OpenAI’s latest model, GPT 5.5, represents a meaningful advancement across enterprise content use cases — demonstrating particular strength on tasks that require sustained, multi-step reasoning over complex documents.
In a head-to-head comparison against GPT 5.4, GPT 5.5 achieved a 10-percentage-point lead in overall agent accuracy, scoring 77% against 67% across weighted rubric items. This margin sets a new high-water mark for performance on the most challenging enterprise reasoning tasks.
Measuring end-to-end agent performance
The Box AI Complex Work Eval for GPT 5.5 measures how well a model performs across full agentic workflows, not just isolated prompt-response quality. This means testing across three distinct stages:
- Orchestration: Breaking down a complex task and deciding how to approach it
- Retrieval: Identifying and surfacing the right information from across a document set
- Answer generation: Synthesizing that information into a structured, accurate response
Tasks were deliberately designed to require heavy reasoning and span use cases including report drafting from data, due diligence, data analysis, and expert review/verification. For the complex work evaluation, we kept all factors constant about the agent and swapped in the models on their reasoning setting set to “high.”
This approach surfaces failure modes that single-turn benchmarks miss entirely. A retrieval miss at stage two doesn’t just affect one answer. It can cascade into a structurally flawed final output that no amount of strong generation can recover. Errors compound across interdependent decisions, and that’s precisely what this evaluation is designed to detect.
Where reasoning defines the frontier
GPT 5.5’s most significant results came from tasks requiring sustained, multi-step reasoning — those demanding chained logic, calculation, and synthesis across the full pipeline.
Performance by use case (GPT 5.5 vs. GPT 5.4):
- Report drafting from data: 81% vs. 76%
- Expert review/verification: 79% vs. 74%
- Data analysis: 78% vs. 61%
- Due diligence: 69% vs. 57%
The data analysis gap is the most striking. At 17 percentage points, it reflects what happens as task complexity increases — moving from structured review toward open-ended analysis and inference. These tasks require a model to not just retrieve and summarize, but actively transform and reconcile information across sources to reach a defensible conclusion.
Three concrete examples illustrate where GPT 5.5 pulls ahead.
- In a grading policy task, GPT 5.5 correctly categorized student assignments and computed weighted grades across all rubric items;GPT 5.4 miscalculated the weights, producing incorrect final scores for individual students
- In an entertainment IP analysis, GPT 5.5 extracted social media ratings and computed composite performance scores against YouTube view data; GPT 5.4 could not parse the numeric ratings from the source report and declined to calculate composite scores at all
- In a clinical care task, GPT 5.5 produced a complete peri-arrest protocol with the correct diagnosis and grounded interventions; GPT 5.4 identified the same diagnosis but delivered a substantially less thorough treatment plan
Specialized industry benchmarks
Performance is strongest in the domains where document complexity and reasoning demands are highest (GPT 5.5 vs. GPT 5.4):
- Financial services: 83% vs. 64%— the largest margin at 19 points
- Healthcare: 78% vs. 61%
- Public sector: 72% vs. 59%
- Media & entertainment: 70% vs. 57%
These are the domains characterized by dense, schema-rich documents — financial filings, clinical records, policy documents, and creative content — where GPT 5.5’s ability to hold chains of interdependent reasoning intact translates directly into accuracy gains.
Impact across enterprise use cases
- Financial services: Automate multi-year P&L projections from Year 1 baseline data with higher consistency and correct expense ratio application
- Healthcare: Generate clinical management plans and peri-arrest protocols (including correct diagnosis and grounded intervention sequences) directly from ventilator and medical reports
- Media & entertainment: Compute composite performance scores by synthesizing YouTube analytics and social media data to rank episode or IP performance
- Public sector: Apply complex, multi-step policies to raw datasets — accurately categorizing records and calculating weighted outcomes for large-scale compliance reporting
Get started today
The advanced reasoning and extraction capabilities of GPT 5.5 will be available for Box AI customers soon.
