Claude API Model Comparison: Haiku vs Sonnet vs Opus

Choosing between Haiku, Sonnet, and Opus comes down to one tradeoff repeated across three dimensions: speed, cost, and reasoning depth. Haiku is the fastest and cheapest, built for high-volume, low-latency tasks. Opus is the most capable, built for complex reasoning where accuracy matters more than response time. Sonnet sits in between and is the default choice for most production applications because it balances both well.

If you just need a quick answer — use Sonnet for general app logic, Haiku for classification/extraction at scale, and Opus for the hardest reasoning or long-context tasks where mistakes are expensive. The rest of this article breaks down why, with concrete scenarios and code.

The three models at a glance

| Model | Best for | Relative speed | Relative cost | Reasoning depth | |---|---|---|---|---| | Claude Haiku | High-volume, simple tasks | Fastest | Lowest | Basic–moderate | | Claude Sonnet | General-purpose apps | Fast | Moderate | Strong | | Claude Opus | Complex, high-stakes tasks | Slowest | Highest | Deepest |

These aren't marketing labels — they reflect real architectural tradeoffs. Smaller models respond faster and cost less per token because they do less computation per request. Larger models spend more compute reasoning through a problem, which shows up as both higher latency and higher cost, but also better handling of ambiguity, multi-step logic, and nuanced instructions.

When Haiku is the right call

Haiku shines when you're running the same type of request thousands of times a day and the task itself is narrow:

• Sentiment classification on support tickets
• Extracting structured fields from short text (names, dates, categories)
• Moderation checks before content goes live
• Autocomplete or quick suggestions in a UI
• Summarizing short snippets (a few sentences to a paragraph)

The pattern here is repetitive, bounded, low-ambiguity work. If a human could do the task correctly in under five seconds without thinking hard, Haiku is probably fine, and you'll save significantly on both cost and latency compared to using a bigger model for the same job.

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-haiku",
    "max_tokens": 100,
    "messages": [
      {"role": "user", "content": "Classify sentiment as positive, negative, or neutral: '\''The delivery was late again.'\''"}
    ]
  }'

When Sonnet is the right call

Sonnet is the model most teams should reach for by default. It handles multi-step reasoning, longer documents, and nuanced instructions well, without the latency and cost of Opus.

Good fits:

• Chat assistants handling varied user questions
• Code generation and review for typical features
• Document summarization where the source is a few pages, not a full book
• Tool-calling workflows where the model needs to pick the right function and format arguments correctly
• Drafting content that needs to sound coherent and on-brand

In practice, Sonnet is where most applications should start. Only drop to Haiku once you've confirmed a specific task doesn't need Sonnet's reasoning, and only move up to Opus once you've confirmed Sonnet isn't accurate enough for a specific task.

const response = await fetch("https://api.subtoapi.app/v1/messages", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "claude-sonnet",
    max_tokens: 1024,
    messages: [
      { role: "user", content: "Review this function for bugs and suggest a fix." }
    ]
  })
});

When Opus is the right call

Opus costs the most and responds the slowest, so it should be reserved for tasks where getting the answer wrong is more expensive than waiting a few extra seconds.

Good fits:

• Legal or financial document analysis where errors have real consequences
• Complex multi-step agentic workflows involving several tool calls chained together
• Research synthesis across long, dense source material
• Architecture decisions or code review on critical systems
• Any task where you've tested Sonnet and found its outputs inconsistent or shallow

A common mistake is defaulting to Opus everywhere "to be safe." This inflates costs without necessarily improving outcomes — many tasks don't benefit from deeper reasoning, they just need a correct, fast answer. Benchmark your specific use case before committing to the most expensive model by default.

A practical way to decide

1. Start with Sonnet. It's the safest default for unknown or mixed workloads.
2. Measure accuracy and cost per request type. If a task type consistently gets correct answers from Haiku, move it down.
3. Isolate the hard cases. If a subset of requests involves deep reasoning (long chains of tool calls, ambiguous instructions, high-stakes output), route those specifically to Opus.
4. Re-test periodically. Model behavior and pricing change; what justified Opus six months ago might not today.

This kind of tiered routing — sending simple requests to Haiku, general requests to Sonnet, and hard requests to Opus — is one of the most effective ways to control API spend without sacrificing quality where it matters.

If you're already running Claude access through an API layer, switching models per request is just a matter of changing the model field in your request body — see the docs and quickstart for the full request format, or streaming docs if you're piping responses to a UI in real time. SubToAPI turns your Claude access into a standard HTTPS API with application keys (sub_live_...), so you can experiment with model tiers across different parts of your app without touching infrastructure. Plans start at €9/month with a free trial — see pricing.

Questions

Is Opus always more accurate than Sonnet? Not for every task. Opus tends to outperform on complex, multi-step, or ambiguous problems, but for straightforward tasks the two often produce comparable results, making Sonnet the better cost-to-accuracy tradeoff in those cases.

Can I switch models per request instead of committing to one for my whole app? Yes. Most APIs, including SubToAPI, let you specify the model per request via the model field, so you can route different endpoints or request types to different models — see /docs/messages for the request schema.

Does a bigger model always mean slower responses? Generally yes — Opus has higher latency than Sonnet, which has higher latency than Haiku, due to the additional reasoning compute involved. If latency is critical, test Haiku or Sonnet first before assuming you need Opus.