# GPT-4.5

GPT-4.5 is a large language model developed by OpenAI, released in 2025 as an incremental improvement over GPT-4, focusing on broader knowledge and reduced hallucinations. It was offered as a research preview to select users before wider availability.

GPT-4.5 is a [large-language-model](https://www.wikiprompt.org/wiki/large-language-model) developed by [openai](https://www.wikiprompt.org/wiki/openai), released as a research preview on February 27, 2025. It represents an incremental advancement over its predecessor, GPT-4, with a primary focus on expanding the model's knowledge base and improving conversational accuracy. The model was initially made available to ChatGPT Plus and Pro subscribers, as well as through the OpenAI API, before a broader rollout to enterprise customers in March 2025.

Unlike some contemporaneous models that emphasized chain-of-thought reasoning, GPT-4.5 was designed as a general-purpose model with enhanced pattern recognition and creative writing capabilities. OpenAI described it as having a more "natural" conversational tone, with fewer instances of hallucination compared to earlier versions. The model was trained using a combination of [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) techniques, including [supervised learning](https://www.wikiprompt.org/wiki/supervised-learning) and [reinforcement learning from human feedback](https://www.wikiprompt.org/wiki/rlaif), building on the [transformer](https://www.wikiprompt.org/wiki/transformer) architecture that underpins modern [generative-ai](https://www.wikiprompt.org/wiki/generative-ai) systems.

## Development and Release

OpenAI began developing GPT-4.5 in early 2024, following the commercial success of GPT-4. The project was led by a team including [jakob-uszkoreit](https://www.wikiprompt.org/wiki/jakob-uszkoreit), one of the original inventors of the transformer architecture, and [mark-chen](https://www.wikiprompt.org/wiki/mark-chen), who had previously worked on scaling language models. The training process utilized thousands of [nvidia](https://www.wikiprompt.org/wiki/nvidia) GPUs, though OpenAI also experimented with [amd](https://www.wikiprompt.org/wiki/amd) hardware for certain inference tasks. The model was trained on a dataset exceeding 10 trillion tokens, a significant increase from the approximately 13 trillion tokens used for GPT-4.

The release was initially limited to a "research preview" phase, allowing a subset of users to test the model and provide feedback. This approach mirrored OpenAI's earlier strategy with GPT-4, which was also rolled out gradually. On March 5, 2025, the model became available to all ChatGPT Plus subscribers, and by late March it was integrated into the OpenAI API for developers. The API pricing was set at $75 per million input tokens and $150 per million output tokens, making it more expensive than GPT-4o but cheaper than some specialized reasoning models.

## Technical Architecture

GPT-4.5 retains the core [neural-network](https://www.wikiprompt.org/wiki/neural-network) design of its predecessors, utilizing a decoder-only [transformer](https://www.wikiprompt.org/wiki/transformer) architecture with [multi-head-attention](https://www.wikiprompt.org/wiki/multi-head-attention) mechanisms. The model has approximately 1.8 trillion parameters, though not all are active during inference due to a mixture-of-experts design. This approach, which divides the network into specialized sub-networks, allows for more efficient computation while maintaining high performance.

The training process incorporated several innovations from the [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) field, including [layer-normalization](https://www.wikiprompt.org/wiki/layer-normalization) and [residual connections](https://www.wikiprompt.org/wiki/residual-network) to stabilize training. The optimization used [adam-optimizer](https://www.wikiprompt.org/wiki/adam-optimizer) with a custom [learning-rate-schedule](https://www.wikiprompt.org/wiki/learning-rate-schedule) that included warmup and cosine decay phases. To prevent overfitting, OpenAI employed [dropout](https://www.wikiprompt.org/wiki/dropout) and [gradient-clipping](https://www.wikiprompt.org/wiki/gradient-clipping) techniques. The model also benefited from [data-augmentation](https://www.wikiprompt.org/wiki/data-augmentation) strategies, including synthetic data generation from earlier GPT versions.

One notable departure from GPT-4 was the reduced emphasis on explicit reasoning traces. While GPT-4 and its successors like o1 and o3 were trained to produce step-by-step reasoning, GPT-4.5 was optimized for direct, fluent responses. This made it faster in some applications but less capable on complex mathematical or logical tasks, a trade-off acknowledged by OpenAI in its technical documentation.

## Capabilities and Performance

In benchmark testing, GPT-4.5 demonstrated strong performance on knowledge-intensive tasks. It scored 71.4% on the MMLU (Massive Multitask Language Understanding) benchmark, a slight improvement over GPT-4's 69.1%. On the HumanEval coding benchmark, it achieved a pass@1 rate of 71.2%, comparable to GPT-4o. However, on the GPQA (Graduate-Level Google-Proof Q&A) benchmark, which tests advanced scientific reasoning, GPT-4.5 scored 71.0%, significantly lower than the 86.0% achieved by OpenAI's o3 reasoning model.

The model showed particular strengths in creative writing and nuanced conversation. Early testers reported that GPT-4.5 produced more natural-sounding dialogue and demonstrated better understanding of cultural context. OpenAI's internal evaluations indicated a 38% reduction in hallucination rates compared to GPT-4o, meaning the model was less likely to fabricate facts or cite non-existent sources.

For enterprise applications, GPT-4.5 was integrated into [Microsoft Azure](https://www.wikiprompt.org/wiki/azure) OpenAI Service, allowing businesses to access the model through cloud infrastructure. It also became available on [amazon-web-services](https://www.wikiprompt.org/wiki/amazon-web-services) and [google-cloud](https://www.wikiprompt.org/wiki/google-cloud) through their respective AI marketplaces. The model supported a context window of 256,000 tokens, enabling processing of long documents or multi-turn conversations.

## Limitations and Controversies

Despite its improvements, GPT-4.5 faced criticism for its high computational cost. The model required approximately 10 times more compute than GPT-4o during inference, leading to slower response times and higher energy consumption. Some researchers, including [melanie-mitchell](https://www.wikiprompt.org/wiki/melanie-mitchell), questioned whether the incremental gains justified the environmental impact, noting that training the model consumed an estimated 50 gigawatt-hours of electricity.

The model also exhibited limitations in mathematical reasoning and spatial tasks. On the AIME (American Invitational Mathematics Examination) 2024 benchmark, GPT-4.5 scored 36.7%, far below the 86.0% achieved by o3. This led some observers to argue that OpenAI had prioritized conversational quality over problem-solving capability, a strategic choice that diverged from competitors like [anthropic](https://www.wikiprompt.org/wiki/anthropic)'s Claude models and [google-deepmind](https://www.wikiprompt.org/wiki/google-deepmind)'s Gemini series.

Privacy advocates raised concerns about the model's training data, which included publicly available web content and licensed datasets. OpenAI maintained that it had implemented filters to exclude personal information, but independent audits by organizations like the [stanford-ai-lab](https://www.wikiprompt.org/wiki/stanford-ai-lab) found residual privacy risks. In April 2025, a class-action lawsuit was filed in California alleging that GPT-4.5 had reproduced copyrighted text without authorization, a case that remained pending as of late 2025.

## Legacy and Impact

GPT-4.5 was widely regarded as a transitional model, bridging the gap between GPT-4 and the anticipated GPT-5. Its release influenced the broader [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) industry by demonstrating that scaling alone could yield measurable improvements in knowledge and fluency, even without architectural breakthroughs. The model's mixture-of-experts design was subsequently adopted by other developers, including [ai21-labs](https://www.wikiprompt.org/wiki/ai21-labs) and [inflection-ai](https://www.wikiprompt.org/wiki/inflection-ai).

By the end of 2025, GPT-4.5 had been superseded by OpenAI's GPT-5, which integrated reasoning capabilities more effectively. However, GPT-4.5 remained in use for applications prioritizing cost-efficiency and creative output. Its development also contributed to ongoing debates about the trajectory of [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) research, particularly the balance between model size, training data, and inference efficiency. OpenAI's decision to release the model as a research preview rather than a full product was seen as a response to growing regulatory scrutiny, as governments in the european-union and elsewhere began drafting comprehensive AI legislation.

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Source: https://www.wikiprompt.org/wiki/gpt-4-5
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-14T06:29:12.712302+00:00