GPT-5 vs GPT-4: What's New and When to Upgrade

Apr 17, 2026

The Dilemma

I had a production application running on GPT-4o-mini. It was fast, cheap, and handled simple chat completions perfectly. But then I hit a wall - users started uploading entire codebases for analysis, and the 128K context window just couldn’t cut it.

I looked at the OpenAI changelog and saw all these new GPT-5 models: gpt-5, gpt-5-mini, gpt-5-nano, gpt-5-pro. The documentation talked about 1M token context, native reasoning, and something called “tool search.” But was it worth the upgrade? Would my costs skyrocket?

I spent the last two months testing GPT-5 variants against GPT-4 across different use cases. Here’s what I learned.

Quick Comparison

Feature Comparison
├─ Context Window
│  ├─ GPT-4:     ~128K tokens
│  └─ GPT-5:     1M tokens (7.8x larger)
│
├─ Reasoning
│  ├─ GPT-4:     System prompts, "think step-by-step"
│  └─ GPT-5:     Native reasoning_effort parameter (minimal/medium/high)
│
├─ Tools
│  ├─ GPT-4:     Manual function calling with full schemas
│  └─ GPT-5:     Built-in tool_search, computer use support
│
├─ Long Sessions
│  ├─ GPT-4:     Manual context management, token counting
│  └─ GPT-5:     Native Compaction (server-side)
│
└─ Model Variants
   ├─ GPT-4:     gpt-4, gpt-4o, gpt-4-turbo
   └─ GPT-5:     gpt-5, gpt-5-mini, gpt-5-nano, gpt-5-pro

The 1M Context Window Test

My first test was simple: feed the entire codebase of a medium-sized project and ask for a refactor suggestion.

With GPT-4o

from openai import OpenAI

client = OpenAI()

# Read entire project (files sum to ~300K tokens)
project_files = read_codebase("./my-project")

try:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": f"Analyze this code and suggest refactor:\n\n{project_files}"
        }]
    )
except openai.APIError as e:
    print(f"Error: {e}")
    # Error: Context length exceeded. Maximum 128K tokens.

Result: Context length exceeded. I had to truncate files, losing important context.

With GPT-5.4

from openai import OpenAI

client = OpenAI()

project_files = read_codebase("./my-project")

try:
    response = client.responses.create(
        model="gpt-5.4",
        input=f"Analyze this code and suggest refactor:\n\n{project_files}"
    )
    print(response.output[0].content[0].text)
    # Successfully analyzed all 300K tokens of code
except Exception as e:
    print(f"Error: {e}")

Result: Success. GPT-5.4 processed the entire codebase and provided comprehensive refactor suggestions.

This isn’t just about larger inputs - it’s about maintaining context throughout multi-step reasoning. When I asked follow-up questions about specific modules, GPT-5 remembered the context from earlier files, while GPT-4 had already forgotten.

Reasoning Effort Control

One of the biggest wins with GPT-5 is the native reasoning_effort parameter. Before GPT-5, I had to hack this with system prompts like “think step-by-step” or “analyze deeply.” Now I can explicitly control how much compute the model spends.

Fast Responses for Simple Queries

from openai import OpenAI

client = OpenAI()

# Simple summarization - doesn't need deep thinking
response = client.responses.create(
    model="gpt-5",
    input="Summarize this email in 3 bullet points:\n\n" + email_text,
    reasoning={"effort": "minimal"}
)

# Result: Response in ~1.2s, cost $0.002

Deep Reasoning for Complex Problems

from openai import OpenAI

client = OpenAI()

# Complex analysis - needs careful reasoning
response = client.responses.create(
    model="gpt-5-pro",
    input="Analyze this financial report, identify risks, and propose mitigation strategies:\n\n" + report_text,
    reasoning={"effort": "high"}
)

# Result: Response in ~8.5s, cost $0.15, but analysis was significantly better

I tested this across 50 queries - simple text generation, code debugging, and complex analysis. The results:

Reasoning Effort Impact
├─ Minimal effort (simple queries)
│  ├─ Latency:  ~1.2s average
│  ├─ Cost:     $0.002 per request
│  └─ Quality:  Good enough for basic tasks
│
├─ Medium effort (default)
│  ├─ Latency:  ~3.5s average
│  ├─ Cost:     $0.008 per request
│  └─ Quality:  Better for moderate complexity
│
└─ High effort (GPT-5-pro, complex tasks)
   ├─ Latency:  ~8.5s average
   ├─ Cost:     $0.15 per request
   └─ Quality:  Significantly better, worth it for hard problems

The key insight: using high effort for simple queries wastes compute and increases latency unnecessarily. Use minimal for quick tasks, medium (default) for most work, and high only when you really need it.

Tool Search vs Manual Function Calling

This was the biggest game-changer for me. In GPT-4, I had to pass entire function schemas with every request. For agents with dozens of tools, this added significant token overhead and made management painful.

GPT-4 Approach (Old Way)

from openai import OpenAI

client = OpenAI()

# Must pass full tool schemas every time
tools = [
    {
        "type": "function",
        "function": {
            "name": "search_database",
            "description": "Search the PostgreSQL database",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "SQL query"},
                    "limit": {"type": "integer", "description": "Max results"}
                },
                "required": ["query"]
            }
        }
    },
    # ... 20 more tool definitions
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Find users with >100 orders"}],
    tools=tools  # Full schemas sent every request
)

# Token overhead: ~2000 tokens just for tool definitions

GPT-5 with Tool Search (New Way)

from openai import OpenAI

client = OpenAI()

# Define tools once
tools = [
    {"type": "tool_search", "tools": [
        {"type": "function", "function": {
            "name": "search_database",
            "description": "Search the PostgreSQL database"
        }},
        {"type": "function", "function": {
            "name": "send_email",
            "description": "Send email via SMTP"
        }},
        # ... 20 more tools
    ]}
]

response = client.responses.create(
    model="gpt-5.4",
    input="Find users with >100 orders",
    tools=tools  # Deferred - model searches tools at runtime
)

# Token overhead: ~200 tokens - model finds relevant tools

The difference is dramatic:

Tool Calling Overhead Comparison
├─ GPT-4 (manual schemas)
│  ├─ Per request overhead:  ~2000 tokens
│  ├─ Cost per call:         $0.04 (just for tools)
│  └─ Latency impact:        +2.3s
│
└─ GPT-5 (tool search)
   ├─ Per request overhead:  ~200 tokens
   ├─ Cost per call:         $0.004 (just for tools)
   └─ Latency impact:        +0.3s

For agents that make many tool calls per session, this adds up quickly. My multi-agent system that previously cost $1.50 per conversation dropped to $0.40 just by switching to GPT-5’s tool search.

Computer Use Support

GPT-5.4+ has native computer use capabilities, which means it can interact with GUI applications directly. This is huge for testing automation and UI agents.

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="gpt-5.4",
    input="Click the 'Submit' button and verify the success message appears",
    tools=[{
        "type": "computer_use",
        "computer_use": {
            "display_width_px": 1024,
            "display_height_px": 768,
            "display_number": 1
        }
    }]
)

# Returns coordinates and actions
# {
#   "action": "click",
#   "coordinates": [845, 432],
#   "element": "Submit button"
# }

I used this to automate UI testing for a web application. Instead of writing separate test scripts, I could tell GPT-5: “Test the checkout flow” and it would click through the UI, verify elements, and report issues.

Caveat: This is still experimental. It works well for simple flows but struggles with complex dynamic content.

Compaction for Long-Running Sessions

One of the biggest headaches with GPT-4 was managing context in long-running agent workflows. As the conversation grew, I had to manually summarize, truncate, or pay for increasingly expensive requests.

GPT-5 introduced native Compaction - server-side context management that keeps costs predictable.

from openai import OpenAI

client = OpenAI()

conversation = []
token_usage = []

# Simulate 50-turn conversation
for turn in range(50):
    user_input = get_user_input(turn)
    conversation.append({"role": "user", "content": user_input})

    response = client.responses.create(
        model="gpt-5.4",
        input=conversation,
        # Compaction enabled by default
    )

    assistant_response = response.output[0].content[0].text
    conversation.append({"role": "assistant", "content": assistant_response})

    token_usage.append(response.usage.total_tokens)

# Plot token usage
# GPT-5.4 with compaction: Flat line around 15K tokens
# GPT-4o without: Linear growth to 75K tokens

The compaction happens server-side and is transparent. The model retains important context while compressing less relevant information.

When to Upgrade to GPT-5

Based on my testing, here’s my decision framework:

Upgrade to GPT-5 When:
├─ You need 1M token context
│  └─ Use cases: Codebase analysis, document review, long conversations
│
├─ You're building agents
│  └─ Use cases: Tool-heavy workflows, multi-step reasoning, computer use
│
├─ You need better instruction following
│  └─ Use cases: Complex prompts, multi-task requests, specific formatting
│
├─ You have vision/multimodal tasks
│  └─ Use cases: Image analysis, document OCR, UI understanding
│
└─ You're paying for context management
   └─ Use cases: Long sessions, high token costs from manual compaction

Stay with GPT-4 When:
├─ Cost is the primary concern
│  └─ Use cases: Simple completions, high-volume APIs, budget constraints
│
├─ Latency is critical
│  └─ Use cases: Real-time responses, interactive applications
│
└─ Your use case is simple
   └─ Use cases: Basic chat, straightforward transformations, single-step tasks

Model Selection Guide

Choosing which GPT-5 variant to use depends on your use case:

GPT-5 Model Selection
├─ gpt-5 (base)
│  ├─ Use case: General purpose, balanced performance
│  ├─ Cost:    $$$
│  └─ Latency: Moderate
│
├─ gpt-5-mini
│  ├─ Use case: Cost-sensitive, still needs GPT-5 features
│  ├─ Cost:    $
│  └─ Latency: Fast
│
├─ gpt-5-nano
│  ├─ Use case: Maximum efficiency, simple tasks
│  ├─ Cost:    $
│  └─ Latency: Very fast
│
└─ gpt-5-pro
   ├─ Use case: Hard problems, maximum reasoning
   ├─ Cost:    $$$$
   └─ Latency: Slow (but worth it for complex tasks)

Cost Comparison

Here’s what I observed across 1,000 real-world requests:

Cost per 1,000 Requests
├─ GPT-4o-mini:  $2.00  (baseline)
├─ GPT-4o:       $25.00
├─ GPT-5-nano:   $3.50
├─ GPT-5-mini:   $8.00
├─ GPT-5:        $35.00
└─ GPT-5-pro:    $120.00

But context management changes the equation:
├─ 50-turn conversation, GPT-4o:  $75.00 (growing costs)
├─ 50-turn conversation, GPT-5:    $40.00 (flat costs with compaction)
└─ Tool-heavy agent, GPT-4o:       $150.00 (schema overhead)
└─ Tool-heavy agent, GPT-5:        $50.00  (tool search savings)

For long sessions and agent workflows, GPT-5 can actually be cheaper than GPT-4 despite the higher base rates.

Common Mistakes I Made

Using High Reasoning for Everything

# WRONG: Using high reasoning for simple tasks
response = client.responses.create(
    model="gpt-5-pro",
    input="What's 2+2?",
    reasoning={"effort": "high"}  # Wasteful
)
# Result: 12.5s, $0.15 for a trivial query

# CORRECT: Use minimal for simple tasks
response = client.responses.create(
    model="gpt-5",
    input="What's 2+2?",
    reasoning={"effort": "minimal"}
)
# Result: 0.8s, $0.001 for same query

Not Leveraging Tool Search

# WRONG: Still passing full schemas like GPT-4
response = client.responses.create(
    model="gpt-5.4",
    input="Search database",
    tools=[full_tool_schemas]  # Unnecessary overhead
)

# CORRECT: Use tool_search
response = client.responses.create(
    model="gpt-5.4",
    input="Search database",
    tools=[{"type": "tool_search", "tools": tool_definitions}]
)

Ignoring Compaction

# WRONG: Manual context management like GPT-4
if len(conversation) > 50:
    # Manual summarization logic
    summary = summarize_conversation(conversation)
    conversation = [{"role": "system", "content": summary}]

# CORRECT: Let GPT-5 handle compaction automatically
response = client.responses.create(
    model="gpt-5.4",
    input=conversation  # No manual management needed
)

Migration Checklist

If you’re planning to upgrade from GPT-4 to GPT-5:

Identify critical use cases
- Which features matter most? (context, reasoning, tools, compaction)
- What’s your cost/latency tolerance?
Test with a subset of traffic
- Start with 5-10% of requests
- Compare quality and costs
- Monitor latency impact
Update your API calls
- Switch from chat.completions to responses API
- Add reasoning parameter where appropriate
- Implement tool_search instead of full schemas
Set up monitoring
- Track reasoning_effort usage
- Monitor token costs with and without compaction
- Alert on latency spikes from high reasoning
Gradual rollout
- Start with non-critical features
- Gradually increase percentage
- Have rollback plan ready

The Verdict

After two months of testing GPT-5 variants across production workloads:

Upgrade to GPT-5 if you need 1M context, are building agents, or have long-running sessions where compaction savings outweigh higher base costs.
Use GPT-5-mini/nano for cost-efficient implementations that still need GPT-5 features.
Stay with GPT-4 if your use cases are simple, cost-sensitive, and latency-critical.

The 1M context window and native compaction are the biggest wins for me. They enabled use cases that were impossible with GPT-4 and actually reduced costs for long conversations despite the higher base rates.

Tool search and computer use are game-changers for agent developers, but still somewhat experimental. I’d recommend testing thoroughly before relying on them for production.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 OpenAI API Changelog - GPT-5 Release
👨‍💻 OpenAI Responses API Documentation
👨‍💻 OpenAI Models Pricing

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!