GPT-5.5 vs GPT-5.4: Which Model Should You Use for Real Work?

Where GPT-5.5 pulls ahead

GPT-5.5’s clearest advantage is in work that looks less like a simple chatbot exchange and more like a real project: writing code, researching, analysing data, using software tools and carrying multi-step tasks through to completion.

OpenAI calls GPT-5.5 its smartest model yet and says it is designed for complex work such as coding, research and data analysis across tools . CNBC’s coverage makes a similar point, describing GPT-5.5 as better at coding, using computers and pursuing deeper research capabilities .

CNET also frames GPT-5.5 as a general model that may be most useful for research and intensive tasks such as coding. It notes that the model has agentic capabilities and scored higher than GPT-5.4 on benchmarks measuring app use across a computer and math problem-solving .

The public benchmark numbers support that direction. OpenAI says GPT-5.5 reaches 84.9% on GDPval, which tests agents on well-specified knowledge work across 44 occupations; 78.7% on OSWorld-Verified, which measures whether a model can operate real computer environments on its own; and 98.0% on Tau2-bench Telecom, a benchmark for complex customer-service workflows, without prompt tuning .

Why GPT-5.4 is not obsolete

GPT-5.4 is not suddenly a weak model just because GPT-5.5 exists. OpenAI introduced GPT-5.4 as a frontier model that brings together advances in reasoning, coding and agentic workflows, while improving how the model works across tools, software environments and professional tasks involving spreadsheets, presentations and documents .

Its strongest case is controlled deployment. OpenAI’s prompt guidance says GPT-5.4 is designed for production-grade assistants and agents that need multi-step reasoning, evidence-rich synthesis and reliable performance over long contexts . The same guidance says GPT-5.4 performs especially well when prompts clearly define the output contract, tool-use expectations and completion criteria .

That matters in real systems. If your GPT-5.4 workflow already has carefully tuned prompts, tool calls, retrieval rules, evaluation criteria and fallbacks, switching models is not just a version-number upgrade. It can change tone, tool-use behavior, answer length, latency patterns and pass/fail rates on your internal tests. The practical move is to benchmark GPT-5.5 against your actual workload before replacing GPT-5.4.

What the benchmarks do — and do not — prove

The public data points generally favor GPT-5.5, but the details are not one-dimensional.

In healthcare-related evaluations, GPT-5.5 scores 56.5 on length-adjusted HealthBench, 2.5 points higher than GPT-5.4; 31.5 on HealthBench Hard, 2.4 points higher; and 51.8 on HealthBench Professional, 3.7 points higher. However, GPT-5.5 scores 95.6 on HealthBench Consensus, 0.7 points below GPT-5.4 . In other words, even within one broad evaluation area, the result depends on the exact test.

Cybersecurity evaluations show a similar need for caution. OpenAI’s system card says the UK AI Security Institute judged GPT-5.5 the strongest overall model on narrow cyber tasks, while also noting that the performance was within the margin of error . On expert-level narrow cyber tasks, GPT-5.5 scored 90.5% ± 12.9% pass@5, compared with 71.4% ± 19.8% for GPT-5.4 .

There is also a broader benchmarking caveat. In its GPT-5.4 launch material, OpenAI noted that benchmarks were conducted in a research environment and may produce slightly different output from production ChatGPT in some cases . So benchmarks are useful signals, but they are not a substitute for testing the model on your own prompts, tools, data and success criteria.

Practical recommendation

Choose GPT-5.5 first if you are starting a new project and need the strongest available capability for coding, research, data analysis or tool-heavy agent workflows. OpenAI’s own positioning and benchmark results put GPT-5.5 ahead for this kind of work .

Choose GPT-5.4, or benchmark carefully before switching, if you already run a production assistant or agent that has been tuned around GPT-5.4. OpenAI still describes GPT-5.4 as a fit for production-grade assistants and agents requiring multi-step reasoning, evidence-rich synthesis and reliable long-context performance .

The balanced takeaway: GPT-5.5 is the stronger model for most cases where maximum capability matters, especially when the task involves code, research, data analysis or multiple tools. GPT-5.4 remains a reliable production choice when the system has already been optimized around it. The right migration decision should come from real workload tests, not the model name alone.