GPT-5.5 Model Specification Sheet

Core Specifications

Full Name: GPT-5.5
Codename: “Spud”
Developer: OpenAI
Initial Release Date: April 23, 2026
Predecessor: GPT-5.4
Model Type: Multimodal generative pre-trained transformer

Technical Specifications

Architecture: Advanced transformer with improved training efficiency and reasoning optimization
Training: Large-scale training on NVIDIA GPU clusters with extensive post-training alignment
Latency Improvement: ~15% faster response time compared to GPT-5
Compute Efficiency: ~20% lower computational overhead than previous generation
Reasoning System: Enhanced multi-step planning and self-correction mechanisms

Performance & Benchmarks

Coding: Strong improvements in debugging, code generation, and automation tasks
Benchmark Example: ~82%+ accuracy on advanced coding benchmarks (Terminal-Bench class tasks)
Task Handling: Designed for complex, multi-step workflows and long-horizon reasoning
Reliability Focus: Emphasis on correctness, consistency, and reduced hallucination rates

Capabilities

Advanced reasoning across coding, research, and productivity domains
Improved tool use including browsing, coding environments, and structured workflows
Enhanced error detection and self-correction
Supports agent-like workflows for extended task execution

Variants & Modes

GPT-5.5 Standard: Default model for general use
GPT-5.5 Thinking: Extended reasoning mode for complex tasks
GPT-5.5 Pro: Higher-tier version with increased performance and access limits

Availability & Access

Available in ChatGPT for Plus, Pro, Business, and Enterprise tiers
Gradual rollout across Codex and developer tools
API access released in phases following initial launch
Not initially available to free-tier users

Use Cases

Software development and large-scale codebase management
Research assistance and data analysis
Enterprise productivity workflows
Automation and agent-based task execution

Design Philosophy

Shift from raw scale toward efficiency, reliability, and real-world usefulness
Optimized for enterprise deployment and sustained workloads
Focus on practical intelligence rather than purely benchmark-driven improvements

Limitations & Notes

API availability may lag behind ChatGPT rollout
Performance depends on tool integration and task complexity
Still subject to occasional inaccuracies despite improvements

Recent Highlights

Represents a refinement-focused upgrade emphasizing efficiency and reliability
Introduces stronger agent-like capabilities and planning
Marks a shift in AI development toward practical, production-ready systems