GPT-5.4 Developer Guide: Standard, Thinking, and Pro Compared — 1M Context, Native Computer Use, and Tool Search Explained (2026)

Feed a million tokens into a single prompt, then watch the AI take over your desktop and run a multi-step workflow on its own — that was science fiction three weeks ago. GPT-5.4 just made it real.

On March 5, 2026, OpenAI did something unprecedented: they launched GPT-5.4 across ChatGPT, the API, and Codex simultaneously — a first in the company’s history. Three variants (Standard, Thinking, Pro), a 1.05-million-token context window, native computer-use capabilities, and a brand-new Tool Search system for agent workflows. Whether you’re building production AI agents or just trying to figure out which tier is worth your money, this guide covers everything developers need to know about GPT-5.4.

What Makes GPT-5.4 Different

GPT-5.4 isn’t just another incremental update. It merges the industry-leading coding capabilities of GPT-5.3-Codex with improved reasoning, agentic workflows, and tool management into a single frontier model. OpenAI is essentially consolidating what used to be separate specialized models into one unified system. The triple launch across ChatGPT, the API, and Codex — all on the same day — signals that OpenAI now views these three surfaces as a unified platform rather than separate products.

The numbers speak for themselves. Compared to GPT-5.2, factual errors dropped by 33% on individual claims, and overall responses are 18% less likely to contain errors. On the BrowseComp benchmark (agentic browsing), GPT-5.4 jumped from 65.8% to 82.7%, while the Pro variant hit 89.3%. Perhaps most impressively, GPT-5.4 scored 75% on OSWorld — a desktop automation benchmark where human average is 72.4%. For the first time, a general-purpose AI model can operate computers better than the average person.

OpenAI also claims GPT-5.4 is their most token-efficient reasoning model yet, using significantly fewer tokens to solve problems than GPT-5.2. That efficiency gain compounds across every API call, which matters enormously at scale.

GPT-5.4 Standard vs Thinking vs Pro: Complete Comparison

GPT-5.4 ships in three variants, each targeting different use cases and budgets. Choosing the right one can mean the difference between a cost-effective pipeline and an unnecessarily expensive one.

The key distinction: Standard and Thinking are the same base model accessed through different interfaces. Standard is for API developers, while Thinking is the ChatGPT consumer/team interface with user-friendly reasoning mode controls (Light, Standard, Extended, Heavy for Pro subscribers). The Pro variant is a separate, higher-performance model — 12x the input cost of Standard, but it delivers measurably better results on the hardest problems.

One important pricing detail: once your prompt exceeds 272K tokens, the input rate doubles to $5.00/1M. Cached input tokens are charged at just 10% of the standard rate, so caching strategies become critical for cost management in agent workflows with repetitive system prompts.

The 1-Million-Token Context Window in Practice

GPT-5.4’s 1,050,000-token context window is the largest OpenAI has ever offered commercially — 922,000 input tokens and 128,000 output tokens. But raw numbers only matter if you know how to use them effectively.

• Full codebase analysis: Load an entire mid-sized project (hundreds of files) in a single call and get refactoring plans, architecture reviews, or migration strategies
• Long-document processing: Analyze hundreds of pages of legal contracts, technical specifications, or research papers in one shot — no chunking needed
• Multi-turn agents: Maintain extensive conversation history while performing complex multi-step tasks without losing context
• RAG alternative: For some use cases, feeding the entire document into context can be more accurate than a RAG pipeline, especially when relationships between distant sections matter

However, bigger context doesn’t always mean better results. The 272K surcharge threshold means that precise context curation — extracting only the information you actually need — often wins on both cost and accuracy. Think of the million-token window as a ceiling, not a target.

Native Computer Use: A New Frontier for AI Agents

GPT-5.4 is OpenAI’s first general-purpose model with native computer-use capabilities. It can see screens, move cursors, click buttons, type text, and execute multi-step workflows autonomously. The 75% score on OSWorld — surpassing the human average of 72.4% — marks a genuine inflection point.

For developers, the implications are significant. Tasks that previously required building custom API integrations for each application can now be handled by a single computer-use agent. Legacy system automation, cross-application workflows, and interactions with software that lacks an API are all suddenly within reach.

That said, computer use is currently available through the API and Codex only — not directly in the ChatGPT consumer interface. And when deploying computer-use agents in production, security is paramount. Always run them in sandboxed environments with minimal permissions. The capability is powerful, but it demands careful architectural decisions around access control and error handling.

Tool Search: Cutting Agent Token Costs by 47%

The Tool Search system introduced with GPT-5.4 solves a fundamental problem in agent development. Previously, every tool definition had to be included in the prompt upfront, causing token costs to balloon as tool ecosystems grew.

Tool Search flips this model. Instead of receiving all tool definitions at once, the model gets a lightweight list of available tools plus a search capability. When it needs a specific tool, it looks up that tool’s full definition and appends it to the conversation at that moment. On Scale’s MCP Atlas benchmark — 36 MCP servers, 250 tasks — this approach reduced total token usage by 47% while maintaining identical accuracy.

This is a game-changer for enterprise agents with dozens of API connectors. No more paying to load every tool schema on every call. The latency and cost savings compound dramatically as your tool ecosystem grows, making previously impractical agent architectures suddenly viable.

GPT-5.4 Mini and Nano: The Economics of Sub-Agents

On March 17, OpenAI followed up with GPT-5.4 mini and nano — lightweight variants optimized for speed and cost efficiency.

Mini is free in ChatGPT for Free and Go tier users. Nano is API-only. The $0.20/1M input pricing on nano is remarkable — as one developer pointed out, you can describe 76,000 photos for just $52.

The most effective strategy in practice is an orchestrator-subagent architecture: GPT-5.4 Standard or Pro handles complex planning and decision-making, while mini or nano takes care of repetitive tasks like classification, data extraction, and validation. This layered approach maintains quality where it matters while dramatically reducing overall costs.

Practical Developer Tips for GPT-5.4

Here are the key considerations when bringing GPT-5.4 into your production stack. These tips are based on the pricing structure, benchmark results, and architectural patterns that have emerged in the first weeks since launch.

• Dynamic reasoning effort: The API supports reasoning.effort from none to xhigh. Don’t default to xhigh for everything — match the reasoning level to the task complexity and save significantly on token costs
• Batch API for async workloads: Any task that doesn’t need real-time responses (bulk analysis, content generation, data processing) can be run through the Batch API at a 50% discount
• Cache aggressively: Cached input tokens cost 10% of standard pricing. For agent workflows with repeated system prompts and tool definitions, this alone can cut costs by more than half
• Implement Tool Search early: If your agent uses 10+ tools, Tool Search pays for itself immediately. The 47% token reduction compounds over every call
• Sandbox computer use: Native computer use is powerful but demands strict security. Always run computer-use agents in isolated environments with minimal permissions — never on production systems with broad access

One more thing worth noting: GPT-5.4 also introduced ChatGPT for Excel and Google Sheets in beta, allowing ChatGPT to be embedded directly within spreadsheets for financial modeling and data analysis. While this is more of a consumer feature, it hints at the broader direction OpenAI is taking — embedding AI directly into existing professional tools rather than requiring users to come to a separate chat interface.

For complete API documentation and parameters, check the official GPT-5.4 model docs. For the latest pricing details, see the OpenAI pricing page.

The Bottom Line: Which GPT-5.4 Variant Should You Use?

GPT-5.4 isn’t just a model upgrade — it’s a platform shift. The combination of a million-token context, native computer use, and Tool Search fundamentally expands what AI agents can do. For most workloads, Standard delivers excellent results at a reasonable price. Pro is worth the 12x premium only for the hardest problems where marginal accuracy gains have real business value. And mini plus nano make the orchestrator-subagent pattern economically viable at any scale.

The developers who will get the most out of GPT-5.4 are the ones who think carefully about which variant handles which part of their pipeline. Understanding the tradeoffs between cost, speed, and capability across all five models is the core skill for AI engineering in 2026. If you’re designing an agent architecture or automation system and want to talk through the tradeoffs, feel free to reach out below.

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