Claude Opus vs Sonnet: When to Use Which

Anthropic's Claude family gives you two workhorses that cover most production needs: anthropic/claude-opus-4-8, the deepest-reasoning model, and anthropic/claude-sonnet-4-6, the balanced everyday model. Both are available through Model Database's single OpenAI-compatible endpoint, so choosing between them is a one-line change. The harder question is when each is worth it.

This guide breaks down the practical differences and gives you a decision rule you can apply per request.

What separates Opus from Sonnet

Think of the two as different points on the same capability curve:

• Opus is the top-tier reasoning model. It shines on multi-step problems, ambiguous specifications, complex refactors, and agentic workflows where small mistakes compound across many steps.
• Sonnet is the balanced model. It is faster and cheaper while still being strong at the vast majority of real-world tasks: drafting, extraction, summarization, RAG answers, and routine coding.

A useful intuition: Sonnet handles the work you do every day; Opus is the specialist you bring in for the genuinely hard 10 to 20 percent. Paying Opus rates for tasks Sonnet nails is the most common avoidable cost in an LLM app.

When to reach for Opus

Opus tends to earn its keep when:

• The task requires sustained reasoning across many steps, such as a coding agent editing files, running tests, and self-correcting.
• Instructions are subtle or contradictory and the model must weigh trade-offs.
• The cost of a wrong answer is high, for example architecture decisions, security analysis, or financial logic.
• You are building an autonomous loop where each error propagates downstream.

In these cases the higher per-request cost is small compared to the engineering time saved by a correct answer.

When Sonnet is the right call

Sonnet is usually the better default when:

• A human is waiting on the response and latency matters.
• Volume is high, so per-request cost dominates your bill.
• The task is well-specified: summarize this, extract these fields, answer from this context, write this function.

For most apps, Sonnet should be your baseline and Opus the escalation, not the other way around.

Switching between them

Because both sit behind the same endpoint, you only change the model field:

from openai import OpenAI

client = OpenAI(
    base_url="https://modeldatabase.com/v1",
    api_key="mdb_live_...",
)

def answer(prompt, hard=False):
    model = "anthropic/claude-opus-4-8" if hard else "anthropic/claude-sonnet-4-6"
    return client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
    )

A single boolean lets you route easy traffic to Sonnet and hard traffic to Opus from the same code path.

An escalation pattern that saves money

You don't have to decide upfront. A robust pattern is to try Sonnet first, then escalate to Opus only when the result fails a check, for example a JSON schema validation, a failing unit test, or a low-confidence self-rating.

resp = answer(task, hard=False)          # Sonnet attempt
if not passes_checks(resp):
    resp = answer(task, hard=True)        # escalate to Opus

To make this concrete, you can track the cost difference directly. Every billable response returns X-MDB-Charged-USD and X-MDB-Balance-USD, so you can log how often you escalate and what each tier actually costs on your traffic. Many teams find that 80 to 90 percent of requests never need Opus, which keeps the average cost close to Sonnet while preserving Opus-level quality on the cases that matter.

Don't forget evaluation

The split between the two depends entirely on your workload, so measure rather than assume. Take a sample of real tasks, run both models, and compare quality against the charged cost. If Sonnet's outputs are indistinguishable from Opus on your task, default to Sonnet. If Opus meaningfully reduces errors on high-stakes work, the premium is justified there.

Curious where the line falls for your use case? Create a key and add credit on your dashboard, then check the docs for streaming and parameter details. Start with Sonnet, escalate to Opus, and let the cost headers tell you the real story.