GPT-5.5
vgpt-5-5-2026-04-24OpenAI
OpenAI's current flagship (codename 'Spud') and first fully retrained base model since GPT-4.5. ~1.05M context, 85.0% ARC-AGI-2, 93.6% GPQA Diamond, 58.6% SWE-Bench Pro. Designated migration target for most of the GPT-5.x line.
Trust Vector Analysis
Dimension Breakdown
🚀Performance & Reliability+
First fully retrained base since GPT-4.5. State-of-the-art across reasoning (85.0% ARC-AGI-2, 93.6% GPQA) and agentic coding (82.7% Terminal-Bench 2.0). ~40% more token-efficient than GPT-5.4.
Industry-standard coding and terminal benchmarks measuring real-world software engineering tasks
PhD-level science, frontier mathematics, and abstract reasoning benchmarks
Expert-comparison knowledge work and computer-use benchmarks
Internal consistency testing reported by provider across reasoning effort levels
Median latency for API requests with standard prompt sizes
95th percentile response time across diverse workloads
Official specification from provider
Historical uptime data from official status page
🛡️Security+
Mature multi-layer safety stack. Retrained base required full safety recalibration, which OpenAI reports as complete; long-tail agentic behaviors still being characterized by third parties.
Testing against OWASP LLM01 prompt injection attacks
Adversarial prompt testing against jailbreak datasets
Analysis of privacy policies and data handling practices
Safety testing across harmful content categories
Review of API security features and best practices
🔒Privacy & Compliance+
Standard OpenAI enterprise posture: SOC 2, no API-data training by default, 30-day default retention with zero-data-retention options.
Review of enterprise documentation
Policy review of data usage terms
Terms of service and enterprise documentation review
Review of data protection capabilities
Verification of compliance certifications
Enterprise feature review
👁️Trust & Transparency+
Strong transparency with reasoning summaries and detailed release documentation. Training data disclosure remains at industry-standard (limited) level.
Evaluation of reasoning transparency and explanation capabilities
Factual accuracy testing on QA datasets
Bias benchmarks and demographic testing
Qualitative assessment of confidence expression in outputs
Documentation completeness and clarity review
Review of public disclosures about training data
Analysis of built-in safety mechanisms
⚙️Operational Excellence+
Industry-leading operational maturity. As the designated GPT-5.x migration target, GPT-5.5 offers the longest expected support horizon in the OpenAI lineup.
Review of API design, consistency, and feature completeness
SDK quality, documentation, and maintenance review
Review of versioning policy and historical deprecation practices
Review of available monitoring tools and metrics
Support and documentation assessment
Ecosystem breadth and depth analysis
Review of licensing terms and restrictions
- +Industry-leading abstract reasoning: 85.0% ARC-AGI-2, 93.6% GPQA Diamond
- +State-of-the-art agentic coding: 58.6% SWE-Bench Pro, 82.7% Terminal-Bench 2.0
- +~1.05M token input context with 128K output
- +First fully retrained base since GPT-4.5 with ~40% fewer output tokens than GPT-5.4
- +84.9% GDPval on economically valuable knowledge work
- +Designated long-term migration target for the GPT-5.x line
- +Batch/Flex tiers at 50% discount
- !Premium pricing: $5/$30 per 1M tokens (2x GPT-5.4's base rate)
- !Not HIPAA eligible
- !30-day default API data retention (zero retention requires enterprise arrangement)
- !GPT-5.5 Pro is very expensive ($30/$180 per 1M)
- !Recently retrained base — long-tail behaviors less battle-tested than GPT-5.x predecessors
- !Training data transparency limited (industry standard)
Use Case Ratings
code generation
58.6% SWE-Bench Pro and 82.7% Terminal-Bench 2.0. ~1.05M context fits very large codebases. Codex variants remain preferable for dedicated agentic coding pipelines.
customer support
Token efficiency (~40% fewer output tokens) lowers cost per conversation. $5/$30 pricing is premium for high-volume support.
content creation
Retrained base produces concise, higher-quality drafts. Strong long-form coherence over very long contexts.
data analysis
93.6% GPQA and 51.7% FrontierMath T1-3 support rigorous quantitative work. ~1.05M context enables whole-dataset reasoning.
research assistant
84.9% GDPval on expert knowledge work with ~1.05M context for literature-scale inputs.
legal compliance
Strong document reasoning; SOC 2 and zero-data-retention options available, but not HIPAA eligible and 30-day default retention.
healthcare
Excellent clinical reasoning (93.6% GPQA) but not HIPAA eligible; privacy controls less strict than Anthropic's.
financial analysis
Frontier math performance (51.7% FrontierMath T1-3) and GDPval results support complex financial modeling.
education
Top-tier STEM reasoning with adjustable effort for tutoring at different depths. Reduced hallucinations vs prior generations.
creative writing
Strong narrative quality; conciseness bias from token-efficiency training can need prompting for expansive prose.