OpenAI dropped a significant update on March 5, 2026: GPT-5.4, a model built from the ground up for autonomous agent work. It ships with two things practitioners have been waiting for — native computer-use capabilities and a 1M-token context window in API preview. If you build agents, this changes your architecture options in real ways.
What Actually Shipped
GPT-5.4 comes in two variants:
- Standard GPT-5.4 — The default API model with native computer-use support and 1M-token context
- GPT-5.4 Pro — A higher-performance tier aimed at complex, long-horizon tasks
The model is available in ChatGPT, the Codex environment, and the API. Microsoft Foundry integration is also confirmed, meaning enterprise teams using Azure AI Foundry can access it without a separate onboarding.
The headline benchmark: GPT-5.4 scores 75% on the OSWorld-Verified benchmark — a rigorous test of computer control accuracy across real operating system environments. That’s a meaningful number because OSWorld-Verified doesn’t give partial credit for almost-right actions; it measures whether the agent actually completed the task.
How to Use GPT-5.4 Native Computer Use in Your Agent Workflows
Step 1: Access the API
GPT-5.4 is available through the standard OpenAI API. Update your SDK:
pip install --upgrade openai
For the 1M-token context window, request access via the OpenAI API dashboard — it’s in preview and may require allowlist access depending on your tier.
Step 2: Enable Computer Use Mode
The computer-use capability in GPT-5.4 works through a tool-use interface similar to how Claude’s computer use works. In your API call, include the computer-use tool in your tools array:
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-5.4",
messages=[
{
"role": "user",
"content": "Open the browser, navigate to our internal dashboard, and pull the daily active user count."
}
],
tools=[
{
"type": "computer_use",
"computer_use": {
"display_width_px": 1920,
"display_height_px": 1080,
"environment": "desktop"
}
}
]
)
GPT-5.4 will return tool calls with actions like screenshot, click, type, and scroll. Your agent harness executes these against the actual desktop environment and returns the results.
Step 3: Handle the Action Loop
The key pattern for computer use is a tight action-observation loop:
import base64
def run_computer_agent(client, task, desktop_env):
messages = [{"role": "user", "content": task}]
while True:
response = client.chat.completions.create(
model="gpt-5.4",
messages=messages,
tools=[computer_use_tool]
)
choice = response.choices[0]
# If the model is done, return the final message
if choice.finish_reason == "stop":
return choice.message.content
# Execute tool calls against the desktop
tool_results = []
for tool_call in choice.message.tool_calls:
action = tool_call.function.arguments # parsed JSON
result = desktop_env.execute(action) # your desktop controller
tool_results.append({
"tool_call_id": tool_call.id,
"role": "tool",
"content": result
})
messages.append(choice.message)
messages.extend(tool_results)
Step 4: Use the 1M Context Window for Long-Running Workflows
The 1M-token context is where this model shines for complex pipelines. Practical applications:
- Full codebase context: Ingest an entire project repo before asking the agent to make changes
- Long audit trails: Feed the agent the full log of a multi-hour session to reason about what happened
- Document processing pipelines: Load entire contracts, filings, or research papers without chunking
Be aware that 1M-token requests are significantly more expensive per call. For most agent tasks, a smaller context with good summarization is still the right call — but for tasks where full fidelity matters, the option is now there.
Step 5: Integrate with Microsoft Foundry (Enterprise)
If your team is on Azure, Foundry access means you can use GPT-5.4 without leaving your existing compliance boundary:
from openai import AzureOpenAI
client = AzureOpenAI(
azure_endpoint="https://YOUR_RESOURCE.openai.azure.com/",
api_version="2026-03-01",
api_key="YOUR_KEY"
)
# Same API surface — use model="gpt-5.4" or your deployment name
What This Means for Agent Builders
The 75% OSWorld score matters not because it’s perfect, but because it’s now high enough to trust in supervised workflows. You wouldn’t leave this running unattended on production systems — but for internal tooling, QA pipelines, and administrative tasks where a human can review the output, this opens up a lot of automation that wasn’t practical before.
The 1M-token context is less about chat and more about giving agents a working memory that scales to real-world complexity. The old workaround — chunking, summarizing, RAG-ing your way through long documents — still has its place, but there are tasks where full-fidelity context just works better.
Two versions (standard and Pro) also signal that OpenAI is thinking about cost-routing: use the cheaper model for straightforward tasks, escalate to Pro for complex ones. That’s a pattern worth building into your orchestration layer now.
What to Watch
OpenAI hasn’t published a full technical report for GPT-5.4 yet. The 75% OSWorld-Verified benchmark is confirmed across multiple outlets, but deeper capability comparisons (especially against Anthropic’s computer-use models) will emerge as practitioners run their own evals.
The 1M-token context window is in preview — plan for potential rate limits and pricing changes before building production workflows that depend on it.
Sources
- TechCrunch — OpenAI launches GPT-5.4 with Pro and Thinking versions (March 5, 2026)
- Neowin — OSWorld benchmark score of 75% verification (March 5, 2026)
- InterestingEngineering — GPT-5.4 computer-use capabilities coverage (March 5, 2026)
- OpenAI Blog — Official announcement (March 5, 2026)
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260305-2000
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