When most professionals open Claude, they reach for the most powerful model available — every single time. It feels like the safe choice. In practice, it is one of the costliest habits you can develop, because more capable models consume a proportionally larger share of your daily usage limit.
Consider a simple prompt: "Draft a 200-word internal email confirming a meeting time." All three models can handle this comfortably. But the token cost is not the same.
Using Opus for a task Haiku can handle comfortably is the equivalent of hiring a senior partner to send a calendar invite. The output looks the same — the cost does not.
Note on pricing: the figures above reflect API usage for developers building software. Subscription plan limits work differently — but the relative cost ratios between models remain the same. Opus still depletes your plan allowance roughly five times faster than Haiku for an equivalent task.
Choosing the right model is not about capability — all three models are competent. It is about proportionality: deploying a level of compute that matches what the task genuinely requires.
Matching the model to the task is one of the easiest efficiency gains available to you. Here is a practical reference for the most common professional scenarios.
| Task type | Examples | Best model |
|---|---|---|
| High-volume, repetitive drafting | Product descriptions, social captions, email templates, FAQ answers | Haiku |
| Summarisation & classification | Summarising meeting notes, categorising support tickets, tagging content | Haiku |
| Standard professional writing | Client emails, internal memos, project briefs, slide copy | Sonnet |
| Analysis & research synthesis | Market analysis, competitor reviews, policy summaries, interview synthesis | Sonnet |
| Complex reasoning & strategy | M&A briefings, legal analysis, risk frameworks, executive strategy documents | Opus |
| High-stakes, client-facing output | Board papers, investor memos, expert reports, regulatory submissions | Opus |
Models evolve fast. New versions are released regularly, capabilities shift, and what is current today may not be current in six months. This course reflects the model landscape at the time of publication — always check the latest PairWorkflows guidance for up-to-date recommendations before making model decisions at scale.
There is a cost dimension to Claude usage that most professionals overlook entirely: the longer a conversation runs, the more expensive each individual message becomes — even if you are asking the exact same question.
The reason is structural. Claude does not maintain a running memory of your conversation the way a human would. Instead, every time you send a new message, the entire conversation history is re-sent to the model from the beginning. Prompt 1 carries no history. Prompt 30 carries every message, every response, and every document you have pasted in since the session opened.
The practical implication: a sprawling all-day conversation — starting with a document review, moving into rewrites, then pivoting to a strategy question — accumulates a very heavy context load. By the end of the day, even a short question is expensive, because it is towing the weight of everything that came before it.
The takeaway: long conversations are not just slower — they are heavier. Starting a fresh, focused conversation for each distinct task is not starting over on your work. It is one of the smartest efficiency moves available to you. Focused beats sprawling, every time.
Quick maths: one session — a 10-page document, 15 exchanges, three full rewrites — can burn through more of your session budget than 10 focused, single-purpose conversations combined.