GPT-5.4 Is Here — What It Means for AI Agent Developers

OpenAI just released GPT-5.4 — their most capable model to date. It combines the coding strengths of GPT-5.3-Codex with major improvements in reasoning, computer use, and tool calling. Available now in the API as gpt-5.4 and in ChatGPT as GPT-5.4 Thinking.

Here’s what matters if you’re building AI agents.

Native Computer Use

GPT-5.4 is OpenAI’s first general-purpose model with native computer-use capabilities. It can operate computers through screenshots and keyboard/mouse commands, or via code libraries like Playwright.

The numbers are impressive:

• OSWorld-Verified: 75.0% (up from 47.3% with GPT-5.2, surpassing human performance at 72.4%)
• WebArena-Verified: 67.3% for browser-based tasks

For agent frameworks like Pydantic AI and LangChain, this opens up a new class of workflows — agents that don’t just call APIs but interact with actual software interfaces. Combined with sandboxed environments (like Docker or Daytona), computer-use agents become viable for production.

1M Token Context Window

GPT-5.4 supports up to 1 million tokens of context in the API. For long-running agents, this is transformative — an agent can hold an entire codebase, a full document set, or hours of conversation history without hitting context limits.

That said, context management still matters. Our experience with summarization-pydantic-ai shows that even with large context windows, intelligent compression (LLM summarization + sliding window) produces better results than dumping everything into context. The model attends more carefully to recent, relevant information.

Tool Search — The Biggest API Change

This is the sleeper feature. Previously, all tool definitions were included in every API request. With dozens of tools, that’s thousands of tokens per call — expensive and slow.

GPT-5.4 introduces tool search: the model gets a lightweight list of available tools and can look up full definitions on demand. OpenAI reports 47% fewer tokens on tool-heavy workloads with identical accuracy.

For frameworks that compose many toolsets — like pydantic-deepagents where a single agent can have 30+ tools from filesystem, planning, sub-agents, middleware, and more — this directly translates to lower cost and faster responses.

Coding Improvements

GPT-5.4 matches or beats GPT-5.3-Codex on coding benchmarks:

• SWE-Bench Pro: 57.7% (vs 56.8%)
• Terminal-Bench 2.0: 75.1%

More importantly, it’s significantly more token-efficient — solving problems with fewer reasoning tokens than GPT-5.2. For agent loops where the model iterates on code (edit → run → fix), fewer tokens per iteration means lower costs and faster cycles.

Better Tool Calling

On Toolathlon, which tests multi-step tool use, GPT-5.4 scores 54.6% (vs 45.7% for GPT-5.2) — and does it in fewer turns. Better tool calling accuracy with less back-and-forth directly improves agent reliability.

For developers using lifecycle hooks (like those in pydantic-ai-middleware) to track cost and audit tool calls, fewer unnecessary calls means cleaner logs and lower budgets.

Pricing

GPT-5.4 costs more per token but uses fewer tokens:

Model	Input	Cached Input	Output
gpt-5.2	$1.75/M	$0.175/M	$14/M
gpt-5.4	$2.50/M	$0.25/M	$15/M

With tool search and more efficient reasoning, the total cost per task may actually decrease for tool-heavy agents.

What This Means for Our Stack

We’re already testing GPT-5.4 across our tooling:

• pydantic-deepagents: The 1M context window means fewer summarization triggers and longer autonomous runs. Tool search could reduce prompt overhead by 40%+ for our full toolset.
• pydantic-ai-backend: Computer-use capabilities open the door for browser-based testing within sandboxes.
• Full-Stack AI Agent Template: GPT-5.4 works as a drop-in replacement — just change the model string. Try it in the web configurator.

The model is available now. If you’re building agents with Pydantic AI, LangChain, or LangGraph, GPT-5.4 is worth testing today — especially for tool-heavy and long-running workflows.

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Vstorm builds production AI agent systems. We maintain 10+ open-source packages for the Pydantic AI ecosystem.