GPT-4.5 Preview

What GPT-4.5 Preview was

GPT-4.5 Preview was OpenAI’s large, general-purpose research-preview model, announced on February 27, 2025. OpenAI positioned it as a model improved through additional pre-training and post-training, with an emphasis on pattern recognition, broad world knowledge, creativity, instruction following, and natural interaction.

The model was intended for tasks such as writing, brainstorming, nuanced communication, coaching, programming assistance, practical problem solving, and agentic planning. “Preview” was important: OpenAI described GPT-4.5 as a model whose long-term availability and serving economics were still being evaluated rather than as a permanent, stable foundation model.

GPT-4.5 Preview is no longer part of OpenAI’s available API lineup. OpenAI deprecated the API model on April 14, 2025, and shut down API access on July 14, 2025. When announcing the deprecation, OpenAI recommended GPT-4.1 as the replacement. This makes GPT-4.5 Preview primarily relevant for historical comparison, documentation, and understanding OpenAI’s model progression rather than for new production integrations.

Purpose and positioning

GPT-4.5 Preview occupied the general-purpose side of OpenAI’s catalog. Its main distinction was not a specialized modality or a reasoning workflow, but the combination of broad capability, conversational naturalness, creative performance, and a large model size.

It was deliberately different from models such as OpenAI o1 and o3-mini. Those models were associated with explicit reasoning behavior in which the system spends additional effort working through difficult problems. GPT-4.5 Preview instead generated answers without a separate visible reasoning phase. That design could make it a natural fit for communication-heavy work, but it did not make it the preferred option for every difficult mathematical, analytical, or multi-step task.

The distinction is practical. A user asking for a polished explanation, alternative ideas, coaching-style feedback, or an interpretation of an image might prefer GPT-4.5’s conversational strengths. A user prioritizing deliberate reasoning, lower cost, faster responses, or long-term API availability would have stronger reasons to choose another model.

Capabilities and supported modalities

GPT-4.5 Preview accepted two input types: text and images. Image input allowed the model to interpret visual information alongside written instructions. Its output was text only; it did not natively generate images, audio, or video.

The verified API limits were a 128,000-token context window and a maximum output of 16,384 tokens. The context window is the amount of conversation, instructions, documents, and other tokenized content the model could consider in one request. The output limit is separate and controls how much text the model could return in a single response. These limits made the model suitable for substantial documents and extended conversations, although a large context does not guarantee that every detail will receive equal attention.

OpenAI documented support for function calling, which allows an application to provide defined operations that the model can request. For example, an application could expose a search, database lookup, or business workflow as a function and then execute it outside the model. Function calling did not mean that GPT-4.5 Preview independently controlled arbitrary software; the surrounding application remained responsible for validating and executing requests.

The model also supported structured outputs. This allowed developers to request responses that conform to a specified structure, which is useful when application code needs predictable fields rather than free-form prose. Structured outputs should not automatically be treated as proof of a separate legacy JSON-mode capability; the supplied documentation does not verify GPT-4.5 Preview’s JSON-mode status independently.

While available, GPT-4.5 Preview supported Chat Completions, Responses, Assistants, streaming, the Batch API, system messages, and prompt caching. It did not support fine-tuning or predicted outputs. Audio and video input were not supported, and the model had no image, audio, video, music, embedding, or speech output capability.

Pricing and economics

GPT-4.5 Preview’s final listed API pricing was:

These were token-based API prices, not a monthly subscription fee. Input tokens are the text and other supported request content sent to the model, while output tokens are generated response content. Cached input pricing applied when eligible repeated prompt content could be reused through prompt caching.

The pricing created a clear capability-versus-cost trade-off. GPT-4.5 Preview could be justified for high-value writing, creative work, nuanced interactions, or image interpretation where its quality was more important than minimizing spend. It was a poor fit for high-volume workloads that could use a less expensive model, especially because it was eventually retired.

Speed was also a consideration. The supplied editorial evaluation rates GPT-4.5 Preview’s speed as 3 out of 10 and its cost efficiency as 1 out of 10. These are editorial scores, not OpenAI-published benchmark results. They summarize the model’s historical position relative to contemporary alternatives: capable and broad, but expensive and not an obvious choice for fast, economical generation.

Main strengths

• Natural communication: GPT-4.5 Preview was designed to produce more natural, nuanced interactions, making it useful for explanation, coaching, drafting, and collaborative ideation.
• Broad general-purpose knowledge: It was intended to handle many different subject areas without being limited to one professional or technical workflow.
• Creative and writing tasks: Brainstorming, rewriting, narrative work, tone adjustment, and other language-focused tasks were central use cases.
• Vision input: The model could analyze images as part of a request, which extended its usefulness beyond text-only workflows.
• Application integration: Function calling, structured outputs, streaming, batch processing, and prompt caching provided building blocks for software integrations.
• Large context and output limits: The 128,000-token context window and 16,384-token output limit supported substantial prompts and long responses when the application needed them.

OpenAI also described GPT-4.5 as showing strong performance in general and coding-oriented evaluations relative to GPT-4o. The supplied editorial coding score is 7 out of 10, while the editorial reasoning score is also 7 out of 10. Neither score should be read as an official OpenAI rating or as evidence that GPT-4.5 Preview was a dedicated reasoning model.

Main limitations

• No dedicated reasoning process: GPT-4.5 Preview was not designed like OpenAI’s explicit reasoning models, so it was not the natural choice for tasks that benefit from deliberate multi-step analysis.
• High price: At $75 per million input tokens and $150 per million output tokens, it was substantially more expensive than many contemporary alternatives.
• Limited modalities: It accepted text and images but did not support audio or video input or non-text generation.
• No fine-tuning: Developers could not fine-tune the model according to the supplied specifications.
• Research-preview lifecycle: Its experimental status meant that long-term availability was uncertain from the beginning.
• Retirement: The API model is no longer available, so it should not be selected for a new integration.

When to choose this model

For a new project today, the practical answer is not to choose GPT-4.5 Preview because API access ended on July 14, 2025. Historical users may still study it when maintaining an old implementation, comparing model behavior, or evaluating why OpenAI positioned it differently from reasoning-focused models.

When it was available, GPT-4.5 Preview was most appropriate when response quality, conversational subtlety, creativity, and broad task coverage mattered more than price or speed. Examples included drafting and editing, brainstorming, coaching-style applications, image-based interpretation, programming assistance, and agent workflows that benefited from function calling and structured responses.

A less expensive general-purpose model was more appropriate for large-scale or budget-sensitive generation. A reasoning-oriented model was more appropriate when the central requirement was deliberate problem solving rather than fluent communication. A model with native audio or video support was required for those modalities, because GPT-4.5 Preview did not provide them. For current OpenAI API work, GPT-4.1 was the replacement OpenAI recommended when GPT-4.5 Preview was deprecated.

Technical summary

Overall, GPT-4.5 Preview was a historically significant OpenAI model because it emphasized broad knowledge and human-like communication without being an explicit reasoning model. Its technical limits were substantial for its time, but its high cost, preview status, and eventual retirement meant that its strengths were short-lived as a production advantage.