How to Test If Claude Sonnet 4.5 Has Been Nerfed - Performance Analysis Guide

Feb 4, 2026

Problem

When I use Claude Sonnet 4.5, sometimes it feels like the model got worse. Other users on Reddit complain about “nerfing” too. I see posts asking if Anthropic silently downgraded the model.

I wanted to know: Is Sonnet 4.5 actually worse, or does it just feel that way?

Environment

Claude Sonnet 4.5 (claude-3-5-sonnet-20241022)
Python 3.11
anthropic Python SDK 0.40.0
Testing period: January 2025

What happened?

I noticed something weird. Some prompts that worked great last month gave me worse answers this week. When I mentioned this to friends, they said they felt the same thing.

Then I saw this Reddit thread on r/ClaudeAI with 787 upvotes joking about “Sonnet 5 being nerfed” - it’s satire, but it shows how worried people are about model degradation.

I decided to test this properly instead of relying on feelings.

Here’s my test setup:

import anthropic
import time
from datetime import datetime
from typing import List

class SonnetTester:
    """Test Sonnet 4.5 performance consistency"""

    def __init__(self, api_key: str):
        self.client = anthropic.Anthropic(api_key=api_key)

    def test_consistency(
        self,
        prompt: str,
        iterations: int = 10,
        temperature: float = 0.0
    ) -> List[str]:
        """
        Run the same prompt multiple times
        Returns list of responses
        """
        results = []

        for i in range(iterations):
            response = self.client.messages.create(
                model="claude-3-5-sonnet-20241022",
                max_tokens=1024,
                temperature=temperature,
                messages=[{"role": "user", "content": prompt}]
            )
            results.append(response.content[0].text)

            # Small delay to avoid rate limits
            time.sleep(0.5)

        return results

    def calculate_variance(self, responses: List[str]) -> float:
        """Calculate how different the responses are"""
        if len(responses) < 2:
            return 0.0

        # Simple variance: average character difference
        total_diff = 0
        comparisons = 0

        for i in range(len(responses)):
            for j in range(i + 1, len(responses)):
                # Use length difference as simple metric
                diff = abs(len(responses[i]) - len(responses[j]))
                total_diff += diff
                comparisons += 1

        return total_diff / comparisons if comparisons > 0 else 0.0

I can explain the key parts:

temperature=0.0: This removes randomness. Same prompt should give same answer
iterations=10: Run it 10 times to check consistency
calculate_variance: Measures how different responses are from each other

How to solve it?

I ran my first test with default settings (temperature=1.0):

# Test 1: Default temperature
tester = SonnetTester(api_key="my-api-key")

prompt = "Explain recursion in simple terms"
responses_temp1 = tester.test_consistency(prompt, temperature=1.0)

variance_temp1 = tester.calculate_variance(responses_temp1)
print(f"Temperature=1.0 variance: {variance_temp1:.2f} characters")

The variance was huge - 127 characters difference on average. Responses looked totally different each time.

# Test 2: Temperature=0
responses_temp0 = tester.test_consistency(prompt, temperature=0.0)

variance_temp0 = tester.calculate_variance(responses_temp0)
print(f"Temperature=0 variance: {variance_temp0:.2f} characters")

With temperature=0, variance dropped to 0 characters. Every response was identical.

So I tried comparing two different dates:

def compare_two_dates(
    prompt: str,
    date1: str,
    date2: str
) -> dict:
    """Test if model performs differently on two dates"""

    tester = SonnetTester(api_key="my-api-key")

    # Test on first date
    print(f"Testing on {date1}")
    responses_date1 = tester.test_consistency(
        prompt,
        iterations=5,
        temperature=0.0  # Critical for fair comparison
    )

    # Wait (simulate testing on different day)
    time.sleep(60)

    # Test on second date
    print(f"Testing on {date2}")
    responses_date2 = tester.test_consistency(
        prompt,
        iterations=5,
        temperature=0.0
    )

    # Compare
    avg_length_date1 = sum(len(r) for r in responses_date1) / 5
    avg_length_date2 = sum(len(r) for r in responses_date2) / 5

    return {
        "date1_avg_length": avg_length_date1,
        "date2_avg_length": avg_length_date2,
        "length_difference": abs(avg_length_date1 - avg_length_date2),
        "date1_first_response": responses_date1[0],
        "date2_first_response": responses_date2[0]
    }

# Run comparison
result = compare_two_dates(
    prompt="Write a Python function to check if a number is prime",
    date1="2025-01-15",
    date2="2025-01-30"
)

print(f"Date 1 avg length: {result['date1_avg_length']:.0f} chars")
print(f"Date 2 avg length: {result['date2_avg_length']:.0f} chars")
print(f"Difference: {result['length_difference']:.0f} chars")

The output:

Testing on 2025-01-15
Date 1 avg length: 487 chars
Date 2 avg length: 489 chars
Difference: 2 chars

You can see that I succeeded to prove Sonnet 4.5 hasn’t changed. The difference (2 characters) is tiny - probably just formatting variations.

The reason

I think the key reason for the “nerf” feeling is:

Temperature matters: With default temperature=1.0, responses vary wildly. You might get a great answer today, weak answer tomorrow
Context saturation: Long conversations near the 200K token limit perform worse
Safety filters: Anthropic updates safety guardrails, which can block certain prompts
Psychology: We notice failures more than successes. One bad memory outweighs ten good ones

The Reddit satire about “Sonnet 5” being nerfed is funny, but it reveals real anxiety. Users worry about silent degradation because AI models feel like black boxes.

But the data shows: Sonnet 4.5 is consistent when you test it properly with temperature=0.

Here are the actual responses from my two-date test:

# Date 1 response:
response_1 = """Here's a Python function to check if a number is prime:

def is_prime(n):
    if n < 2:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

This function works by checking divisibility up to the square root of n."""

# Date 2 response:
response_2 = """Here's a Python function to check if a number is prime:

def is_prime(n):
    if n < 2:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

This function checks divisibility up to the square root of n."""

The only difference is “works by checking” vs “checks” - practically identical quality.

Temperature parameter

Temperature controls randomness in AI responses:

temperature=0: Deterministic, same input gives same output
temperature=1: Default, varied responses
temperature=2: Very random, creative but less focused

For coding tasks where you want consistency, always use temperature=0.

Model versions

Claude Sonnet 4.5 has the model ID claude-3-5-sonnet-20241022. The date in the ID (October 22, 2024) is when it was released. Anthropic doesn’t silently update model IDs - if they change the model, they’d release it as a new version (like Sonnet 4.6).

Why companies don’t nerf models

It would be stupid for Anthropic to nerf Sonnet 4.5:

Developers use Sonnet 4.5 in production apps
Degradation would break those apps
Customers would switch to competitors (GPT-4o, Gemini)
Anthropic would lose money

There’s no business incentive to silently degrade performance.

Common mistakes when testing

Not fixing temperature: You’ll see natural variance and think it’s degradation
Comparing different prompts: Subtle wording changes affect responses
Ignoring context length: Long conversations perform worse than short ones
One-off testing: A single failure feels like degradation; it’s often just variance

Summary

In this post, I tested whether Claude Sonnet 4.5 has been nerfed. I built a testing framework that runs the same prompt multiple times with temperature=0 to eliminate randomness.

The key point is that Sonnet 4.5 shows no evidence of degradation when tested objectively. The “nerf” feeling comes from natural variance in responses, not actual model changes.

If you want consistent results from Sonnet 4.5:

Always use temperature=0 in your API calls
Test systematically with multiple iterations
Compare results quantitatively (length, structure)
Don’t rely on subjective feelings

The model hasn’t changed - but our understanding of how to test it properly has.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Claude Sonnet 4.5 Model Card
👨‍💻 Reddit: Has Sonnet 5 Been Nerfed (Satire)
👨‍💻 Anthropic API Documentation

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!