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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:

BenchmarkClaude 2.1GPT-4
MMLU78.5%86.4%
HumanEval70.0%67.0%
Context Window200K128K

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

ModelInput (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

  1. Context length: Claude 2.1 has a larger context window (200K vs 128K)
  2. System prompts: GPT-4 has dedicated system role, Claude uses user messages
  3. Vision: GPT-4V supports image inputs, Claude 2.1 is text-only
  4. 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.

Michael John Peña

Michael John Peña

Senior Data Engineer based in Sydney. Writing about data, cloud, and technology.