Haiku vs Sonnet vs Opus: When to Use Cheaper Models and When to Spend Up

One of the most common mistakes people make when building with AI is defaulting to the most powerful model for everything. It feels safe. You know the big model is capable, so why risk it with something smaller?

The problem is that approach gets expensive fast, and honestly it's usually overkill. A cheaper model can handle a lot more than people give it credit for, and knowing when to step up versus stay down is one of those skills that separates developers who build sustainable AI apps from the ones who panic when their API bill arrives.

Let's break this down practically.

Jump to Section

• How to think about model tiers
• Low complexity tasks: when to use Haiku
• Medium complexity tasks: when Sonnet is the sweet spot
• High complexity tasks: when Opus is worth it
• Real-world examples
• Cost considerations
• FAQ

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How to think about model tiers {#model-tiers}

Every major AI provider structures their models in roughly the same way: a small fast cheap model, a mid-tier capable model, and a large powerful expensive model. For Anthropic that's Haiku, Sonnet, and Opus. For OpenAI it's something like GPT-4o mini, GPT-4o, and o1 or o3 for the reasoning-heavy stuff.

The differences between tiers come down to a few things: reasoning depth, instruction following, handling of ambiguity, context retention over long conversations, and output quality on complex tasks.

The key insight is that most of the tasks in any production AI app don't actually need deep reasoning. They need speed, consistency, and acceptable quality. That's where the cheaper models earn their keep.

A useful mental model is to think of it in three buckets:

• Low complexity: predictable inputs, simple outputs, speed matters, cost matters
• Medium complexity: nuanced outputs needed, some reasoning required, quality matters but budget is real
• High complexity: deep reasoning, multi-step logic, creative or analytical work, quality is the only metric that counts

Low complexity tasks: when to use Haiku {#low-complexity}

Haiku (or whatever the smallest model is for your provider) is way more capable than most people assume. For a huge category of production tasks it's the right choice and using anything bigger is just burning money.

Good Haiku use cases:

• Classification and routing. Is this customer message a complaint, a refund request, or a general inquiry? Haiku will nail this. You don't need Opus to sort an email into a category.
• Simple extraction. Pull the date, the order number, the city name out of a block of text. Fast, cheap, accurate.
• Sentiment analysis. Positive, negative, or neutral? Done.
• Short templated outputs. Generate a one-line summary of a product. Fill in a template with extracted values. Translate a short string. These are Haiku jobs.
• Moderation and filtering. Does this user-submitted text contain anything problematic? Run it through Haiku first before doing anything else with it.
• Autocomplete suggestions. Completing a sentence or suggesting a next word as someone types.
• High-volume pipelines. If you're processing thousands of items and the task is straightforward, Haiku lets you do it without the cost killing your margin.

The general rule: if you could describe the task to a reasonably smart person in one sentence and they could do it in ten seconds without thinking hard, Haiku can probably handle it.

Medium complexity tasks: when Sonnet is the sweet spot {#medium-complexity}

Sonnet (or the mid-tier equivalent) is where most serious production work lives. It's significantly more capable than Haiku for tasks that require nuance, and it's significantly cheaper than Opus for tasks that don't require the absolute ceiling of reasoning ability.

Good Sonnet use cases:

• Writing and editing. Blog posts, emails, marketing copy, documentation. Sonnet produces genuinely good output here and the cost is manageable even at volume.
• Coding assistance. For most day-to-day coding tasks, Sonnet is excellent. Debugging, writing functions, explaining code, code review. You'd only reach for Opus when the architectural problem is unusually complex.
• Summarization of longer documents. Condensing a long report into key points, summarizing a meeting transcript, pulling the important stuff out of a dense document.
• Q&A over a knowledge base. Answering questions based on retrieved context. Sonnet handles this well as long as the reasoning isn't super deep.
• Multi-turn conversations. Customer support bots, chatbots, interactive assistants. Sonnet follows instructions well and maintains context.
• Structured data generation. Generating JSON, filling out structured schemas, extracting complex nested data from unstructured text.
• Moderate coding tasks. Writing API integrations, building components, debugging logic errors.

If you're building an app and you're not sure where to start, start with Sonnet. It's the model that's capable enough to produce real results without requiring you to justify the cost every time.

You can use the tokenizer tool on this site to check how many tokens your typical prompts are consuming, which is useful when you're trying to figure out actual per-request costs across model tiers.

High complexity tasks: when Opus is worth it {#high-complexity}

Opus (or whatever the top-tier model is) is where you go when the task genuinely demands it. Not when you want to feel safe. When you actually need the reasoning depth, creative range, or analytical capability that only the big model has.

Good Opus use cases:

• Complex multi-step reasoning. Problems where the model needs to hold a lot of context, reason through multiple dependencies, and arrive at a non-obvious conclusion.
• Sophisticated analysis. Deep research synthesis, comparing and contrasting complex ideas, identifying patterns across a large body of information.
• High-stakes writing. Executive communications, legal or compliance-adjacent drafting, anything where the quality of the output has real consequences.
• Architecture and planning. Designing a system, planning a complex project, thinking through tradeoffs at a high level.
• Difficult debugging. When you've got a gnarly bug you can't figure out and you need the model to reason carefully through the problem space.
• Novel creative work. Long-form fiction, complex narrative structures, creative work where you actually care about originality and quality.
• Ambiguous or underspecified problems. When the task itself isn't clear and the model needs to do a lot of inference to even understand what's being asked.

The honest test for whether something needs Opus: run it through Sonnet first. If the output is genuinely good enough, you didn't need Opus. If the output is missing something important, step up.

Real-world examples {#real-examples}

Here's how this plays out in a few actual scenarios.

E-commerce support bot. Use Haiku to classify incoming messages and route them. Use Sonnet to draft responses for moderate issues. Only escalate to Opus if a case is genuinely unusual and you need high-quality analysis before a human reviews it.

Content pipeline. Use Haiku to check incoming content for obvious problems. Use Sonnet to write drafts, do light editing, generate SEO metadata. Only use Opus for long-form pieces where quality is the primary metric and you can't afford mediocre output.

Developer tool. Use Haiku for autocomplete and quick inline suggestions. Use Sonnet for most coding tasks, function generation, and documentation. Use Opus for complex architectural questions or when a user is stuck on something genuinely hard.

Data processing pipeline. Haiku almost everywhere. You're extracting, classifying, tagging, formatting. Speed and cost are everything. Only involve Sonnet or Opus at the end when you need to generate something that a person will actually read and judge.

Cost considerations {#cost}

The cost difference between model tiers is not trivial. Haiku is often 10-20x cheaper per token than the largest models. If you're running any kind of volume, this matters a lot.

The right approach is to build your app with model selection as a first-class design decision, not an afterthought. For each step in your workflow, ask: what does this step actually need to produce? How much reasoning does that require? Can I test with a smaller model and only move up if the results aren't good enough?

Start cheap, step up when you have evidence you need to. Don't start expensive because it feels safer.

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FAQ {#faq}

How do I know if a task is too complex for a cheaper model? Run it. Seriously. Test the task with the smaller model and evaluate the output. If it's good enough, you're done. If it's not, move up. Don't guess.

Is Opus always better than Sonnet? For reasoning-heavy and complex tasks, yes. For lots of practical tasks in production apps, Sonnet produces output that's just as good for the purpose. Opus isn't always worth the cost premium.

Can I mix models within the same app? Yes, and you probably should. Route simple tasks to cheap models and complex tasks to expensive ones. Most frameworks make this easy to implement.

What about latency? Smaller models are faster, right? Yes, generally. Haiku is much faster than Opus. For user-facing features where response time matters, that's another reason to use the smallest model that gets the job done.

Does this apply to other AI providers, not just Anthropic? The same principle applies everywhere. OpenAI has GPT-4o mini vs GPT-4o vs o1. Google has Gemini Flash vs Gemini Pro. The tier logic is the same regardless of who you're using.

What if I'm just experimenting and don't have volume yet? Start with Sonnet for most things. It's the best all-around model for building and testing. Switch to Haiku for high-volume paths once you've validated the approach works.

How do I track which model I'm using for each task? Log it. Seriously, from day one log which model handled each request. You'll want that data when you're trying to optimize costs later.

Is there a rule of thumb for when to use each tier? Roughly: if the task is structured and predictable, Haiku. If the task requires nuance and quality but isn't deeply analytical, Sonnet. If the task requires real reasoning depth or the output has high stakes, Opus.

What about fine-tuned models? Fine-tuning is a whole separate conversation, but the same tier logic applies. A fine-tuned small model can sometimes outperform a general large model on a specific task, which is another reason not to default to the biggest model for everything.

Should I tell users which model generated their response? Up to you. Some products make this a feature (transparency about model usage). Others don't surface it at all. There's no universal right answer.