In its April 23, 2026 announcement, NVIDIA reported that engineers who used to spend days chasing down a single bug started closing tickets in hours. The change came when the company swapped the underlying model in its internal Codex deployment to GPT-5.5. Ten thousand employees across product, legal, marketing, finance, and HR are now using the agentic coding app, and the internal chatter has shifted from cautious optimism to something closer to life-changing.

GPT-5.5 lands in Codex, runs on Nvidia GB200 NVL72

OpenAI announced that Codex — its agentic coding application — has been updated to the latest model, GPT-5.5. The model runs on Nvidia's GB200 NVL72, a rack-scale AI system. According to Nvidia, the GB200 NVL72 delivers 35x lower cost per million tokens and 50x higher tokens per second per megawatt compared to the previous generation. Those numbers translate to economics that make frontier-model inference practical at enterprise scale.

Nvidia has been running GPT-5.5-based Codex internally for several weeks, and the measurable results are already in. Debug cycles have collapsed from days to hours. Experiments that required weeks of work across multi-file codebases now complete overnight. Teams are shipping end-to-end features from natural language prompts, reporting higher reliability and fewer wasted work cycles than with the previous model.

The agent now handles what humans used to chase manually

Before this deployment, an engineer would dig through debug logs, navigate across files, and apply manual fixes. Now the Codex agent analyzes the entire codebase from a single natural language command and surfaces suggested fixes. Nvidia addressed security by provisioning every employee with a cloud virtual machine, so the agent operates in an isolated sandbox with access to company data. The Codex app connects to authorized cloud VMs through remote SSH connections, running under a data-no-retention policy with read-only permissions.

The OpenAI-Nvidia partnership traces back to 2016, when Jensen Huang personally delivered the first DGX-1 AI supercomputer to OpenAI's headquarters. Since then, the two companies have collaborated across the full AI stack. OpenAI has committed to deploying over 10 gigawatts of Nvidia systems — based on millions of Nvidia GPUs — for next-generation AI infrastructure. They also operate as early silicon and co-design partners: OpenAI's feedback feeds into Nvidia's hardware roadmap, and OpenAI gets early access to new architectures. A concrete output of this collaboration is the first 100,000-GPU cluster of GB200 NVL72 systems, which has already completed multiple large-scale training runs and set new benchmarks for system-level reliability.

Interpreting the cost comparison

The 35x claim compares infrastructure generations. It is not an OpenAI API price or a guaranteed discount on a customer bill. A practical evaluation should compare task success, retries, token usage and human review time under the same workload.

Correction — September 14, 2026: We removed an unrelated benchmark headline and unsupported pricing and configuration examples. This article reports NVIDIA's announcement; the deployment results are company-reported.