Claude Sonnet 4 vs Opus 4: Which Model Should You Use?

The most common mistake people make with Claude is defaulting to Opus 4 for everything because it's the flagship model. The second most common mistake is defaulting to Sonnet 4 for everything because it's cheaper. Neither is right.

The decision comes down to task complexity. On most production workloads, Sonnet 4 is the correct default. On a specific set of high-stakes tasks, Opus 4 pays for itself. Here's how to tell the difference.

What actually differs between the two

Opus 4 and Sonnet 4 are both capable models. The gap between them is narrower than marketing suggests, but it's real in specific areas.

Where Opus 4 is genuinely better:

• Multi-step reasoning that requires holding many constraints simultaneously
• Complex coding tasks — edge cases, intricate logic, debugging subtle bugs
• Following long, highly detailed system prompts without drifting
• Legal, financial, or technical document analysis requiring precise interpretation
• Tasks where a single error has high downstream cost

Where Sonnet 4 is equal or better:

• Latency-sensitive applications
• High-volume, lower-complexity tasks
• Content generation, summarization, Q&A
• RAG-backed retrieval tasks where the model just needs to synthesize retrieved context
• Most customer-facing chatbots

The raw capability difference is real but modest for most everyday tasks. The cost and speed difference is not modest at all.

Cost comparison

Current pricing (API, per million tokens):

Opus 4 costs 5x more than Sonnet 4 on input and 5x more on output. For a pipeline processing 10 million tokens per day, that's the difference between ~$90/day and ~$450/day. Over a month, that's $2,700 vs $13,500 — for the same task, if the task doesn't actually need Opus.

Haiku 4 is the tier below Sonnet — roughly 4x cheaper again. It's appropriate for very simple classification, routing, or extraction tasks where reasoning depth doesn't matter.

Use case matrix

Use Opus 4 for:

Complex agent loops. When an agent needs to plan, execute, re-plan based on tool outputs, and make judgment calls across many steps, Opus 4 makes fewer reasoning errors. In long chains, a single bad step can cascade. The cost premium is worth it when a failed run means wasted compute plus a broken workflow.

Hard coding problems. Debugging a race condition, implementing a non-trivial algorithm, or writing code that needs to handle many edge cases correctly on the first pass. Sonnet handles most coding tasks fine; Opus earns its price on the ones that require holding many constraints in mind simultaneously.

Legal and contract analysis. When the output will be acted on directly — not just a first draft to review, but an actual analysis of what a clause means — the precision difference matters. Opus 4 makes fewer interpretive errors on dense legal text.

Instruction-heavy system prompts. If your system prompt is 2,000+ words with dozens of specific rules, Opus is more reliable about following all of them. Sonnet drifts more on very complex instruction sets.

Use Sonnet 4 for:

Most production applications. Customer support, content generation, document summarization, Q&A over knowledge bases — Sonnet handles all of these extremely well at a fraction of the cost.

RAG pipelines. The model's job in a RAG setup is to synthesize retrieved context and answer a question. This doesn't require the deep reasoning Opus is optimized for. Sonnet is the right call here, and it's faster.

High-volume batch processing. Summarizing 10,000 documents, classifying support tickets, extracting structured data — the reasoning ceiling Opus provides adds nothing here, and the cost adds up fast.

Latency-sensitive features. Opus 4 is meaningfully slower. If you're building a feature where users wait for a response, Sonnet's speed is a real product advantage.

Iterative drafting and writing. Content generation, email drafts, blog posts, marketing copy — Sonnet's output quality here is excellent. You don't need Opus to write well.

Use Haiku 4 for:

Simple classification tasks. Intent detection. Routing decisions. Generating short structured outputs where the inputs are clean and the output schema is simple. Haiku is also a good choice for the "cheap worker" in multi-agent architectures where a coordinator (Sonnet or Opus) delegates simple sub-tasks.

5 tasks where you'd notice the difference

These are the task types where Opus 4 outperforms Sonnet 4 in ways that affect real outcomes:

1. Multi-constraint code generation: "Write a Python function that handles pagination, rate limiting, retries with exponential backoff, and logs errors in a structured format." Sonnet often drops one constraint. Opus doesn't.

2. Deep reasoning over long documents: Analyzing 50 pages of a contract and answering specific questions about obligations, carve-outs, and edge cases. The precision gap shows up.

3. Agentic tasks with 10+ steps: The compounding effect of Opus's lower error rate becomes apparent.

4. Following complex personas or personas with many restrictions: A system prompt that says "Never mention competitors, always deflect pricing questions to sales, respond only in English even if the user writes in another language, and never use the word 'unfortunately'" — Opus is more consistent.

5. Mathematical reasoning: Multi-step calculations or problems that require translating a word problem into an approach, then executing. Opus makes fewer logical errors.

5 tasks where you wouldn't notice the difference

1. Writing a marketing email
2. Summarizing a meeting transcript
3. Answering FAQ-style customer support questions
4. Generating 5 headline variations
5. Extracting entities from a document

For all of these, Sonnet 4 output quality is indistinguishable in practice. Using Opus here is leaving money on the table.

In Claude.ai (not the API)

If you're using Claude.ai rather than the API, the decision is simpler:

• Pro plan: Gives you access to Sonnet 4 as the default, with a limited number of Opus 4 uses per day.
• Max plan: Higher usage limits on Opus 4, plus access to extended thinking.

For most personal and professional use cases, Pro with Sonnet 4 is sufficient. The Max plan is worth it if you're regularly running the kinds of complex, long-horizon tasks described above — or if you hit the Pro limits regularly.

The hybrid approach

The most cost-efficient setup for teams building with the API: use Sonnet 4 as the default everywhere, and route specific task types to Opus 4 based on a classifier. The classifier itself can be Haiku — cheap and fast — and it checks whether the incoming request matches patterns that warrant Opus (long, complex, high-stakes). You pay Opus prices only where they matter.

This architecture is underused. Most teams pick one model and use it everywhere because routing adds complexity. But at scale, the savings are substantial.

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For how Claude's prompting behavior compares to OpenAI's models, see Claude vs GPT-4o prompting. For a broader three-way comparison including Gemini, ChatGPT vs Claude vs Gemini covers where each model has a genuine edge.