GPT-5 API Complete Guide for Developers (2026): Pricing, Features & Code Examples

Published: June 27, 2026 · 12 min read

Introduction

OpenAI's GPT-5, released in early 2026, represents the company's most ambitious model to date. With a 1 million token context window, a dedicated reasoning mode that rivals the best chain-of-thought models, and a pricing structure that undercuts GPT-4o on output tokens, GPT-5 has quickly become the preferred model for developers building production AI applications.

This guide covers everything you need to know about the GPT-5 API in 2026 — pricing, key features, comparison with competitors like DeepSeek V4 and Claude Opus 4, practical Python code examples, and how to access GPT-5 through TokenPAPA alongside other leading models.

Key insight: GPT-5 is the first OpenAI model to simultaneously offer a 1M context window, reasoning mode, structured outputs, and a real-time API — capabilities that previously required switching between different models. It effectively replaces GPT-4o, o1, and o3-mini as a single unified model.

GPT-5 Key Features

1 Million Token Context Window

GPT-5's 1M context is a game-changer. It's a 5x increase over GPT-4o (200K) and 25x over GPT-4 Turbo (32K).

This means you can pass entire codebases, full-length books, or hours of conversation transcripts in a single prompt without chunking or RAG.

Reasoning Mode

GPT-5 introduces a dedicated reasoning mode via the reasoning_effort parameter, replacing OpenAI's previous o1/o3 line:

• low — Fast reasoning for simple logic, classification, and routing
• medium — Default balanced reasoning for general problem-solving
• high — Deep chain-of-thought for math, science, and complex planning

Reasoning mode incurs additional output token costs but delivers significantly better results on multi-step logical deduction tasks.

Structured Outputs & Real-Time API

GPT-5 natively supports structured outputs via response_format with JSON Schema validation, eliminating manual parsing in production. It also powers OpenAI's Real-Time API with WebRTC support, enabling low-latency voice and text interactions for agentic applications.

GPT-5 Pricing

OpenAI introduced a dual pricing structure for GPT-5:

Comparison with Other OpenAI Models

GPT-5 in standard mode is 5x cheaper on input and output than GPT-4o. Even in reasoning mode, it matches GPT-4o pricing on output while being 20% cheaper on input.

GPT-5 vs Competitors

DeepSeek V4 Pro vs GPT-5

DeepSeek V4 Pro is 4-11x cheaper than GPT-5 in reasoning mode. For cost-sensitive workloads — batch processing, data extraction, code generation at scale — DeepSeek V4 Pro offers the best price-performance ratio. However, GPT-5 offers deeper reasoning and more robust tool-use integration for complex agentic applications. See our DeepSeek V4 Flash vs V4 Pro Guide for details.

Claude Opus 4 vs GPT-5

Claude Opus 4 at $15/$75 per 1M tokens is roughly 7.5x more expensive than GPT-5 reasoning mode. It targets safety-critical applications like legal analysis, medical diagnosis, and financial modeling. For general-purpose use, GPT-5 offers competitive reasoning at a fraction of the cost. See our LLM API Pricing Comparison 2026 for a broader view.

How to Use GPT-5 API with Python

GPT-5 uses the OpenAI Chat Completions API, compatible with the openai Python package. All examples use the TokenPAPA unified gateway.

Setup

pip install openai

Example 1: Basic Chat Completion (Standard Mode)

from openai import OpenAI

client = OpenAI(
    api_key="your-tokenpapa-api-key",
    base_url="https://api.tokenpapa.ai/v1"
)

response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "system", "content": "You are a senior software engineer."},
        {"role": "user", "content": "Explain the difference between Rust ownership and Go garbage collection."}
    ],
    temperature=0.7,
    max_tokens=500
)
print(response.choices[0].message.content)

Example 2: Reasoning Mode with Medium Effort

from openai import OpenAI

client = OpenAI(
    api_key="your-tokenpapa-api-key",
    base_url="https://api.tokenpapa.ai/v1"
)

response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "user", "content": "A bat and a ball cost $1.10. The bat costs $1.00 more than the ball. How much does the ball cost? Think step by step."}
    ],
    reasoning_effort="medium",
    max_tokens=2000
)
print(response.choices[0].message.content)

Example 3: Streaming with High Reasoning

from openai import OpenAI

client = OpenAI(
    api_key="your-tokenpapa-api-key",
    base_url="https://api.tokenpapa.ai/v1"
)

stream = client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": "Write a Python function that merges two sorted linked lists."}],
    reasoning_effort="high",
    stream=True,
    max_tokens=3000
)

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

Example 4: Structured Outputs with Pydantic

from openai import OpenAI
from pydantic import BaseModel

client = OpenAI(
    api_key="your-tokenpapa-api-key",
    base_url="https://api.tokenpapa.ai/v1"
)

class CalendarEvent(BaseModel):
    name: str
    date: str
    participants: list[str]

completion = client.beta.chat.completions.parse(
    model="gpt-5",
    messages=[{"role": "user", "content": "Schedule a team standup tomorrow at 10am with Alice, Bob, and Charlie."}],
    response_format=CalendarEvent,
)

event = completion.choices[0].message.parsed
print(f"Event: {event.name}, Date: {event.date}, Participants: {', '.join(event.participants)}")

Example 5: 1M Context — Analyze a Codebase

from openai import OpenAI
import os

client = OpenAI(
    api_key="your-tokenpapa-api-key",
    base_url="https://api.tokenpapa.ai/v1"
)

codebase = ""
for root, dirs, files in os.walk("./my_project"):
    for file in files:
        if file.endswith(".py"):
            path = os.path.join(root, file)
            with open(path, "r") as f:
                codebase += f"\n\n# --- {path} ---\n\n" + f.read()
                if len(codebase) > 800_000:
                    break
    if len(codebase) > 800_000:
        break

response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "system", "content": "You are an expert code reviewer."},
        {"role": "user", "content": f"Review this codebase for bugs and security issues:\n\n{codebase}"}
    ],
    reasoning_effort="high",
    max_tokens=8000
)
print(response.choices[0].message.content)

GPT-5 Use Cases

1. Advanced Coding Assistance

GPT-5's reasoning mode and 1M context enable architecture-level code review, refactoring suggestions, and security audits across entire repositories in a single prompt.

2. Long-Document Analysis

Legal contracts, academic papers, and full-length books can be analyzed in one shot without chunking. Reasoning mode excels at extracting nuanced arguments and cross-referencing sections across hundreds of pages.

3. Agentic Workflows

Native tool use, structured outputs, and real-time API support make GPT-5 ideal for production AI agents that plan, execute, and verify multi-step actions with high reliability.

4. Content Generation at Scale

At $0.50/$2 per 1M tokens in standard mode, GPT-5 is cost-effective for generating articles, documentation, marketing copy, and translations with structured output integration.

5. Data Extraction

JSON Schema-based structured outputs let you extract structured data from unstructured sources — emails, PDFs, web pages — with guaranteed valid JSON output.

Access GPT-5 via TokenPAPA

TokenPAPA is a unified LLM API gateway providing access to GPT-5 and 30+ models — including DeepSeek V4 Flash/Pro, Claude Sonnet 4 and Opus 4, Gemini 2.5 Pro/Flash, MiniMax, Moonshot, and more — through a single OpenAI-compatible endpoint.

Why TokenPAPA?

• Unified access — One API key for GPT-5, DeepSeek, Claude, Gemini, and 30+ providers
• No region restrictions — Access GPT-5 from anywhere in the world
• Flexible payments — PayPal, credit cards, and cryptocurrency accepted
• Competitive pricing — Same rates as direct providers, no minimum commitments

from openai import OpenAI

client = OpenAI(
    api_key="your-tokenpapa-api-key",
    base_url="https://api.tokenpapa.ai/v1"
)

response = client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": "What are the key trends in AI for 2026?"}],
    reasoning_effort="medium"
)
print(response.choices[0].message.content)

For a broader model comparison, see our Best LLM APIs in 2026 guide and the LLM API Pricing Comparison 2026.

FAQ

Is GPT-5 better than GPT-4o?

Yes. GPT-5 outperforms GPT-4o on virtually every benchmark, offers a 5x larger context window, introduces a dedicated reasoning mode, and is 5x cheaper in standard mode. OpenAI has positioned it as the direct successor to GPT-4o for all use cases.

Does GPT-5 support function calling?

Yes. GPT-5 fully supports function calling, parallel tool calls, recursive tool use for complex agentic workflows, and structured JSON output via response_format with JSON Schema validation.

How does reasoning mode affect pricing?

Reasoning mode raises output costs from $2/1M tokens (standard) to $10/1M (reasoning low/medium) or $15/1M (reasoning high). Internal chain-of-thought tokens are also billed at the output rate. Use standard mode for simple tasks and reasoning mode for complex problem-solving where superior results justify the cost.

Can I use GPT-5 for real-time voice applications?

Yes. GPT-5 powers OpenAI's Real-Time API with WebRTC support, enabling low-latency voice interactions. Its reasoning capabilities make it particularly effective for conversational agents that need to think before responding.

Getting Started with GPT-5

Ready to build with GPT-5? Sign up for TokenPAPA today and get instant access to GPT-5, DeepSeek V4, Claude, Gemini, and 30+ AI models — all through a single OpenAI-compatible API. No region restrictions, no minimum commitments, and flexible payment options.

Start building with GPT-5 on TokenPAPA →