GPT 5.5 API

GPT-5.5 is OpenAI's April 2026 frontier model for complex coding and professional work. It is built to carry more of a task from intent to completion: planning across ambiguity, using tools precisely, checking results, and continuing through long multi-step workflows instead of stopping after a partial answer.

iCreat provides gpt-5.5 through an OpenAI-compatible Chat Completions API. Developers can use familiar OpenAI Python or Node.js clients, authenticate with an iCreat API key, and choose complete or streamed responses without managing model infrastructure.

Model Positioning

GPT-5.5 succeeds GPT-5.4 with stronger agentic coding, computer-use reasoning, professional knowledge work, and early scientific analysis. It keeps the same 1.05M-token context window and 128K maximum output, but OpenAI reports higher task performance with fewer tokens and fewer retries on complex workflows.

GPT-5.5 also changes the reasoning baseline: medium is the default, while none remains available for latency-critical requests. Later GPT-5.6 models extend the family further, but GPT-5.5 remains a clear fit for teams that have evaluated this model, rely on its API behavior, or need its balance of high capability and controllable reasoning.

Key Capabilities

Long-Horizon Agentic Coding

GPT-5.5 is designed to navigate large systems, resolve ambiguous failures, coordinate tools, implement changes, and verify surrounding code. OpenAI reports stronger persistence and fewer premature stops than GPT-5.4 on extended engineering work.

Professional Task Execution

The model can turn incomplete business inputs into analyses, plans, reports, spreadsheet logic, and document-ready content. Its stronger outcome interpretation reduces the need to prescribe every intermediate step when the goal, constraints, and success criteria are clear.

Million-Token Context Retrieval

With a published 1.05M-token context window, GPT-5.5 can work across large repositories, research collections, policy sets, and multi-document evidence. OpenAI's long-context results show a larger improvement over GPT-5.4 as input length approaches the upper end of the window.

Controllable Reasoning Efficiency

Reasoning effort ranges from none to xhigh, with medium as the default. Teams can lower effort for responsive routine work or raise it for difficult coding, research, and multi-step decisions where measured quality gains justify additional latency and token use.

High-Detail Visual Understanding

GPT-5.5 accepts image input and improves default handling of detailed screenshots and visual documents. This supports interface analysis, visual quality checks, document interpretation, and computer-use workflows that depend on small on-screen details.

Pricing

iCreat bills GPT-5.5 by input, output, cache-read, and cache-write tokens:

Use Cases

• Repository-scale engineering: investigate cross-file behavior, implement broad changes, debug difficult failures, and validate the result.
• Tool-heavy business agents: coordinate multi-step customer service, operations, research, or internal knowledge workflows.
• Professional deliverables: transform mixed business material into analyses, implementation plans, report content, and spreadsheet-ready logic.
• Scientific research support: inspect datasets, identify confounders, design analyses, and turn expert questions into reproducible computational work.
• Visual software workflows: interpret dense screenshots, review interfaces, and reason about applications from visual state.

Production systems should still define tool permissions, approval boundaries, stopping conditions, and executable validation for actions with external or material consequences.

Model Comparison

GPT-5.5 vs GPT-5.4

GPT-5.5 vs Claude Opus 4.7 and Gemini 3.1 Pro

Why Choose GPT-5.5?

Choose GPT-5.5 when successful completion matters more than producing a fast first draft. It is particularly valuable when a request spans a large codebase or evidence set, requires several tool-assisted steps, or must turn loosely structured intent into a polished professional result.

For short classification, extraction, or routine chat, a smaller model may provide a better latency profile. For GPT-5.5 workloads, start with a representative evaluation set and choose the lowest reasoning effort that consistently meets the required quality bar.

Specifications

Architecture

OpenAI does not disclose GPT-5.5's parameter count or network architecture. The developer-visible design is a multimodal reasoning model with configurable effort, long-context processing, tool use, function calling, and structured output support.

GPT-5.5 was co-designed with the inference stack used to serve it on NVIDIA GB200 and GB300 NVL72 systems. This is an infrastructure detail rather than a disclosed model architecture; it helps explain how OpenAI maintained GPT-5.4-class per-token latency while increasing capability.

Production Notes

Treat GPT-5.5 as a model to evaluate independently rather than a drop-in replacement for GPT-5.4. Start from a lean prompt that defines the outcome, constraints, evidence requirements, allowed actions, stopping conditions, and output shape; remove inherited process instructions unless they encode a real product requirement.

Higher reasoning effort is not automatically better. Ambiguous instructions, weak stopping rules, or unrestricted tools can produce unnecessary work at higher settings. Compare quality, total tokens, retries, and end-to-end latency on the same production-like tasks.

For repeated long prompts, keep stable instructions and reusable context at the beginning and dynamic request data near the end to improve cache reuse. Long-running agents should preserve completed actions, tool results, active assumptions, unresolved blockers, and the next goal when compacting state.

FAQ

Can existing GPT-5.4 prompts be reused without changes?

They can be used as an evaluation baseline, but OpenAI recommends tuning GPT-5.5 as a new model family. Remove duplicated process instructions first, keep the product contract and success criteria, then compare the revised prompt against the existing version on representative tasks.

Which reasoning effort should I choose for GPT-5.5?

Start with the default medium. Test low for latency-sensitive work that still needs planning or tool use, and reserve high or xhigh for difficult tasks where evaluations show a measurable gain. Use none only when low latency matters more than multi-step reasoning.

Can GPT-5.5 analyze high-resolution interface screenshots?

Yes. GPT-5.5 accepts image input, and its auto image-detail behavior preserves more visual information than earlier defaults. For dense interfaces, keep text and controls legible, avoid unnecessary blank area, and test token use and accuracy with the screenshot sizes your application actually sends.

How should I verify GPT-5.5's token-efficiency advantage before migrating?

Run GPT-5.4 and GPT-5.5 on the same end-to-end tasks. Measure successful completion, human corrections, retries, tool calls, total input and output tokens, and wall-clock latency; fewer tokens are valuable only when the final result still passes the same acceptance criteria.