When to use Opus vs Sonnet vs Haiku ⚡
Master this high-ROI AI workflow: The mistake most people make is pouring Opus for every cup. This guide shows you when to use Opus vs Sonnet vs Haiku.
⚡ TL;DR
- The mistake most people make is pouring Opus for every cup.
- This guide shows you when to use Opus vs Sonnet vs Haiku.
The mistake most people make: pouring Opus for every cup. This guide fixes that.
The decision tree
Three questions. Ask in order. Stop at the first "yes."
1. Is this strategic?
"Will the answer shape a real decision: pricing, positioning, hiring, money, a public commitment?"
- ✅ Yes → Use Opus. The stakes justify premium reasoning.
- ⬇️ No → Continue to Q2.
2. Does the wording matter?
"Will a human read this exact output? Client copy, published content, an important email?"
- ✅ Yes → Use Sonnet. The everyday workhorse, strong at language, drafting, judgment.
- ⬇️ No → Continue to Q3.
3. Is speed or volume the priority?
"Do I want this to feel instant? Is the task mechanical: formatting, extracting, tagging?"
- ✅ Yes → Use Haiku. Fast, cheap, accurate enough for routine work.
- ⬇️ No → Default to Sonnet.
12 worked examples
The right model for each, plus a one-line "why."
| # | Task | Pick | Why |
|---|---|---|---|
| 1 | Drafting an Instagram caption | Sonnet | Voice and word choice matter; Haiku reads flat. |
| 2 | Outlining a 6-month content strategy | Opus | Multi-variable, strategic, hard to redo if wrong. |
| 3 | Reformatting a list into clean bullet points | Haiku | Mechanical. Opus here = waste of money. |
| 4 | Summarising 10 articles into one digest | Sonnet | Synthesis needs nuance, but not deep reasoning. |
| 5 | Competitive teardown of 3 brands' positioning | Opus | Strategic call you'll act on. Worth the cents. |
| 6 | Quick fact lookup ("when did X launch?") | Haiku | One-shot, factual, no judgment needed. |
| 7 | Reviewing a contract clause for risk | Opus | High-stakes, ambiguity-heavy, real consequences. |
| 8 | Tagging customer feedback by theme | Haiku | Repetitive classification, Haiku nails it at scale. |
| 9 | Architecting a new automation system | Opus | Design decisions compound; hard to undo later. |
| 10 | Writing a small utility function | Sonnet | Common code, but you want it correct first try. |
| 11 | Daily email inbox triage / summary | Haiku | Volume + speed beat depth. |
| 12 | Brainstorming product names | Sonnet | Creative range matters; Haiku is too literal. |
What this actually costs
Current API prices (per 1M tokens, April 2026)
| Model | Input | Output |
|---|---|---|
| Haiku 4.5 | $1 | $5 |
| Sonnet 4.6 | $3 | $15 |
| Opus 4.7 | $5 | $25 |
Plain-English calculator
Assume "1 hour of active use" ≈ 30k input + 15k output tokens (typical chat with some context loaded).
| Daily use | Haiku | Sonnet | Opus |
|---|---|---|---|
| 15 min/day | ~$0.80/mo | ~$2.40/mo | ~$3.95/mo |
| 1 hour/day | ~$3.15/mo | ~$9.45/mo | ~$15.75/mo |
| 3 hours/day | ~$9.45/mo | ~$28.35/mo | ~$47.25/mo |
| Heavy (5+ hr/day, big docs) | ~$25/mo | ~$75/mo | ~$125/mo |
What this means
Tasks that almost always belong in Haiku
This is where most people overspend. Move these off Opus immediately.
- ✅ Summarising emails or Slack threads
- ✅ Reformatting lists, bullet points, JSON, CSVs
- ✅ Tagging or categorising (sentiment, topic, priority)
- ✅ Translating short snippets between languages
- ✅ Generating subject lines, alt text, meta descriptions
- ✅ Extracting structured data from a paragraph (names, dates, prices)
- ✅ Spelling, grammar, capitalisation fixes
- ✅ Quick definitions and fact lookups
- ✅ Title-case / sentence-case conversions
- ✅ Drafting routine messages (booking confirmations, RSVPs, intros)
- ✅ Estimating reading time or word counts
- ✅ Generating placeholder copy (lorem-ipsum-style filler)
- ✅ First-pass classification before a human reviewer
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