What Is GPT-3.5 or GPT-4 or GPT-4 Turbo? Everything You Should Know

If you’ve been following the AI space (and let’s be honest, who hasn’t?), you’ve probably heard these terms thrown around: GPT-3.5, GPT-4, GPT-4 Turbo. But what do they actually mean? How are they different? And more importantly—which one should you use?

As someone who’s been building enterprise systems for over two decades and has spent the last year integrating these models into production applications, I want to give you a practical, no-nonsense breakdown of OpenAI’s GPT model family.

Quick Context: This article reflects the state of OpenAI’s models as of January 2024, following the GPT-4 Turbo announcement at DevDay in November 2023. The AI landscape moves fast, so always check OpenAI’s documentation for the latest updates.

What is GPT-4 Turbo?
GPT-4 vs. GPT-4 Turbo
How GPT-4 Turbo Works
How to Access GPT-4 Turbo
Is GPT-4 Turbo Free? Pricing Differences Between GPT Variants
GPT-4 Turbo for Developers
Benefits of GPT-4 Turbo
Drawbacks of GPT-4 Turbo
Conclusion

What is GPT-4 Turbo?

GPT-4 Turbo is OpenAI’s latest and most capable model, announced at DevDay on November 6, 2023. Think of it as GPT-4’s faster, cheaper, and smarter sibling. It’s designed to address the main pain points developers and enterprises had with the original GPT-4: cost, speed, and the knowledge cutoff date.

But let’s back up a moment and understand the GPT family tree:

The GPT Model Family (As of January 2024)

Model	Release	Context Window	Knowledge Cutoff
GPT-3.5	March 2022	4K / 16K tokens	Sept 2021
GPT-4	March 2023	8K / 32K tokens	Sept 2021
GPT-4 Turbo	Nov 2023	128K tokens	April 2023
GPT-4 Vision	Nov 2023	128K tokens	April 2023

GPT-3.5 is the workhorse model that powers ChatGPT for free users. It’s fast, cheap, and good enough for most casual use cases. But it’s noticeably less capable at complex reasoning, coding, and nuanced tasks.

GPT-4 was a massive leap forward—better reasoning, fewer hallucinations, and the ability to handle much more complex prompts. But it came with a cost: it was slow and expensive.

GPT-4 Turbo aims to give you GPT-4’s intelligence at a fraction of the cost and with significant speed improvements. It’s the model OpenAI recommends for most production use cases.

GPT-4 vs. GPT-4 Turbo

This is the comparison most developers care about. You’ve been using GPT-4, and you’re wondering: should I switch? Here’s the breakdown:

Feature	GPT-4	GPT-4 Turbo
Context Window	8K / 32K tokens	128K tokens ✓
Knowledge Cutoff	Sept 2021	April 2023 ✓
Input Cost (per 1K tokens)	$0.03	$0.01 ✓ (3x cheaper)
Output Cost (per 1K tokens)	$0.06	$0.03 ✓ (2x cheaper)
JSON Mode	No	Yes ✓
Function Calling	Yes	Improved ✓
Reproducible Outputs	No	Yes (seed parameter) ✓
Speed	Slower	Faster ✓

The verdict? GPT-4 Turbo is better in almost every way. Unless you have a very specific use case that requires the original GPT-4’s behavior (and I can’t think of many), you should be using GPT-4 Turbo.

The 128K Context Window: This is massive. 128K tokens is approximately 300 pages of text. You can now fit entire codebases, lengthy documents, or extensive conversation histories into a single prompt. This opens up use cases that were previously impossible with GPT-4.

How GPT-4 Turbo Works

Under the hood, GPT-4 Turbo is still a transformer-based large language model (LLM), but OpenAI has made significant optimizations:

Architecture Improvements

Optimized Inference: OpenAI has optimized the model’s inference pipeline, reducing latency and improving throughput.
Efficient Attention Mechanisms: Handling 128K tokens requires clever engineering. The model likely uses techniques like sparse attention or sliding window attention to manage the massive context efficiently.
Knowledge Distillation: Some improvements likely come from distilling knowledge from larger models into a more efficient architecture.

Key Technical Features

1. JSON Mode

One of my favorite new features. You can now force the model to output valid JSON:

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4-turbo-preview",
    response_format={"type": "json_object"},
    messages=[
        {"role": "system", "content": "You are an API that returns JSON."},
        {"role": "user", "content": "Extract: John is 30 years old and lives in Dublin"}
    ]
)

# Guaranteed valid JSON output
# {"name": "John", "age": 30, "city": "Dublin"}
print(response.choices[0].message.content)

2. Reproducible Outputs (Seed Parameter)

For testing and debugging, you can now get deterministic outputs:

response = client.chat.completions.create(
    model="gpt-4-turbo-preview",
    seed=42,  # Fixed seed for reproducibility
    messages=[{"role": "user", "content": "Tell me a joke"}]
)

# Same seed + same prompt = same output (mostly)
# Check response.system_fingerprint to verify

3. Improved Function Calling

GPT-4 Turbo can now call multiple functions in a single response and follows function schemas more accurately:

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                },
                "required": ["location"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "get_stock_price",
            "description": "Get current stock price",
            "parameters": {
                "type": "object",
                "properties": {
                    "symbol": {"type": "string"}
                },
                "required": ["symbol"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4-turbo-preview",
    messages=[{"role": "user", "content": "What's the weather in Dublin and the price of AAPL?"}],
    tools=tools,
    tool_choice="auto"
)

# GPT-4 Turbo can call BOTH functions in a single response

How to Access GPT-4 Turbo

There are several ways to use GPT-4 Turbo:

1. ChatGPT Plus ($20/month)

If you’re a ChatGPT Plus subscriber, you already have access to GPT-4 Turbo. When you select “GPT-4” in the model dropdown, you’re now getting GPT-4 Turbo under the hood.

2. OpenAI API

For developers, the API is the way to go. You’ll need:

An OpenAI account with billing set up
API credits (pay-as-you-go)

# Install the OpenAI Python library
# pip install openai

from openai import OpenAI

client = OpenAI(api_key="your-api-key")

# Use gpt-4-turbo-preview for the latest GPT-4 Turbo
response = client.chat.completions.create(
    model="gpt-4-turbo-preview",  # or "gpt-4-1106-preview" for the specific version
    messages=[
        {"role": "user", "content": "Hello, GPT-4 Turbo!"}
    ]
)

print(response.choices[0].message.content)

3. Azure OpenAI Service

For enterprise customers, Microsoft Azure offers GPT-4 Turbo through Azure OpenAI Service. This gives you enterprise-grade security, compliance, and SLAs:

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="your-azure-key",
    api_version="2024-02-15-preview",
    azure_endpoint="https://your-resource.openai.azure.com"
)

response = client.chat.completions.create(
    model="gpt-4-turbo",  # Your deployment name
    messages=[{"role": "user", "content": "Hello from Azure!"}]
)

Model Names Reference

Model Name	Description
`gpt-4-turbo-preview`	Latest GPT-4 Turbo (points to newest version)
`gpt-4-1106-preview`	GPT-4 Turbo from November 6, 2023
`gpt-4-vision-preview`	GPT-4 Turbo with vision capabilities
`gpt-4`	Original GPT-4 (8K context)
`gpt-3.5-turbo`	Latest GPT-3.5 (fast and cheap)

Is GPT-4 Turbo Free? Pricing Differences Between GPT Variants

Let’s talk money. This is often the deciding factor for production applications.

Is GPT-4 Turbo Free?

Short answer: No. GPT-4 Turbo is not free. However, you can access it:

ChatGPT Plus: $20/month for unlimited* access via the chat interface
API: Pay-per-token pricing

*ChatGPT Plus has rate limits, but they’re generous for most personal use.

API Pricing Comparison (January 2024)

Model	Input (per 1K tokens)	Output (per 1K tokens)	Context
GPT-3.5 Turbo	$0.0005	$0.0015	16K
GPT-4	$0.03	$0.06	8K
GPT-4 (32K)	$0.06	$0.12	32K
GPT-4 Turbo	$0.01	$0.03	128K

Real-World Cost Examples

Let’s put this in perspective with some practical scenarios:

Scenario: Summarizing a 10-page document (~4,000 tokens input, ~500 tokens output)

GPT-3.5 Turbo: $0.002 + $0.00075 = $0.00275 per document
GPT-4:         $0.12 + $0.03    = $0.15 per document
GPT-4 Turbo:   $0.04 + $0.015   = $0.055 per document

If you process 10,000 documents per month:
- GPT-3.5 Turbo: $27.50/month
- GPT-4:         $1,500/month
- GPT-4 Turbo:   $550/month (63% cheaper than GPT-4!)

Cost Optimization Tip: For many use cases, you can use GPT-3.5 Turbo for initial processing and only escalate to GPT-4 Turbo for complex tasks. This “tiered” approach can dramatically reduce costs while maintaining quality where it matters.

GPT-4 Turbo for Developers

If you’re building applications with GPT-4 Turbo, here are the key things you need to know:

Best Practices

1. Use System Messages Effectively

response = client.chat.completions.create(
    model="gpt-4-turbo-preview",
    messages=[
        {
            "role": "system",
            "content": """You are a senior software engineer reviewing code.
            Be concise and specific. Focus on:
            - Security vulnerabilities
            - Performance issues
            - Code maintainability
            Always provide actionable suggestions."""
        },
        {"role": "user", "content": "Review this code: [code here]"}
    ]
)

2. Leverage the 128K Context Window

# You can now include entire files, documentation, or conversation history
with open("large_codebase.py", "r") as f:
    code = f.read()

response = client.chat.completions.create(
    model="gpt-4-turbo-preview",
    messages=[
        {"role": "system", "content": "You are a code analysis assistant."},
        {"role": "user", "content": f"Analyze this codebase for security issues:\n\n{code}"}
    ],
    max_tokens=4096
)

3. Use JSON Mode for Structured Outputs

def extract_entities(text: str) -> dict:
    response = client.chat.completions.create(
        model="gpt-4-turbo-preview",
        response_format={"type": "json_object"},
        messages=[
            {
                "role": "system",
                "content": """Extract entities from text. Return JSON:
                {
                    "people": [{"name": string, "role": string}],
                    "organizations": [string],
                    "locations": [string],
                    "dates": [string]
                }"""
            },
            {"role": "user", "content": text}
        ]
    )
    return json.loads(response.choices[0].message.content)

4. Implement Retry Logic with Exponential Backoff

import time
from openai import OpenAI, RateLimitError, APIError

def call_gpt4_turbo_with_retry(messages, max_retries=3):
    client = OpenAI()
    
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model="gpt-4-turbo-preview",
                messages=messages
            )
            return response
        except RateLimitError:
            wait_time = 2 ** attempt  # Exponential backoff
            print(f"Rate limited. Waiting {wait_time} seconds...")
            time.sleep(wait_time)
        except APIError as e:
            print(f"API error: {e}")
            if attempt == max_retries - 1:
                raise
    
    raise Exception("Max retries exceeded")

Integration Patterns

RAG (Retrieval Augmented Generation) with GPT-4 Turbo

from openai import OpenAI
import chromadb

client = OpenAI()
chroma_client = chromadb.Client()
collection = chroma_client.get_collection("knowledge_base")

def rag_query(user_question: str) -> str:
    # Retrieve relevant documents
    results = collection.query(
        query_texts=[user_question],
        n_results=5
    )
    
    context = "\n\n".join(results['documents'][0])
    
    # Generate answer with GPT-4 Turbo
    response = client.chat.completions.create(
        model="gpt-4-turbo-preview",
        messages=[
            {
                "role": "system",
                "content": f"""Answer questions based on the following context.
                If the answer isn't in the context, say so.
                
                Context:
                {context}"""
            },
            {"role": "user", "content": user_question}
        ]
    )
    
    return response.choices[0].message.content

Benefits of GPT-4 Turbo

After using GPT-4 Turbo extensively in production, here are the benefits that matter most:

1. Massive Cost Reduction

3x cheaper input costs and 2x cheaper output costs compared to GPT-4. For high-volume applications, this can mean savings of thousands of dollars per month.

2. 128K Context Window

This is a game-changer for:

Code analysis: Analyze entire codebases in one prompt
Document processing: Summarize or extract from long documents without chunking
Conversation memory: Maintain much longer conversation histories
Few-shot learning: Include many more examples in your prompts

3. Updated Knowledge Cutoff (April 2023)

GPT-4 Turbo knows about events up to April 2023, including:

Latest programming frameworks and libraries
Recent world events
Updated best practices

4. JSON Mode for Reliable Structured Output

No more parsing errors from malformed JSON. The model is guaranteed to output valid JSON when you enable this mode.

5. Reproducible Outputs

The seed parameter enables consistent outputs for testing and debugging—a feature developers have been requesting for ages.

6. Improved Function Calling

Better accuracy in function selection and parameter extraction, plus the ability to call multiple functions in parallel.

7. Faster Response Times

Optimized inference means you get responses faster, improving user experience and reducing timeout issues.

Drawbacks of GPT-4 Turbo

No model is perfect. Here are the limitations you should be aware of:

1. Still in Preview

As of January 2024, GPT-4 Turbo is still labeled as “preview.” This means:

The model may be updated without notice
Behavior might change between versions
OpenAI recommends pinning to specific versions for production

2. Knowledge Still Has a Cutoff

April 2023 is better than September 2021, but the model still doesn’t know about recent events. For current information, you need to use RAG or function calling.

3. Rate Limits

GPT-4 Turbo has stricter rate limits than GPT-3.5:

Lower tokens-per-minute (TPM) limits
Lower requests-per-minute (RPM) limits
New accounts may have very low limits initially

4. Not Always Better Than GPT-3.5

For simple tasks, GPT-3.5 Turbo might be:

Fast enough
Accurate enough
20-60x cheaper

Don’t use a sledgehammer when a regular hammer will do.

5. Potential for Hallucinations

While reduced compared to earlier models, GPT-4 Turbo can still hallucinate, especially for:

Obscure topics
Recent events after April 2023
Precise numerical data
Citations and references

6. Context Window Cost

Using the full 128K context window means you’re paying for 128K input tokens. Make sure you actually need that much context—don’t pad prompts unnecessarily.

Conclusion

GPT-4 Turbo represents a significant step forward in making advanced AI accessible and practical for production use. With its lower costs, larger context window, and improved features like JSON mode and reproducible outputs, it addresses most of the pain points developers had with GPT-4.

Here’s my recommendation:

For simple tasks (basic Q&A, simple text generation): Use GPT-3.5 Turbo. It’s fast, cheap, and good enough.
For complex reasoning, coding, or analysis: Use GPT-4 Turbo. The quality improvement justifies the cost.
For production applications: Consider a tiered approach—GPT-3.5 for routine tasks, GPT-4 Turbo for complex ones.
For enterprise deployments: Look at Azure OpenAI for better SLAs and compliance.

The AI landscape is evolving rapidly. By the time you read this, there might be new models or pricing changes. Always check OpenAI’s official documentation for the latest information.

Key Takeaways:

GPT-4 Turbo is 3x cheaper than GPT-4 with better features

128K context window enables new use cases

JSON mode and seed parameter improve reliability

Still in preview—pin versions for production

Not a replacement for GPT-3.5 in all cases—choose the right model for the task

References & Further Reading

OpenAI GPT-4 Turbo Announcement – openai.com/blog
OpenAI API Documentation – platform.openai.com/docs
OpenAI Pricing – openai.com/pricing
Azure OpenAI Service – azure.microsoft.com
OpenAI Cookbook – cookbook.openai.com

Have questions about integrating GPT-4 Turbo into your applications? Connect with me on LinkedIn to discuss your use case.

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What Is GPT-3.5 or GPT-4 or GPT-4 Turbo? Everything You Should Know

Table of Contents

What is GPT-4 Turbo?

The GPT Model Family (As of January 2024)

GPT-4 vs. GPT-4 Turbo

How GPT-4 Turbo Works

Architecture Improvements

Key Technical Features

How to Access GPT-4 Turbo

1. ChatGPT Plus ($20/month)

2. OpenAI API

3. Azure OpenAI Service

Model Names Reference

Is GPT-4 Turbo Free? Pricing Differences Between GPT Variants

Is GPT-4 Turbo Free?

API Pricing Comparison (January 2024)

Real-World Cost Examples

GPT-4 Turbo for Developers

Best Practices

Integration Patterns

Benefits of GPT-4 Turbo

1. Massive Cost Reduction

2. 128K Context Window

3. Updated Knowledge Cutoff (April 2023)

4. JSON Mode for Reliable Structured Output

5. Reproducible Outputs

6. Improved Function Calling

7. Faster Response Times

Drawbacks of GPT-4 Turbo

1. Still in Preview

2. Knowledge Still Has a Cutoff

3. Rate Limits

4. Not Always Better Than GPT-3.5

5. Potential for Hallucinations

6. Context Window Cost

Conclusion

References & Further Reading

Discover more from Code, Cloud & Context

Leave a Reply

Searching in

Table of Contents

What is GPT-4 Turbo?

The GPT Model Family (As of January 2024)

GPT-4 vs. GPT-4 Turbo

How GPT-4 Turbo Works

Architecture Improvements

Key Technical Features

How to Access GPT-4 Turbo

1. ChatGPT Plus ($20/month)

2. OpenAI API

3. Azure OpenAI Service

Model Names Reference

Is GPT-4 Turbo Free? Pricing Differences Between GPT Variants

Is GPT-4 Turbo Free?

API Pricing Comparison (January 2024)

Real-World Cost Examples

GPT-4 Turbo for Developers

Best Practices

Integration Patterns

Benefits of GPT-4 Turbo

1. Massive Cost Reduction

2. 128K Context Window

3. Updated Knowledge Cutoff (April 2023)

4. JSON Mode for Reliable Structured Output

5. Reproducible Outputs

6. Improved Function Calling

7. Faster Response Times

Drawbacks of GPT-4 Turbo

1. Still in Preview

2. Knowledge Still Has a Cutoff

3. Rate Limits

4. Not Always Better Than GPT-3.5

5. Potential for Hallucinations

6. Context Window Cost

Conclusion

References & Further Reading

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