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Claude 2.1 vs GPT-4: A Technical Comparison While We Await Claude 3
Claude 2.1 vs GPT-4: A Technical Comparison While We Await Claude 3
With Claude 3 expected soon, now is a good time to compare the current state of play between Claude 2.1 and GPT-4. Let’s dive into a technical comparison of these two frontier models.
Current Benchmark Performance
Based on published benchmarks, both models perform impressively:
| Benchmark | Claude 2.1 | GPT-4 |
|---|---|---|
| MMLU | 78.5% | 86.4% |
| HumanEval | 70.0% | 67.0% |
| Context Window | 200K | 128K |
API Comparison
Claude 2.1
import anthropic
client = anthropic.Anthropic()
response = client.messages.create(
model="claude-2.1",
max_tokens=4096,
messages=[
{"role": "user", "content": "Write a binary search in Python"}
]
)
GPT-4
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4-turbo-preview",
max_tokens=4096,
messages=[
{"role": "system", "content": "You are a helpful coding assistant."},
{"role": "user", "content": "Write a binary search in Python"}
]
)
Context Window Comparison
# Claude 2.1: 200K tokens
# GPT-4 Turbo: 128K tokens
# Example: Processing large documents with Claude
def process_large_document(document: str) -> str:
client = anthropic.Anthropic()
response = client.messages.create(
model="claude-2.1",
max_tokens=4096,
messages=[
{
"role": "user",
"content": f"Summarize this document:\n\n{document}"
}
]
)
return response.content[0].text
Pricing Comparison
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| Claude 2.1 | $8.00 | $24.00 |
| GPT-4 Turbo | $10.00 | $30.00 |
Practical Test: Code Generation
Let’s test both models with a real coding task:
# Test prompt: "Create a REST API endpoint for user authentication"
# Claude 2.1 typically produces clean, well-documented code:
from fastapi import FastAPI, HTTPException, Depends
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from pydantic import BaseModel
import jwt
from datetime import datetime, timedelta
app = FastAPI()
security = HTTPBearer()
SECRET_KEY = "your-secret-key"
class UserLogin(BaseModel):
username: str
password: str
class Token(BaseModel):
access_token: str
token_type: str
@app.post("/auth/login", response_model=Token)
async def login(user: UserLogin):
# Validate credentials (simplified)
if user.username == "admin" and user.password == "password":
token = jwt.encode(
{
"sub": user.username,
"exp": datetime.utcnow() + timedelta(hours=24)
},
SECRET_KEY,
algorithm="HS256"
)
return Token(access_token=token, token_type="bearer")
raise HTTPException(status_code=401, detail="Invalid credentials")
@app.get("/auth/verify")
async def verify(credentials: HTTPAuthorizationCredentials = Depends(security)):
try:
payload = jwt.decode(
credentials.credentials,
SECRET_KEY,
algorithms=["HS256"]
)
return {"username": payload["sub"], "valid": True}
except jwt.ExpiredSignatureError:
raise HTTPException(status_code=401, detail="Token expired")
Key Differences
- Context length: Claude 2.1 has a larger context window (200K vs 128K)
- System prompts: GPT-4 has dedicated system role, Claude uses user messages
- Vision: GPT-4V supports image inputs, Claude 2.1 is text-only
- Function calling: GPT-4 has more mature tool use capabilities
What Claude 3 Might Change
When Claude 3 releases, we expect:
- Potential vision capabilities
- Improved benchmark scores
- Possibly multiple model tiers for different use cases
- Better reasoning on complex tasks
Recommendation
Choose based on your current needs:
- Claude 2.1: Best for long documents, instruction following, cost-conscious deployments
- GPT-4: Best for established ecosystem, multimodal needs, Azure integration
Conclusion
Both models are excellent choices today. The best approach is often a multi-provider strategy, using each model for its strengths. Stay tuned for Claude 3’s release, which could shift this comparison significantly.