Which model should I start with if I have never used Claude before? Always start with Sonnet, because it covers most tasks well at a reasonable price. Then test which tasks should move to Haiku to cut costs, or to Opus for more accuracy.
Is the only difference between Haiku and Sonnet speed? Not just speed. Sonnet reasons more deeply and handles complex context better than Haiku. It suits tasks that involve judgement or analysis, while Haiku suits tasks with a clear, repeatable pattern.
Would it be better to use Opus all the time, since it is the smartest? Not necessarily. The cost per request is much higher, and if Sonnet already produces a similar result for a task, using Opus means paying more than you need to.
How many models should a small organisation use? For most, Sonnet alone is enough to start. Add Haiku when there is high-volume work where you need to control costs, or add Opus when you meet genuinely complex tasks.
Is there a way to check whether I have chosen the right model for a task? Test real sample tasks on several models and compare the results against the cost. If a cheaper model gives a similar result, always choose the cheaper one.
Author: Tanyalak Techapeompol Managing Director of Custocare, an AI and Digital Transformation consultancy with 17 years of IT experience LinkedIn
Published: 18/08/2026 | Last updated: 18/08/2026
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