Claude 3 Opus vs Claude 3 Sonnet API: Which to Use?

If you're choosing between Claude 3 Opus and Claude 3 Sonnet for an API-driven product, the short answer is: Opus is for complex reasoning where accuracy matters more than cost, and Sonnet is for high-volume, latency-sensitive workloads where "good enough" at scale beats "best possible" at a premium. Both are called through the same Messages API with the same request/response shape — the difference is which model string you pass and what tradeoffs you accept.

This matters because the two models aren't interchangeable in production. Opus costs roughly 5x more per token than Sonnet and responds noticeably slower under load. If you're building a chatbot that handles thousands of concurrent support tickets, routing everything to Opus will blow your budget and make users wait. If you're building a legal document analyzer or a code review assistant where a wrong answer is expensive, Sonnet's occasional reasoning gaps will cost you more than the token savings are worth.

What actually differs at the API level

The request format is identical. You swap one string:

curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-3-opus-20240229",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Summarize this contract clause."}]
  }'

Change model to claude-3-sonnet-20240229 and the endpoint, headers, streaming behavior, and tool-use schema stay exactly the same. There's no separate SDK, no different auth flow, no different rate-limit tier logic to write. That consistency is useful — it means you can A/B test models by changing one field, not rewriting your integration.

Reasoning quality

Opus consistently handles multi-step reasoning, nuanced instructions, and long-context synthesis better than Sonnet. Tasks where this shows up in practice:

• Extracting structured data from messy, inconsistently formatted documents
• Multi-turn agentic workflows where the model has to track state across many tool calls
• Code generation involving unfamiliar APIs or non-trivial logic
• Tasks requiring careful adherence to many simultaneous constraints in a system prompt

Sonnet is still a capable model — it's not a "weak" tier. It handles straightforward summarization, classification, Q&A, and conversational tasks well. The gap widens as task complexity increases, not at the baseline.

Speed and cost

Sonnet is faster to first token and faster overall, which matters for anything user-facing where latency is part of the UX — live chat, autocomplete, real-time agents. Opus's extra reasoning depth comes with extra compute time per request.

On cost, the difference is substantial enough to change architecture decisions. At scale, teams often don't use one model exclusively — they route:

• Simple, high-volume requests → Sonnet
• Complex, low-volume, high-stakes requests → Opus

This routing pattern is common enough that it's worth designing for from day one rather than retrofitting it later.

A practical routing pattern

A simple approach: classify the request first (cheaply, often with Sonnet itself or a rules-based check), then dispatch to the right model.

async function routeRequest(userInput, complexity) {
  const model = complexity === "high"
    ? "claude-3-opus-20240229"
    : "claude-3-sonnet-20240229";

  const response = await fetch("https://api.anthropic.com/v1/messages", {
    method: "POST",
    headers: {
      "x-api-key": process.env.ANTHROPIC_API_KEY,
      "anthropic-version": "2023-06-01",
      "content-type": "application/json"
    },
    body: JSON.stringify({
      model,
      max_tokens: 1024,
      messages: [{ role: "user", content: userInput }]
    })
  });

  return response.json();
}

In practice, "complexity" might be determined by input length, whether tools are involved, whether the task touches financial or legal content, or a simple heuristic you tune over time based on error rates.

Where this gets operationally messy

Running both models in production means tracking usage, costs, and errors separately for each, managing two sets of rate limits, and usually building some kind of internal dashboard just to see which teams or features are burning through which model's budget. If you're shipping this inside a product with multiple developers or teams touching the same underlying Claude access, that bookkeeping adds up fast.

This is the exact problem SubToAPI solves. It takes your Claude access and exposes it as a clean HTTPS API with sub_live_... application keys, so each team or feature gets its own key, usage is tracked per key automatically, and you can see exactly how much Opus vs Sonnet traffic each part of your product is generating — all from one dashboard instead of stitching together logs. Streaming, tool use, and usage metadata work the same way regardless of which underlying model you call. Setup takes about five minutes — see the /docs/quickstart guide — and plans start at €9/month with a free trial at /signup.

If you're already comparing models on cost and want to also get visibility into per-key spend without building that tooling yourself, it's worth checking /pricing.

Choosing without overthinking it

If you're not sure which model to start with, default to Sonnet and upgrade specific flows to Opus only when you observe quality problems — hallucinated details, missed constraints, inconsistent structured output. Measuring this is easier than guessing: log a sample of real outputs, review them, and move the underperforming flows to Opus. Most products end up using Sonnet for 70-90% of requests and Opus for the hard remainder.

Questions

Does switching between Opus and Sonnet require any code changes besides the model name? No. The Messages API, streaming format, and tool-use schema are identical across Claude 3 models. You only change the model field in the request body.

Is Claude 3 Opus always more accurate than Sonnet? On complex, multi-step, or nuanced tasks, generally yes. On simple classification, summarization, or Q&A, the practical difference is often small enough that Sonnet's speed and cost advantage make it the better default.

Can I use both models through the same API key and dashboard? With the direct Anthropic API, yes — one key covers all models. With SubToAPI, you get the same flexibility plus per-key usage tracking, so you can see Opus and Sonnet spend broken out by team or feature. See /docs/messages for request details.