Claude Opus 5.5 vs. Sonnet 5.5: Welches Modell ist seinen Preis für Programmierung und Agenten wert?

Opus 5.5 vs Sonnet 5.5 at a Glance

Both models accept text and images and produce text. Their matching context and output limits give neither an automatic advantage for a large repository. The difference is how much work each does within that window, how quickly it finishes, and what the complete run costs. Specifications and official rates come from Anthropic's Sonnet 5.5 documentation and Opus 5.5 documentation. The GPT Proto row uses the rates shown on its Sonnet 5.5 and Opus 5.5 model pages; check those pages for current billing details, including caching.

The default effort row deserves attention. A comparison made with each model's API default is also comparing Sonnet at high with Opus at medium. Keep the setting in your test log; otherwise, the result is hard to reproduce.

Is Sonnet 5.5 Actually Cheaper Than Opus 5.5?

For the same number of uncached input and output tokens at Anthropic's published rates, yes. One million input tokens cost $2 on Sonnet and $4 on Opus; one million output tokens cost $10 and $20. On GPT Proto, the respective input/output rates are $1.80/$9 for Sonnet and $3.60/$18 for Opus: 10% below each model's official rates. Five-minute cache writes also cost half as much on Sonnet at Anthropic's rates. Official cache reads are $0.20 per million tokens on both models, so a workflow dominated by repeated cached context has a smaller rate difference than the headline suggests. Check GPT Proto's live cache rates separately rather than applying the input/output discount to every billing category.

Consider a fixed illustration: a request with 20,000 uncached input tokens and 5,000 output tokens would cost about $0.09 on Sonnet or $0.18 on Opus at Anthropic's list rates. Using GPT Proto's displayed input/output rates, the same fixed token counts would cost about $0.081 or $0.162, before caching or other charges. This is arithmetic, not a measurement of what either model needs to complete a real task. Once the models make different numbers of tool calls, consume different output tokens, or require different numbers of retries, the comparison changes.

Artificial Analysis provides a striking counterexample. At max effort across its Intelligence Index tasks, it measured Sonnet 5.5 at $7.60 per task with an index score of 56. Opus 5.5 cost $5.98 per task and scored 58 in that evaluation. Sonnet used roughly 193,000 output tokens per task, about 60% more than Opus at max. The evaluator notes that its Sonnet runs used a prerelease deployment with a structured-output issue that was later fixed, so I would not use those figures to predict a production bill to the cent. They do establish that “half-price tokens” can be the wrong shortcut for a high-effort run.

The useful metric for an application is cost per accepted task. Record uncached input, cache writes, cache reads, output, retries, and the human work required to correct failures. Then ask whether a more expensive request reduced the number of requests or the amount of review.

Which Is Better for Coding? It Depends on the Job

For a clearly specified bug fix or small feature, Sonnet 5.5 is the sensible first run. In one developer's controlled set of ten coding tasks, repeated three times per configuration at high reasoning, both models passed all 30 attempts in Claude Code. Sonnet averaged 53 seconds and about $0.15 per task; Opus averaged 108 seconds and about $0.36. The author says the set has become too easy for the newest models. I take it as evidence for choosing on cost and time when both pass your acceptance test, not evidence that they are equally good at harder software engineering.

For an unclear requirement spanning several files, the case for Opus gets stronger. Anthropic positions it for long-running agentic coding and sustained judgment, while placing Sonnet with better-defined everyday work. That is vendor guidance, not a substitute for testing your repository. A model might write the requested files correctly yet misunderstand the interface contract, omit a migration, or make changes outside the requested scope. Those failures matter more than how polished its first answer sounds.

Code review gives us a more concrete hard-task comparison. CodeRabbit's same-case test found that Sonnet 5.5 caught 6 of 13 known issues through actionable comments. Opus 5.5 caught 8 in its Standard configuration and 10 at Max. Thirteen cases are too few for a universal win rate, and the configurations differ. Still, if a missed defect on a high-risk change has a large downstream cost, Opus deserves its own evaluation rather than being dismissed on list price.

What about frontend coding?

I would start a well-specified frontend build on Sonnet, then judge the rendered page, interactions, responsiveness, and accessibility checks—not just the code diff. For open-ended visual direction, Opus may justify a trial when the first implementation misses the design brief. Published side-by-side anecdotes cannot establish a general “better at frontend” winner: the prompt, reference image, browser checks, and allowed revision time all affect the result.

What Do the Benchmarks Really Show?

Anthropic's Sonnet 5.5 launch results show a mixed picture:

Read the settings with the scores. Anthropic reports Opus's Terminal-Bench result at xhigh; its Sonnet FrontierCode result falls from 52.1% at xhigh to 46.2% at max. The company says the latter setting sometimes triggered additional review subagents, timeouts, or out-of-scope edits that the benchmark penalized. A higher effort setting did not reliably mean a better result on that test.

There is also measurement uncertainty. Anthropic reports a standard error of ±2.6 points for Opus on Terminal-Bench 4.0 and ±1.6–2 points for other Claude models. The 4.2-point observed gap should therefore be described as a result in that evaluation, not proof that Sonnet is consistently stronger at terminal coding. These numbers are most useful for choosing which tasks to include in your own tests.

A Side-by-Side Frontend Example

CodeRabbit published a side-by-side “Brick Studio” showcase using the same long build prompt in two Claude Code sessions. Both produced a brick-model designer with an Alpine Chalet demonstration. Sonnet finished in 29 minutes 27 seconds; Opus took 44 minutes 50 seconds. The testers described the results as close, with Opus slightly ahead on fidelity. Their page links to a video of the outputs.

This is one illustrated example, not a frontend benchmark. The sessions initially shared a working folder, Sonnet moved into an isolated subfolder partway through, and neither received the reference screenshot. Those conditions limit what a visual comparison can prove. For a GPT Proto-specific comparison, the useful addition would be two original rendered screenshots from an identical prompt, plus the acceptance checklist and recorded token usage. Until that test exists, the third-party showcase should stay clearly attributed.

Which Model Should Run Your Agentic Workflow?

An agent can spend much of its budget rereading instructions, repository files, and tool results. Both models have the same published cache-read rate, while their uncached input, output, and cache-write rates differ. In a long session, inspect the bill by token category before assuming a switch to Sonnet will halve spending.

My starting policy would be to send bounded steps—classifying an issue, editing a known component, drafting tests against a stated contract—to Sonnet. Escalate a failed acceptance check, an ambiguous architecture decision, or a sensitive review to Opus. This is an application design recommendation, not a claim that GPT Proto automatically routes requests between the two. It also has a cost: your application needs a clear acceptance check and a rule for when to retry or escalate.

Test both models on the same task set. Keep prompts, tools, context, and pass criteria as similar as possible; record the model and effort actually used. Judge the result after execution, including tool failures and any human edits. A cheap first pass that repeatedly needs Opus to repair it may be an expensive route to the same answer.

How to Compare Both Through GPT Proto

GPT Proto's Opus 5.5 model page shows an OpenAI-style chat-completions request to https://gptproto.com/v1/chat/completions using a bearer API key and the model string claude-opus-5-5. The Sonnet 5.5 model page is also available. Open API Usage or Try this model on each page to confirm the live model string and request format before running a comparison.

This Python example sends the same simple prompt to both model IDs. It deliberately leaves out provider-specific effort controls because support for passing those controls through GPT Proto has not been verified here. Set GPTPROTO_API_KEY in your environment first:

import json
import os
from urllib.request import Request, urlopen

api_key = os.environ["GPTPROTO_API_KEY"]
url = "https://gptproto.com/v1/chat/completions"
prompt = "Explain the smallest safe fix for a Python function that divides by zero."

for model in ("claude-sonnet-5-5", "claude-opus-5-5"):
    payload = json.dumps({
        "model": model,
        "messages": [{"role": "user", "content": prompt}],
    }).encode("utf-8")
    request = Request(
        url,
        data=payload,
        headers={
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json",
        },
        method="POST",
    )
    with urlopen(request, timeout=120) as response:
        result = json.load(response)
    print(model, result["choices"][0]["message"]["content"])
    print("usage:", result.get("usage"))

That prompt only checks access and response shape; it cannot settle the coding comparison. For a decision, replace it with a representative task and inspect the work against your own test suite. The code follows the public model-page request format but was not authenticated or run against a paid account for this article.

Which One Should You Choose?

Sonnet is the stronger default economic hypothesis for a well-defined task: its uncached input and output tokens cost half as much as Opus's, including at GPT Proto's displayed $1.80/$9 versus $3.60/$18 rates. Opus is the stronger quality hypothesis for an open-ended task whose errors are costly. Neither hypothesis replaces a short test on your own work. Check the live prices on the two model pages and the pricing page before projecting monthly spend.