Sonnet 5 or Opus 5.5? Same price on Zukko, so test on your own tasks

Claude Sonnet 5 or Claude Opus 5.5 is a common question, and on Zukko it is answered differently than at the vendor: both models cost the same, $0.30 per 1M input and $0.30 per 1M output tokens. So you choose not by price but by which model handles your tasks better. Below are reference prices and a step-by-step comparison method with code.

Prices: official and on Zukko

Example monthly volume: 10M input and 2M output tokens.

(volumes in millions of tokens, prices per 1M.)

At the vendor, moving from Sonnet to Opus at this volume would add $40 a month. On Zukko the price is the same, so Opus 5.5 is not more expensive per token. The final bill can still differ: if one model writes longer answers, it spends more output tokens.

A note for agents with a large repeated context: at the vendor, prompt cache reads cost about 10% of the input price, while Zukko bills cached tokens at the regular input price. In such scenarios the real difference from the official API is smaller than the calculation above shows.

What the models have in common

• Context: 1M tokens for both.
• Call formats: Anthropic (/v1/messages) and OpenAI (/v1/chat/completions); the gateway converts.
• Request parameters and error handling are the same; the model changes in one line.
• Price on Zukko: $0.30 / $0.30.

What to compare on your tasks

We do not publish our own quality or speed measurements; we have no data that would fit your tasks. So instead of generic advice like "this model is better at X", we suggest measuring four things:

1. Pass rate against your criterion.
2. Output tokens per answer, which drive the bill.
3. Response time, as a median over several requests.
4. Number of agent iterations, if the model uses tools: more steps means more tokens.

The success criterion depends on the task type:

A/B test: step by step

1. Take 20–30 typical requests that actually go through your system. Add a few hard cases: on easy ones models often answer the same way.
2. Write down a reference answer or a check function for each.
3. Run the same requests through both models with the same prompt.
4. Save answers, tokens and timing.
5. Compare the totals. Grade texts blind, without looking at the model name.

import time
import anthropic

client = anthropic.Anthropic(
    base_url="https://api.zukko.pro",
    api_key="fetch_your_key",
)

cases = [
    {"prompt": "Classify the sentiment of this review with one word (positive/neutral/negative): "
               "'The item arrived, but the box was dented and delivery was rescheduled twice.'",
     "check": lambda a: a.strip().lower().startswith("negative")},
    {"prompt": "Write a Python regex for an email with a .ru or .com domain. "
               "Output only the expression.",

The cost of a run is easy to estimate up front. 30 cases × 2 models, on average 500 input and 500 output tokens per request:

Switching between models

Keep the model name in an environment variable so switching does not require a code change:

import os

MODEL = os.getenv("CLAUDE_MODEL", "claude-sonnet-5")

response = client.messages.create(
    model=MODEL,
    max_tokens=1024,
    messages=[{"role": "user", "content": "..."}],
)

In Claude Code the ANTHROPIC_MODEL variable does the same. Details are in the Claude Code guide↗.

Routing based on test results

If your system handles different kinds of tasks, split the A/B set by type and look at results per type. Then pin the choice in code:

ROUTES = {
    "classify": "claude-sonnet-5",   # fill in from your own measurements
    "extract": "claude-sonnet-5",
    "analysis": "claude-opus-5-5",
}

def model_for(task_type: str) -> str:
    return ROUTES.get(task_type, "claude-sonnet-5")

The values in ROUTES are a template, not a recommendation: set them from your own numbers. If both models pass equally on a task type, pick the one that spends fewer output tokens or is faster by your median. Recheck routes when the prompt changes or a new model appears in the catalog.

To try it, create a key in the dashboard↗; a minimal top-up is enough for such a test. The model set changes: the current list is at GET https://api.zukko.pro/v1/models or on the "Models" page in the dashboard.

FAQ

Is Opus 5.5 always better than Sonnet 5?

There is no general answer, and we will not invent one. Run your own set of 20–30 tasks using the method above and compare pass rates. Results on your data matter more than any general ranking.

I tested both and saw no difference. Which should I pick?

The one that, by your measurements, spends fewer output tokens (smaller bill) or responds faster. If that is a tie too, either one; revisit the comparison when the task changes.

Can I work on one model and switch to the other when needed?

Yes. Keep the model in config and route by task type as shown above. The key is to rely on test results, not assumptions.

Is the context window the same?

Yes, 1M tokens for both. You can find out how much your text takes with count_tokens↗ before sending the request.