GPT-5.5
April 2026 breakthrough expanding context to 1-million tokens natively while achieving 86.5% on SWE-bench Verified at affordable $3/$12 economics.
1,000k
~750k words input
$3 / $12
Prompt / Completion per 1M
86.5 % resolved
SWE-bench Verified
Cloud Managed
Zero local VRAM
Holistic Capability Distribution
Normalized 0-100 rating vectors balanced across intelligence, throughput, and economics.
Verified LLM Benchmarks
Empirical evaluations across academic reasoning, software engineering, agentic execution, and human preference.
Inference Latency & Generation Throughput
Empirical telemetry benchmarking generation throughput (Tokens/Second) and Time to First Token (TTFT).
Measured under concurrent production load
p95 latency: 890 ms
Across 3,800 monitored requests
Architectural Strengths vs. Operational Considerations
An objective technical balance sheet analyzing deployment advantages and production constraints.
Key Architectural Strengths
- 1,000,000-token native context window with 100% recall accuracy.
- 86.5% SWE-bench Verified coding precision.
- Aggressive $3 / $12 pricing with $0.30 cache reads.
Operational Considerations
- Large prompt prefixes require prompt caching for optimal budget use.
Hardware & Execution Parameters
Genealogical Provenance
First 1M context foundation model in the GPT-5 series.
openai gpt 5 4
GPT-5.5
Architecture Engineering & Capability Deep-Dive
Topology & Attention Mechanics
High-order attention layers with constant-memory KV cache compression across 1M tokens.
Evaluation Profile & Reasoning Capabilities
Ingests entire codebases and multi-hundred-page regulatory filings simultaneously.
Inference Economics & Cost Optimization
$3 / $12 per 1M tokens.
Primary Production Workloads & Suitability
Cloud Deployment Architecture & Hardware Sizing
Cloud Deployment Architecture
Distributed enterprise serving infrastructure, autoscaling semantics, and runtime execution profile.
Proprietary commercial model hosted on managed multi-region clusters with automatic autoscaling, high availability, and prompt prefix caching.
Multi-Tenant Cloud API
Continuous autoscaling with regional failover
0 GB Local VRAM
Inference executed on hyperscaler accelerator clusters
1,000k tokens
Supported with automated KV cache prefix compression
SDK Installation
Official Repositories & Papers
Compare Showdowns
GPT-5.5 vs GPT-5.4
Compare GPT-5.5 against GPT-5.4's verified benchmarks, context window, and pricing.
GPT-5.5 vs GPT-5.5 Pro
Compare GPT-5.5 against GPT-5.5 Pro's verified benchmarks, context window, and pricing.
GPT-5.5 vs OpenAI Decisions
Compare GPT-5.5 against OpenAI Decisions's verified benchmarks, context window, and pricing.
GPT-5.5 vs GPT-3.5 Turbo
Compare GPT-5.5 against GPT-3.5 Turbo's verified benchmarks, context window, and pricing.
Commercial Token Rates & Cost Estimator
$3.00 / 1M
Standard prompt context ingestion
$12.00 / 1M
Generated completion & thinking tokens
$0.30 / 1M
Re-used prompt prefix context
Monthly Production Cost Simulator
$648.00
Includes prompt caching
-$162.00
Saved via prefix context caching
Comparable Foundation Architectures
Alternative foundation models with comparable intelligence benchmarks, context windows, and deployment profiles.
GPT-5.4
March 2026 foundation model standardizing 83.8% SWE-bench Verified coding precision, 500k context, and $3/$12 economics for enterprise agent automation.
GPT-5.5 Pro
April 2026 professional reasoning summit, combining 1-million-token context with 89.5% SWE-bench Verified and 93.8% GPQA Diamond scores.
OpenAI Decisions
OpenAI Decisions is OpenAI's dedicated high-throughput decision-making API powered by GPT-6 Luna, accessed via the POST /v1/decisions endpoint. Designed for agent routing, automated content moderation, and classification, it processes typed questions (predicates, categorical choices, and rubric scores) roughly 10x faster than standard generation, billed at $0.10 per million input tokens with zero output-token fees.
GPT-3.5 Turbo
OpenAI's foundational conversational model that launched the ChatGPT era, optimized for fast dialogue, summary generation, and lightweight JSON tasks.
SDK Installation & API Integration
import OpenAI from 'openai';
const client = new OpenAI();
const completion = await client.chat.completions.create({
model: 'gpt-5.5',
messages: [{ role: 'user', content: 'Ingest and index this whole repository architecture.' }],
});
console.log(completion.choices[0].message.content);Frequently Asked Questions about GPT-5.5
Engineering specifications, VRAM sizing, commercial licensing terms, token economics, and empirical benchmark evaluations.
Yes, GPT-5.5 supports 1,000,000 tokens of context natively with prompt caching.