OpenAI¶

Overview¶

OpenAI has been at the forefront of LLM development since introducing the transformer-based GPT series. From GPT-1 to GPT-4, OpenAI's models have set the standard for large language models.

OpenAI Model Timeline¶

timeline
 title OpenAI Model Evolution

 2018: GPT (117M)
 2019: GPT-2 (1.5B)
 2020: GPT-3 (175B)
: Codex (released)
 2021: DALL-E (1B visual)
 2022: ChatGPT Released
 2023: GPT-4 Released (frontier SOTA)
: GPT-4 Vision (multimodal)
: GPT-4 Turbo (better, cheaper)
 2024: GPT-4o (omni, live demo)
: GPT-4o mini
: Advanced reasoning models
 2025: o1 (reasoning focus)
: Enhanced multimodal

Model Portfolio¶

Frontier Models¶

GPT-4 & variants - State-of-the-art reasoning and capability
GPT-4 Turbo - Better performance at lower cost
GPT-4o - Optimized for production, multimodal

Lightweight Models¶

GPT-3.5-turbo - Good balance of performance and cost
GPT-4o mini - Efficient, lower latency

Specialized¶

Codex - Code generation (deprecated, features in GPT-4)
DALL-E 3 - Image generation
Whisper - Speech-to-text

Key Characteristics¶

Architecture¶

Decoder-only Transformer
Autoregressive generation
Extended context windows (128K for GPT-4)

Training Approach¶

Massive scale (100s of billions of parameters, estimate)
Constitutional AI approach
RLHF alignment
Continuous updates (knowledge cutoff regularly updated)

Capabilities¶

Excellent reasoning
Strong instruction following
Multimodal (GPT-4V, GPT-4o)
Extended context (128K tokens)
Function calling
Vision capabilities (GPT-4V+)

Limitations¶

API-only (no weights available)
Expensive ($0.03-0.10 per 1K tokens)
Rate limiting
Potential data privacy concerns

Model Details¶

Performance Comparison¶

Model	MMLU	HumanEval	GSM8K	Context	Cost
GPT-4	86.5%	92.3%	92%	8K-128K	High
GPT-4 Turbo	86.5%	91.2%	91%	128K	Medium-High
GPT-4o	88.7%	92.3%	95.2%	128K	Medium
GPT-3.5	71.3%	72%	57%	4K	Low
Codex	-	76.5%	-	2K	Deprecated

Pricing (2024)¶

GPT-4¶

Input: $0.03 per 1K tokens
Output: $0.06 per 1K tokens
Example: 1M input tokens = $30

GPT-4 Turbo¶

Input: $0.01 per 1K tokens
Output: $0.03 per 1K tokens
3x cheaper than GPT-4

GPT-4o (Optimized)¶

Input: $0.005 per 1K tokens
Output: $0.015 per 1K tokens
6x cheaper than GPT-4

GPT-3.5-turbo¶

Input: $0.0005 per 1K tokens
Output: $0.0015 per 1K tokens
60x cheaper than GPT-4

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Use Cases¶

When to Use OpenAI Models¶

Use GPT-4 When:

Maximum quality is critical
Complex reasoning required
Cost is secondary concern
Enterprise customers

Use GPT-4 Turbo When:

Long documents (need 128K context)
Better price-performance balance
Vision understanding needed

Use GPT-4o When:

Cost-conscious but need quality
Production deployment
Balanced performance/cost

Use GPT-3.5 When:

High volume, cost critical
Simple tasks
Real-time applications

Real-World Examples¶

# Example 1
# Use

response = openai.ChatCompletion.create(
 model="gpt-4",
 messages=[{
 "role": "user",
 "content": "Analyze this complex patent..." # Expert analysis needed
 }]
)

# Example 2
# Use

for customer_query in customer_queries:
 response = openai.ChatCompletion.create(
 model="gpt-3.5-turbo",
 messages=[{"role": "user", "content": customer_query}]
)

# Example 3
# Use

response = openai.ChatCompletion.create(
 model="gpt-4-vision-preview",
 messages=[{
 "role": "user",
 "content": [
 {"type": "text", "text": "What's in this image?"},
 {"type": "image_url", "image_url": {"url": "https://..."}}
]
 }]
)

# Example 4
# Use

long_document = open("50_page_document.txt").read()
response = openai.ChatCompletion.create(
 model="gpt-4-turbo-preview",
 messages=[{
 "role": "user",
 "content": f"Summarize this document:\n{long_document}"
 }]
)

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Detailed Model Guides¶

See individual model pages for comprehensive documentation:

Gpt 3 - Foundational 175B model
Gpt 3.5 - Enhanced variant with chat optimization
Gpt 4 & Variants - Frontier models with vision and extended context

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Current Status (2024-2025)¶

Market Position¶

Still leading on most benchmarks
Highest quality for complex reasoning
Most expensive - drives adoption to alternatives

Recent Updates¶

GPT-4o: Cheaper, faster, better quality (June 2024)
o1: Reasoning model with extended thinking (2024)
Vision improvements in GPT-4V
Function calling enhancements

Competitive Pressure¶

Claude 3 Opus approaching GPT-4 quality
Llama 3 405B very competitive at no cost
Mistral/Mixtral excellent open alternatives
Specialized models beating GPT-4 in domains

Future Direction¶

More efficient models (smaller, cheaper)
Better reasoning (o1 line)
Multimodal integration
Real-time capabilities
Domain-specific variants

Integration & Deployment¶

OpenAI API¶

import openai

# Set API key
openai.api_key = "sk-..."

# Create completion
response = openai.ChatCompletion.create(
 model="gpt-4",
 messages=[
 {"role": "system", "content": "You are a helpful assistant."},
 {"role": "user", "content": "Explain quantum computing"}
],
 temperature=0.7,
 max_tokens=500
)

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

OpenAI Python Client¶

from openai import OpenAI

client = OpenAI(api_key="sk-...")

message = client.messages.create(
 model="gpt-4",
 max_tokens=1024,
 messages=[
 {"role": "user", "content": "Hello!"}
]
)

print(message.content[0].text)

Function Calling¶

# Define tools your model can use
tools = [
 {
 "type": "function",
 "function": {
 "name": "get_weather",
 "description": "Get weather for a location",
 "parameters": {
 "type": "object",
 "properties": {
 "location": {"type": "string"}
 }
 }
 }
 }
]

# Model decides when to call tools
response = client.chat.completions.create(
 model="gpt-4",
 messages=[
 {"role": "user", "content": "What's the weather in Paris?"}
],
 tools=tools
)

# Handle tool calls
if response.choices[0].message.tool_calls:
 for tool_call in response.choices[0].message.tool_calls:
 if tool_call.function.name == "get_weather":
 # Call weather API
 pass

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Strengths & Weaknesses¶

Strengths¶

Best-in-class reasoning
Extensive documentation
Reliable API
Regular updates
Function calling
Vision capabilities
Extended context (128K)

Weaknesses¶

Very expensive ($0.06/1K tokens for GPT-4)
No open weights - can't fine-tune
API-only - no local deployment
Rate limits - throttle high-volume usage
Slower than local models (latency)
Privacy - data goes to OpenAI

Competitive Analysis¶

 Quality Speed Cost Privacy
GPT-4 
Claude 3 
Llama 70B 
Mistral 7B

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When Not to Use OpenAI¶

Don't use if:

Budget < $100/month (too expensive)
Privacy critical (data goes to OpenAI)
Need open-source/local deployment
High-volume inference (rate limits)
Real-time embedded systems

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References¶

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Last Updated: 2026-08-09