GPT-5 vs GPT-4o API Pricing: The Complete Cost Breakdown

TL;DR

• GPT-5 API is up to 55–90% cheaper than GPT-4o across common use cases, thanks to lower per-token pricing and a 90% caching discount.
  
• GPT-5 supports 272K input tokens — over 2x the context capacity of GPT-4o — enabling full document, codebase, and conversation processing in one go.
  
• Performance beats price: GPT-5 outperforms GPT-4o in coding (75% vs 31%), math, multimodal reasoning, and factual accuracy.
  
• Best ROI for dev teams: A 10-person engineering team can save $7,200/year net using GPT-5 just on pull request reviews.
  
• Mini & Nano variants + Batch API allow ultra-cheap automation at scale — making GPT-5 the new default choice for startups and enterprises.

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Introduction

OpenAI’s August 2025 release of GPT-5 has shaken up the AI landscape — not just with performance upgrades, but with aggressive API pricing that undercuts the legacy GPT-4o model.

For the first time, developers get more capability for less cost, with GPT-5’s flagship, mini, nano, and “thinking” variants all coming in cheaper per token than GPT-4o equivalents. Add in a 90% caching discount and massive 272K token context window, and the economics for AI-powered products look very different than they did a year ago.

For businesses planning to integrate these models into production-ready apps, working with an OpenAI development company can help maximize both performance and cost efficiency — from choosing the right variant to implementing caching and batch processing at scale.

This guide breaks down every GPT-5 and GPT-4o API variant, subscription tiers, caching advantages, and real-world cost examples so you can choose the best model for your 2025 workloads.

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GPT-5 vs GPT-4o API Pricing at a Glance

Model	Input / 1M tokens	Cached Input / 1M	Output / 1M tokens	Notes
gpt-5	$1.25	$0.125	$10.00	Flagship GPT-5 model with full reasoning, multimodal capabilities, and best overall accuracy.
gpt-5-mini	$0.25	$0.025	$2.00	Lightweight GPT-5 for faster responses and lower cost; ideal for high-volume, mid-complexity tasks.
gpt-5-nano	$0.05	$0.005	$0.40	Cheapest GPT-5 tier; best for summarization, classification, and other simple workloads.
gpt-5-thinking	$1.25	$0.125	$10.00	Same pricing as GPT-5 but engages deeper multi-step reasoning; consumes more output tokens on complex queries.
gpt-5-thinking-mini	$0.25	$0.025	$2.00	Mini version with deeper reasoning mode; balances speed and cost with better accuracy than standard mini.
gpt-5-thinking-nano	$0.05	$0.005	$0.40	Nano tier with reasoning mode; adds more accuracy to lightweight tasks while keeping token costs minimal.
gpt-4o	$5.00	$2.50	$20.00	Legacy flagship GPT-4o; strong real-time multimodal performance but higher cost than GPT-5.
gpt-4o-mini	$0.60	$0.30	$2.40	Legacy GPT-4o mini tier; lower cost than full 4o but still more expensive than GPT-5 equivalents.

Key takeaway: Even at the same per-token rate, GPT-5 Thinking variants can cost more per request due to higher output usage — but still beat GPT-4o on performance per dollar.

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Major AI Model Cost Comparison:

ChatGPT 4o Plus vs. Pro

Deepseek vs ChatGPT Cost Comparison

Top AI Reasoning Model Cost Comparison 2025

Comparing OpenAI Models

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Subscription Tier Access & Pricing

Beyond the API, GPT-5 access varies across ChatGPT subscription tiers:

• Free: GPT-5 until usage cap, then GPT-5 Mini fallback.
  
• Plus ($20/mo): Higher caps, GPT-5 Thinking mode, file uploads, web browsing.
  
• Pro ($200/mo): Unlimited GPT-5 Pro, maximum reasoning depth, early access features.
  
• Team ($25/user): Enterprise controls, GPT-5 Pro, ChatGPT Agent tools, centralized billing.

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The 90% Caching Advantage

One of the most impactful cost-saving features in GPT-5’s API is OpenAI’s semantic caching system, which slashes the cost of repeated input tokens by a massive 90% — bringing the rate down to just $0.125 per 1M cached input tokens.

But this isn’t simple string-matching. It’s intelligent caching that recognizes semantically similar input tokens across requests — even if phrasing or structure varies slightly. That means the model doesn’t just save you money on exact duplicate inputs; it also rewards smart prompt design that reuses consistent instructions or base context.

This makes a huge difference in high-volume production scenarios:

• Customer service chatbots often begin conversations with the same greeting, policy outline, or brand intro. With GPT-5 caching, keeping that prompt consistent can reduce API spend by up to 70% while maintaining response quality.
  
• Code review tools that analyze multiple files from the same repository can reuse system prompts, development context, and even snippets of code — achieving 80% cache hits across interactions.
  
• Document processing pipelines benefit by batching documents of similar structure (e.g., invoices, resumes, contracts) and reusing instructions across tasks, resulting in a 60% reduction in API costs.

Pro Tip:

Structure your prompts for reuse. Keep your system message, formatting instructions, and core logic unchanged across calls. Let the user prompt or variable content change, but keep your foundation steady — OpenAI’s backend will do the rest.

The takeaway? GPT-5 doesn’t just outperform GPT-4o — it does it more affordably, especially when semantic caching is part of your architecture.

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Context Windows & Token Limits: Why GPT-5 Wins on Input Size

While GPT-5 and GPT-4o both support up to 128,000 output tokens, the real differentiator lies in input capacity — and GPT-5 comes out ahead by a wide margin.

• GPT-5: 272K input tokens + 128K output tokens
  
• GPT-4o: 128K input tokens + 128K output tokens

This means GPT-5 can ingest more than double the input compared to GPT-4o — making it the superior choice for use cases that demand deep context and long-form understanding.

Why input size matters:

• No more chunking hacks: Feed entire books, full codebases, legal documents, or multi-threaded chat history in one go.
  
• Smarter outputs: With more information upfront, GPT-5 delivers better reasoning, fewer hallucinations, and richer responses.
  
• Lower API complexity: Fewer requests needed to process large inputs, which also translates to lower cumulative costs.
  
• Better caching efficiency: Larger static prompts (e.g., base instructions or system messages) can be cached and reused at a 90% discount.

So while both models can generate equally long responses, GPT-5 offers more “brainpower” by allowing significantly more data to be considered before generating a reply — giving you performance and cost advantages for large-scale applications.

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Cost Per Use Case — Practical Savings in Action

Understanding per-token pricing is important, but seeing how it plays out in real-world scenarios is where the value of GPT-5 really shines. Below are three practical use cases comparing the cost of running workloads on GPT-5 vs GPT-4o, based on August 2025 API pricing.

Each example assumes standard monthly usage by startups or enterprises — giving you a direct look at how much you can save by switching to GPT-5.

Startup Chatbot (1M input + 1M output tokens/month)

For early-stage companies running customer support, AI onboarding flows, or product explainers via chatbot:

• GPT-5 Standard:
  
  • $1.25 (input) + $10 (output) = $11.25/month
    
• GPT-4o:
  
  • $5 (input) + $20 (output) = $25.00/month

Result: Save ~55% with GPT-5 — even while getting more advanced reasoning and better context handling.

Enterprise Code Assistant (5M output tokens/month)

For dev tools, IDE copilots, or code review platforms generating high volumes of output:

• GPT-5 Standard:
  
  • 5M output tokens × $10 = $50/month
    
• GPT-4o:
  
  • 5M output tokens × $20 = $100/month

Result: 50% cost reduction without sacrificing performance — GPT-5 handles full repositories better thanks to its larger input window.

Bulk Summarization Engine (10M input tokens/month)

For platforms processing news, legal docs, or academic content at scale, where generation is minimal but input is high:

• GPT-5 Nano:
  
  • 10M input tokens × $0.05 = $0.50/month
    
• GPT-4o Mini:
  
  • 10M input tokens × $0.60 = $6.00/month

Result: 91% savings with GPT-5 Nano — ideal for classification, tagging, and summarization at scale.

GPT-5 Wins When Inputs Are Repetitive

GPT-5’s 90% caching discount kicks in when the same or similar input tokens are reused across multiple API calls — like:

• Chatbots that repeat the same greeting or company info
  
• Customer support tools with templated instruction sets
  
• Code reviewers using the same system prompt for every PR
  
• Document parsers that batch similar file types

In these cases, GPT-5 can dramatically cut costs — up to 70–90% on those cached input tokens.

But What If Input Tokens Are Not Repeated?

Here’s where the game changes.

If every input is entirely unique and cannot benefit from caching, like:

• A user uploading completely different documents every time
  
• A system generating unique user prompts for every API call
  
• A language learning app with non-repeating custom lesson text
  
• An AI agent that reads and summarizes ad hoc internet content

👉 In these cases, GPT-4o Mini or GPT-4o might have an edge on pure input cost per token, especially for lower-usage apps.

Why?

Because GPT-4o Mini charges:

• $0.60 per 1M input tokens
  vs.
  
• GPT-5 Standard: $1.25 per 1M input tokens
  
• GPT-5 Nano: $0.05 — but with lower capabilities

So if your workload involves low reasoning + no reuse of prompts, GPT-4o Mini may be slightly cheaper, but less capable.

Scenario	Winner	Why
Repeated or shared input tokens	GPT-5	90% caching = major cost savings
High reasoning + long context	GPT-5	More context = smarter output
One-off, unique low-complexity input	GPT-4o Mini	Lower base input token price

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Performance Justification — Why Cheaper ≠ Weaker

From multiple benchmarks:

• Coding (SWE-bench): GPT-5 74.9% vs GPT-4o 30.8%
  
• Math (HMMT): GPT-5 96.7% vs GPT-4o ~70%
  
• Multimodal reasoning (MMMU): GPT-5 84.2% vs GPT-4o 72.2%
  
• Factuality: GPT-5 reduces hallucinations by ~45% vs GPT-4o

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ROI Analysis for Dev Teams & Businesses

GPT-5 isn’t just cheaper — it drives real productivity gains.

Take a dev team of 10 engineers saving 1 hour per week on pull request reviews with GPT-5. At $60/hr, that’s $31,200/year in time saved.

Even with GPT-5 API costs around $24,000/year, the net gain is $7,200/year — and that’s just one workflow.

Across coding, support, and content tasks, GPT-5 delivers measurable ROI with better output, faster turnaround, and lower total cost.

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Cost Optimization Strategies

Even with GPT-5’s aggressive pricing, smart usage can slash costs further:

• Route lightweight tasks to Mini/Nano:
  Use gpt-5-mini for general-purpose workloads and gpt-5-nano for simple tasks like classification, extraction, or summaries — saving up to 95% vs. standard GPT-5.
  
• Use the Batch API for non-urgent jobs:
  Process large volumes asynchronously with a 50% discount. Perfect for background summarization, translation, or report generation.
  
• Compress prompts and reuse base context:
  Remove redundant instructions and reuse common system prompts to reduce input tokens — especially when combined with caching.
  
• Structure for caching efficiency:
  OpenAI gives 90% off for repeated or semantically similar input tokens. Group similar tasks and maintain consistent prompt structures to maximize cache hits.

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Decision Matrix — Which Model to Choose?

Choosing the right model depends on your use case, budget, and latency needs:

Need	Best Choice	Why
Max reasoning at lowest input cost	GPT-5 or GPT-5 Thinking	High accuracy with same $1.25/1M input pricing
Real-time, multimodal speed	GPT-4o	Lowest latency, optimized for voice and vision
Cheapest bulk processing	GPT-5 Nano or GPT-5 Thinking Nano	Ultra-low $0.05/1M input cost ideal for classification & summaries

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Conclusion

GPT-5’s API lineup — including Mini, Nano, and Thinking variants — offers 30–40% cost savings over GPT-4o while outperforming it in reasoning, code generation, and multimodal tasks.

For most developers, product teams, and startups, GPT-5 is now the default high-ROI choice. And for complex use cases, the Thinking models justify their higher output usage with significantly better results.

🚀 Want to know which model is right for your use case?
Partner with our OpenAI development experts for a free cost simulation and consultation — and see exactly how much you can save with GPT-5.

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