GPT-5 API: Complete Developer Guide (2026)

GPT-5 API: Complete Developer Guide (2026)

OpenAI's GPT-5 represents a significant leap in large language model capabilities, offering improved reasoning, a larger context window, native multimodal processing, and enhanced instruction following compared to its predecessors. This guide covers everything a developer needs to know to integrate the GPT-5 API into applications, from authentication and basic usage to advanced features and cost optimization.

GPT-5 Model Overview

Before diving into the API, here is what makes GPT-5 different from previous OpenAI models:

Feature	GPT-5	GPT-4o	GPT-4 Turbo
Context window	256K tokens	128K tokens	128K tokens
Max output tokens	32,768	16,384	4,096
Multimodal input	Text, images, audio, video	Text, images, audio	Text, images
Reasoning	Advanced chain-of-thought built-in	Standard	Standard
Knowledge cutoff	October 2025	October 2023	April 2023
Input cost (per 1M tokens)	$5.00	$2.50	$10.00
Output cost (per 1M tokens)	$15.00	$10.00	$30.00
Cached input cost	$2.50	$1.25	N/A

Pricing is approximate and subject to change. Check OpenAI's pricing page for current rates.

Getting Started

Step 1: Get Your API Key

1. Go to platform.openai.com.
2. Sign in or create an account.
3. Navigate to API Keys in your dashboard.
4. Click Create new secret key and copy the key.

Store it securely. You will not be able to view the key again after creation.

Step 2: Install the SDK

Python:

pip install openai

Node.js:

npm install openai

Verify the installation:

python -c "import openai; print(openai.__version__)"

You need version 1.60 or later for full GPT-5 support.

Step 3: Set Your API Key

Environment variable (recommended):

export OPENAI_API_KEY="sk-proj-your-key-here"

In code (not recommended for production):

from openai import OpenAI

client = OpenAI(api_key="sk-proj-your-key-here")

Basic Chat Completion

The core endpoint for GPT-5 is the same Chat Completions API you may already be familiar with.

Python Example

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "system", "content": "You are a senior software engineer. Be concise and precise."},
        {"role": "user", "content": "Write a Python function that implements binary search on a sorted list."}
    ],
    temperature=0.3,
    max_tokens=2048,
)

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

Node.js Example

import OpenAI from "openai";

const openai = new OpenAI();

const response = await openai.chat.completions.create({
  model: "gpt-5",
  messages: [
    { role: "system", content: "You are a senior software engineer. Be concise and precise." },
    { role: "user", content: "Write a TypeScript function that implements binary search on a sorted array." },
  ],
  temperature: 0.3,
  max_tokens: 2048,
});

console.log(response.choices[0].message.content);

cURL Example

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-5",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain quantum computing in three sentences."}
    ],
    "temperature": 0.7,
    "max_tokens": 512
  }'

Streaming Responses

For real-time output in chat interfaces or long-form generation, use streaming:

from openai import OpenAI

client = OpenAI()

stream = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "user", "content": "Write a detailed explanation of how garbage collection works in Go."}
    ],
    stream=True,
)

for chunk in stream:
    content = chunk.choices[0].delta.content
    if content:
        print(content, end="", flush=True)

Multimodal Input: Images

GPT-5 accepts images as part of the conversation. This is useful for code review from screenshots, diagram analysis, and visual Q&A.

response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What does this architecture diagram show? List all the services and their connections."},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/architecture-diagram.png",
                        "detail": "high"
                    }
                }
            ]
        }
    ],
    max_tokens=4096,
)

The detail parameter accepts low, high, or auto. Use low for simple images to reduce token usage.

Structured Output with JSON Mode

GPT-5 supports guaranteed JSON output, which is essential for building reliable API pipelines.

from pydantic import BaseModel

class CodeReview(BaseModel):
    issues: list[str]
    severity: str
    suggestion: str
    confidence: float

response = client.beta.chat.completions.parse(
    model="gpt-5",
    messages=[
        {"role": "system", "content": "You are a code reviewer. Analyze the code and return structured feedback."},
        {"role": "user", "content": "Review this: def add(a, b): return a + b + 1"}
    ],
    response_format=CodeReview,
)

review = response.choices[0].message.parsed
print(f"Issues: {review.issues}")
print(f"Severity: {review.severity}")

Function Calling (Tool Use)

GPT-5 has improved function calling with better accuracy and support for parallel tool calls.

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City and state, e.g., San Francisco, CA"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                },
                "required": ["location"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "search_flights",
            "description": "Search for available flights",
            "parameters": {
                "type": "object",
                "properties": {
                    "origin": {"type": "string"},
                    "destination": {"type": "string"},
                    "date": {"type": "string", "description": "YYYY-MM-DD format"}
                },
                "required": ["origin", "destination", "date"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "user", "content": "What's the weather in Tokyo and find me flights from NYC to Tokyo on March 15, 2026?"}
    ],
    tools=tools,
    tool_choice="auto",
)

# GPT-5 can call multiple tools in parallel
for tool_call in response.choices[0].message.tool_calls:
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

GPT-5 Model Variants

OpenAI offers several GPT-5 variants optimized for different use cases:

Model	Best For	Speed	Cost
gpt-5	General purpose, complex tasks	Medium	$5 / $15 per 1M tokens
gpt-5-mini	Fast responses, simple tasks	Fast	$0.50 / $1.50 per 1M tokens
gpt-5-turbo	Balance of speed and capability	Fast	$2.00 / $8.00 per 1M tokens

Choose the smallest model that handles your task well. Use gpt-5-mini for classification, extraction, and simple Q&A. Use the full gpt-5 for complex reasoning, code generation, and multi-step analysis.

Cost Optimization Tips

1. Use Prompt Caching

GPT-5 supports automatic prompt caching. Repeated prefixes in your messages are cached and charged at half the input rate.

# The system prompt below will be cached after the first request
system_prompt = "You are a medical coding assistant. You help classify ICD-10 codes based on clinical descriptions. Always return the code, description, and confidence level."

# First request: full input cost
# Subsequent requests with the same system prompt: cached input cost (50% off)

2. Set Appropriate max_tokens

Do not set max_tokens higher than needed. A lower value means the model stops sooner, saving output tokens.

3. Use Temperature 0 for Deterministic Tasks

For classification, extraction, and code generation where you want consistent results:

response = client.chat.completions.create(
    model="gpt-5",
    messages=[...],
    temperature=0,  # Deterministic output
)

4. Batch API for High Volume

For non-time-sensitive workloads, use the Batch API for 50% cost savings:

# Create a batch file with multiple requests
# Submit via the Batch API endpoint
# Results are returned within 24 hours at half the cost

Error Handling

Robust error handling is essential for production applications:

from openai import OpenAI, APIError, RateLimitError, APITimeoutError
import time

client = OpenAI()

def call_gpt5(messages, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model="gpt-5",
                messages=messages,
                timeout=60,
            )
            return response.choices[0].message.content

        except RateLimitError:
            wait_time = 2 ** attempt
            print(f"Rate limited. Retrying in {wait_time}s...")
            time.sleep(wait_time)

        except APITimeoutError:
            print(f"Request timed out. Attempt {attempt + 1}/{max_retries}")
            time.sleep(1)

        except APIError as e:
            print(f"API error: {e}")
            raise

    raise Exception("Max retries exceeded")

Rate Limits

GPT-5 API rate limits depend on your usage tier:

Tier	RPM	TPM	Daily Limit
Free	3	40,000	200 requests
Tier 1 ($5 paid)	60	200,000	No daily limit
Tier 2 ($50 paid)	200	1,000,000	No daily limit
Tier 3 ($100 paid)	500	2,000,000	No daily limit
Tier 4 ($250 paid)	1,000	5,000,000	No daily limit
Tier 5 ($1,000 paid)	5,000	20,000,000	No daily limit

RPM = Requests per minute. TPM = Tokens per minute.

Migrating from GPT-4o to GPT-5

If you are upgrading from GPT-4o, the migration is straightforward:

1. Change the model parameter from gpt-4o to gpt-5.
2. Review your max_tokens settings. GPT-5 supports up to 32,768 output tokens.
3. Test your prompts. GPT-5 follows instructions more precisely, which may mean existing prompts that relied on GPT-4o's looser interpretation might need adjustment.
4. Update your cost estimates. GPT-5 is priced at $5/$15 per million tokens compared to GPT-4o's $2.50/$10.
5. Take advantage of the larger 256K context window for tasks that previously required chunking.

Conclusion

The GPT-5 API brings meaningful improvements in reasoning, multimodal processing, and instruction following while maintaining backward compatibility with the existing OpenAI API format. The key to using it effectively is choosing the right model variant for your use case, implementing proper error handling, and optimizing costs through caching and appropriate token limits.

If you are building applications that need AI-generated media -- images, videos, talking avatars, or audio -- alongside your LLM integration, check out Hypereal AI. Hypereal offers a unified API for generative media models with pay-as-you-go pricing, making it easy to add visual and audio AI capabilities to any project.