6 min read
Claude 3.5 Sonnet vs GPT-4o: A Technical Comparison
With Claude 3.5 Sonnet and GPT-4o both available, choosing the right model for your application requires understanding their differences. I’ve been testing both extensively for data engineering and AI development tasks. Here’s what I’ve learned.
Benchmark Comparison
| Benchmark | Claude 3.5 Sonnet | GPT-4o |
|---|---|---|
| MMLU | 88.7% | 88.7% |
| HumanEval | 92.0% | 90.2% |
| MATH | 71.1% | 76.6% |
| GPQA | 59.4% | 53.6% |
| MGSM | 91.6% | 90.5% |
The benchmarks are close, but they tell different stories. Claude edges ahead on coding and reasoning; GPT-4o leads on math. Real-world performance differs from benchmarks though.
Code Generation Test
I gave both models the same task:
Create a Python class that implements a connection pool for Azure SQL Database
with automatic retry logic, connection health checks, and graceful shutdown.Claude 3.5 Sonnet Output:
import pyodbc
import threading
import time
from queue import Queue, Empty
from contextlib import contextmanager
from typing import Optional
import logging
logger = logging.getLogger(__name__)
class AzureSQLConnectionPool:
def __init__(
self,
connection_string: str,
min_connections: int = 2,
max_connections: int = 10,
connection_timeout: int = 30,
max_retries: int = 3,
retry_delay: float = 1.0,
health_check_interval: int = 60
):
self.connection_string = connection_string
self.min_connections = min_connections
self.max_connections = max_connections
self.connection_timeout = connection_timeout
self.max_retries = max_retries
self.retry_delay = retry_delay
self.health_check_interval = health_check_interval
self._pool: Queue = Queue(maxsize=max_connections)
self._lock = threading.Lock()
self._active_connections = 0
self._shutdown = False
self._health_check_thread: Optional[threading.Thread] = None
self._initialize_pool()
self._start_health_check()
def _create_connection(self) -> pyodbc.Connection:
"""Create a new connection with retry logic."""
last_exception = None
for attempt in range(self.max_retries):
try:
conn = pyodbc.connect(
self.connection_string,
timeout=self.connection_timeout
)
conn.autocommit = False
return conn
except pyodbc.Error as e:
last_exception = e
logger.warning(f"Connection attempt {attempt + 1} failed: {e}")
if attempt < self.max_retries - 1:
time.sleep(self.retry_delay * (2 ** attempt))
raise ConnectionError(f"Failed to connect after {self.max_retries} attempts") from last_exception
def _initialize_pool(self):
"""Pre-populate pool with minimum connections."""
for _ in range(self.min_connections):
try:
conn = self._create_connection()
self._pool.put(conn)
with self._lock:
self._active_connections += 1
except ConnectionError as e:
logger.error(f"Failed to initialize connection: {e}")
def _is_connection_healthy(self, conn: pyodbc.Connection) -> bool:
"""Check if connection is still valid."""
try:
cursor = conn.cursor()
cursor.execute("SELECT 1")
cursor.fetchone()
cursor.close()
return True
except pyodbc.Error:
return False
def _start_health_check(self):
"""Start background health check thread."""
def health_check_loop():
while not self._shutdown:
time.sleep(self.health_check_interval)
if self._shutdown:
break
self._check_and_repair_connections()
self._health_check_thread = threading.Thread(
target=health_check_loop,
daemon=True
)
self._health_check_thread.start()
def _check_and_repair_connections(self):
"""Check all pooled connections and replace unhealthy ones."""
checked = 0
while checked < self._pool.qsize():
try:
conn = self._pool.get_nowait()
if self._is_connection_healthy(conn):
self._pool.put(conn)
else:
logger.info("Replacing unhealthy connection")
try:
conn.close()
except:
pass
with self._lock:
self._active_connections -= 1
try:
new_conn = self._create_connection()
self._pool.put(new_conn)
with self._lock:
self._active_connections += 1
except ConnectionError as e:
logger.error(f"Failed to replace connection: {e}")
checked += 1
except Empty:
break
@contextmanager
def get_connection(self):
"""Get a connection from the pool."""
if self._shutdown:
raise RuntimeError("Connection pool is shut down")
conn = None
try:
conn = self._pool.get(timeout=self.connection_timeout)
if not self._is_connection_healthy(conn):
try:
conn.close()
except:
pass
with self._lock:
self._active_connections -= 1
conn = self._create_connection()
with self._lock:
self._active_connections += 1
yield conn
conn.commit()
except Empty:
with self._lock:
if self._active_connections < self.max_connections:
conn = self._create_connection()
self._active_connections += 1
try:
yield conn
conn.commit()
finally:
self._pool.put(conn)
return
raise TimeoutError("No connections available")
except Exception as e:
if conn:
try:
conn.rollback()
except:
pass
raise
finally:
if conn and not self._shutdown:
self._pool.put(conn)
def shutdown(self, timeout: float = 30.0):
"""Gracefully shutdown the connection pool."""
logger.info("Initiating connection pool shutdown")
self._shutdown = True
if self._health_check_thread:
self._health_check_thread.join(timeout=5.0)
deadline = time.time() + timeout
while time.time() < deadline:
try:
conn = self._pool.get_nowait()
try:
conn.close()
except:
pass
with self._lock:
self._active_connections -= 1
except Empty:
break
logger.info(f"Connection pool shutdown complete. Remaining: {self._active_connections}")GPT-4o Output:
GPT-4o produced similar quality code but with slightly different design choices - it used async/await patterns and integrated with Azure SDK retry policies. Both were production-viable.
Key Differences I’ve Noticed
Instruction Following
Claude 3.5 Sonnet tends to follow instructions more precisely. When I say “only return JSON,” it returns only JSON. GPT-4o sometimes adds explanatory text.
# Claude follows format instructions strictly
claude_response = """
{
"tables": ["customers", "orders"],
"relationships": [{"from": "orders.customer_id", "to": "customers.id"}]
}
"""
# GPT-4o might add context
gpt4o_response = """
Here's the JSON representation:
{
"tables": ["customers", "orders"],
"relationships": [{"from": "orders.customer_id", "to": "customers.id"}]
}
This shows the basic structure...
"""Context Window Usage
Both have 128K+ token context windows, but they behave differently with long contexts:
- GPT-4o: Better at maintaining coherence across very long documents
- Claude: Better at finding specific details in long contexts (“needle in haystack”)
Code Style
Claude tends toward more verbose, explicit code with comprehensive error handling. GPT-4o often produces more concise solutions. Neither is objectively better - it depends on your team’s preferences.
Testing Framework
Here’s how I compare models systematically:
import anthropic
from openai import AzureOpenAI
import time
import json
class ModelComparator:
def __init__(self):
self.claude = anthropic.Anthropic()
self.openai = AzureOpenAI(
azure_endpoint="https://your-resource.openai.azure.com/",
api_version="2024-05-01-preview"
)
def compare_response(self, prompt: str, evaluation_criteria: list[str]):
results = {}
# Claude
start = time.time()
claude_response = self.claude.messages.create(
model="claude-3-5-sonnet-20240620",
max_tokens=4096,
messages=[{"role": "user", "content": prompt}]
)
results["claude"] = {
"response": claude_response.content[0].text,
"latency": time.time() - start,
"input_tokens": claude_response.usage.input_tokens,
"output_tokens": claude_response.usage.output_tokens
}
# GPT-4o
start = time.time()
gpt_response = self.openai.chat.completions.create(
model="gpt-4o",
max_tokens=4096,
messages=[{"role": "user", "content": prompt}]
)
results["gpt4o"] = {
"response": gpt_response.choices[0].message.content,
"latency": time.time() - start,
"input_tokens": gpt_response.usage.prompt_tokens,
"output_tokens": gpt_response.usage.completion_tokens
}
return results
def run_benchmark_suite(self, test_cases: list[dict]):
all_results = []
for test in test_cases:
result = self.compare_response(
test["prompt"],
test.get("criteria", [])
)
result["test_name"] = test["name"]
all_results.append(result)
return all_results
# Example usage
comparator = ModelComparator()
tests = [
{
"name": "sql_generation",
"prompt": "Generate a SQL query for Azure Synapse that calculates running totals partitioned by region",
"criteria": ["correctness", "efficiency", "readability"]
},
{
"name": "error_diagnosis",
"prompt": "Diagnose this error: 'Cannot insert duplicate key row in object dbo.Users'",
"criteria": ["accuracy", "actionability", "completeness"]
}
]
results = comparator.run_benchmark_suite(tests)Cost Analysis
| Model | Input ($/1M tokens) | Output ($/1M tokens) |
|---|---|---|
| Claude 3.5 Sonnet | $3.00 | $15.00 |
| GPT-4o | $5.00 | $15.00 |
Claude 3.5 Sonnet has lower input costs, which matters for applications with large context windows or RAG systems with lengthy retrieved documents.
My Recommendations
Use Claude 3.5 Sonnet for:
- Code generation and review
- Precise instruction following
- Long document analysis with specific extraction
- Safety-critical applications
Use GPT-4o for:
- Real-time voice applications (Realtime API)
- Math-heavy computations
- Existing OpenAI/Azure OpenAI infrastructure
- Multimodal applications with audio
Use Both: The best strategy is often using both models. Route simpler tasks to the cheaper option, complex tasks to whichever performs better for that specific use case.
Conclusion
Both models are excellent. The “best” choice depends on your specific use case, existing infrastructure, and cost constraints. I recommend building abstraction layers that let you switch models easily - the landscape changes rapidly, and flexibility is valuable.