Claude API: Haiku vs Sonnet vs Opus Compared

Claude's model lineup — Haiku, Sonnet, and Opus — trades off speed, cost, and reasoning depth. Haiku is the fastest and cheapest, built for high-volume, low-latency tasks. Sonnet is the balanced default for most production apps, offering strong reasoning at a moderate price. Opus is the most capable model, best reserved for complex, high-stakes work where accuracy matters more than cost or speed.

If you're picking a model for a new integration, the short answer is: start with Sonnet unless you have a specific reason to use Haiku (cost-sensitive, high-throughput, simple tasks) or Opus (hard reasoning, long-form analysis, low tolerance for errors). The rest of this article breaks down exactly when each makes sense.

What differentiates the three models

Haiku: speed and cost efficiency

Haiku is optimized for throughput. It responds faster and costs significantly less per token than Sonnet or Opus. It handles straightforward tasks well — classification, short summaries, simple extraction, chat responses that don't require deep reasoning — but it will underperform on multi-step logic, nuanced writing, or tasks requiring careful judgment.

Use Haiku when:

• You're processing large volumes of requests (thousands per day) and cost per call matters
• Latency is user-facing and needs to feel instant
• The task is narrow and well-defined (tagging, routing, short Q&A)

Sonnet: the default for most apps

Sonnet sits in the middle and is where most production workloads land. It handles code generation, multi-turn conversations, document analysis, and moderately complex reasoning without the cost or latency of Opus. For the majority of SaaS features — chatbots, content generation, data extraction, coding assistants — Sonnet is the right starting point.

Use Sonnet when:

• You need solid reasoning but not frontier-level accuracy
• You're building a general-purpose feature (support bot, writing assistant, code helper)
• You want a good cost-to-quality ratio without manually benchmarking every option

Opus: maximum capability

Opus is the most capable model in the lineup and costs the most per token, with higher latency than Sonnet or Haiku. It's built for tasks where getting the answer right matters more than speed or cost: complex multi-step reasoning, detailed technical analysis, long documents requiring careful synthesis, or tasks with legal, financial, or medical implications.

Use Opus when:

• Mistakes are expensive (compliance, legal review, financial analysis)
• The task involves long chains of reasoning or ambiguous instructions
• You've tested Sonnet and it's not accurate enough for the use case

Cost and latency tradeoffs in practice

The pricing gap between the three models is substantial — Opus can cost several times more per token than Sonnet, and Sonnet several times more than Haiku. This matters most at scale. A feature that calls the API once per user session behaves very differently in cost terms than one that processes thousands of documents per hour.

A practical approach many teams use:

1. Prototype with Sonnet to establish a quality baseline
2. Test the same prompts against Haiku — if quality holds, switch and cut costs
3. Only escalate to Opus for the subset of requests that actually need it (e.g., route "hard" cases to Opus, keep everything else on Sonnet or Haiku)

This tiered routing pattern is common in production: a cheap model handles the first pass or triage, and a more expensive model only gets invoked when the task is flagged as complex. It requires your application logic to make that routing decision, but it can cut costs meaningfully without sacrificing quality on the cases that need it.

Choosing a model from the API

Regardless of which model you pick, the integration pattern is the same — you specify the model string in your request. Here's an example using SubToAPI, which exposes Claude through a standard HTTPS API with your own sub_live_... key:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Summarize this support ticket in two sentences."}
    ]
  }'

Swapping claude-sonnet for claude-haiku or claude-opus is the only change needed to test a different model against the same prompt — which makes it easy to benchmark quality and cost side by side before committing. See the quickstart and messages docs for full request and response formats.

A simple decision framework

Instead of guessing, run the same set of representative prompts through all three models and compare outputs manually. A few things to check:

• Does Haiku's answer actually drop in quality, or is it just as good for this specific task? Many simple tasks show no meaningful difference.
• Does Opus's answer justify the extra cost and latency, or does Sonnet already get it right? If Sonnet is correct 95% of the time, Opus might not be worth the premium for that feature.
• Is latency user-facing? If a human is waiting on the response, Haiku or Sonnet will usually feel better than Opus, even if Opus is slightly more accurate.

If you're building with streaming responses to keep perceived latency low regardless of model choice, the streaming docs cover how to implement that. And if your app needs the model to call external functions or fetch data mid-conversation, tool use works consistently across all three models — see the tools docs for details.

Pricing when running your own Claude-backed API

If you're exposing Claude to your own application or team, usage across Haiku, Sonnet, and Opus adds up differently depending on the mix of requests. SubToAPI's plans — Solo at €9, Team at €19/seat, and Scale at €49/seat — give every team member their own API key with usage visible per seat, so you can see which models are driving cost before you optimize. Check pricing for details, or start a free trial at signup.

questions

Is Sonnet always the right default? For most general-purpose features, yes. Start there, then move to Haiku if cost or latency becomes a bottleneck and quality holds up, or to Opus if accuracy on complex tasks is insufficient.

Can I mix models within one application? Yes, and it's common practice. Many apps route simple requests to Haiku, general tasks to Sonnet, and only escalate specific hard cases to Opus to control cost.

Does model choice affect how I integrate the API? No — the request format stays the same across models; you just change the model string. See the messages docs for the exact parameters.