Claude Model Comparison: Haiku vs Sonnet vs Opus

If you're building on the Claude API, the first real decision isn't your prompt — it's which model to call. Haiku, Sonnet, and Opus trade off speed, cost, and reasoning quality in ways that directly affect your latency budget and your bill. This guide breaks down the practical differences and gives you a decision framework instead of marketing copy.

The short answer: Haiku is for high-volume, low-latency tasks like classification, extraction, and simple chat. Sonnet is the default workhorse for most production apps — strong reasoning at a reasonable price. Opus is for the hardest tasks — complex multi-step reasoning, long-document synthesis, nuanced writing — where you're willing to pay more and wait longer for better output.

The three models at a glance

| Model | Best for | Relative speed | Relative cost | Context handling | |---|---|---|---|---| | Haiku | Classification, tagging, short replies, autocomplete | Fastest | Lowest | Good, but shallower reasoning on long inputs | | Sonnet | General chat, coding assistants, summarization, agents | Fast | Mid | Strong balance of depth and speed | | Opus | Complex reasoning, research synthesis, hard coding problems | Slowest | Highest | Best at holding and reasoning over large context |

These aren't fixed numbers because Anthropic iterates on model versions, but the relative ordering has held consistent across generations: Haiku optimizes for throughput, Opus optimizes for capability, Sonnet sits in between and is where most teams end up shipping.

When Haiku is the right call

Haiku shines when:

• You're calling the API thousands of times per day and latency compounds (e.g., real-time chat suggestions, live moderation)
• The task is narrow: sentiment tagging, intent classification, short-form extraction, simple Q&A against a known FAQ
• You've already done the hard reasoning elsewhere (search, retrieval, rules) and just need the model to format or summarize a small, well-structured input

A common mistake is using a bigger model "just to be safe" for tasks Haiku handles fine. If your prompt is short, your expected output is short, and the task doesn't require multi-step reasoning, test Haiku first. You'll often get comparable accuracy at a fraction of the cost and latency.

When Sonnet is the right call

Sonnet is the default for a reason. It handles:

• Multi-turn conversations where context and tone matter
• Code generation and debugging for typical application logic
• Summarizing documents of moderate length
• Agentic workflows with tool use where the model needs to plan a few steps ahead

If you're not sure which model to pick, start with Sonnet, measure actual output quality against your task, and only move to Opus if you hit a ceiling, or down to Haiku if you find Sonnet is overkill for a specific endpoint.

When Opus is worth the cost

Opus earns its price on tasks where mistakes are expensive or where the task genuinely requires deep reasoning:

• Legal or financial document analysis with many interacting clauses
• Research tasks that synthesize information across long, dense inputs
• Difficult coding problems involving unfamiliar codebases or subtle bugs
• Creative or technical writing where nuance and coherence across a long output matter

Opus is slower and costs more per token, so it's rarely the right choice for high-frequency, low-stakes calls. Reserve it for the subset of requests where quality is the binding constraint, not speed or cost.

A practical way to decide: route by task, not by app

Most production systems don't pick one model for the whole app — they route per request type. A support bot might use Haiku to classify incoming tickets, Sonnet to draft the reply, and Opus only when the ticket is flagged as complex or escalated. This keeps average cost and latency low while reserving your best model for the cases that need it.

A simple routing pattern looks like this:

function pickModel(task) {
  if (task.type === "classify" || task.type === "extract") {
    return "claude-haiku";
  }
  if (task.type === "escalated" || task.length > 8000) {
    return "claude-opus";
  }
  return "claude-sonnet";
}

You then pass that model name into whichever client you're using to call the Messages API.

Where SubToAPI fits into this decision

If you already have Claude access and want to expose it as a proper API across your team or product, SubToAPI turns that access into a clean HTTPS endpoint with application API keys (sub_live_...), streaming, tool use, and usage metadata — so you can route between Haiku, Sonnet, and Opus per request without managing separate credentials or billing relationships for each.

A basic call looks like:

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 ticket."}]
  }'

Because usage metadata comes back with every response, you can track per-model cost and latency in your own dashboard and tune your routing logic over time instead of guessing. See the quickstart and Messages API docs for the full request format, or streaming docs if you're building a chat UI that needs tokens as they arrive. Plans start at the Solo tier for individual developers and scale to team seats for larger projects.

Testing before you commit

Don't guess — run the same representative prompts through all three models and compare:

1. Output quality: Does Haiku's answer actually miss nuance that Sonnet catches?
2. Latency: Measure real round-trip time under your actual load, not a single test call.
3. Cost at volume: Multiply per-request cost by expected daily calls, not just a one-off estimate.

Many teams are surprised that Sonnet handles 80% of what they assumed needed Opus, and that Haiku handles more of their "simple" traffic than expected once they measure instead of assume.

Questions

Is Sonnet always a safe default? Yes, for most general-purpose applications. Start with Sonnet, then downgrade to Haiku for high-volume simple tasks or upgrade to Opus for tasks where Sonnet's output quality falls short.

Does a bigger model always give better answers? No. For narrow, well-defined tasks, Haiku often matches Sonnet or Opus because the extra reasoning capacity isn't needed — you just pay more and wait longer for the same result.

Can I switch models without changing my whole integration? Yes — if you're calling through a standard Messages API format, switching models is usually just changing the model field in your request, as shown in the Messages API docs.