An early look at OpenAI's next-generation flagship model, offering top-tier intelligence, a 128k context window, and multimodal support.
GPT-4.5 (Preview) represents the next evolutionary step in OpenAI's lineup of flagship models, building upon the formidable foundation of the GPT-4 series. As a 'preview' release, it offers developers and researchers an early opportunity to experiment with cutting-edge AI capabilities before they are widely available. This status implies that the model is still under evaluation, and its performance, features, and pricing are subject to change. It is designed for tasks that demand the highest echelons of reasoning, comprehension, and creative generation, positioning it as a tool for solving the most complex problems.
The model's standout feature is its raw intelligence. Scoring an exceptional 38 on the Artificial Analysis Intelligence Index, GPT-4.5 (Preview) places itself firmly in the top tier of all publicly benchmarked models. This score, significantly above the average of 15 for comparable models, indicates a profound ability to understand nuance, follow intricate instructions, and perform multi-step reasoning. This cognitive power is complemented by a massive 128,000-token context window. Such a large context allows the model to ingest and analyze entire documents, lengthy codebases, or extensive conversation histories in a single pass, enabling applications that require deep contextual understanding, from legal document review to complex software engineering support.
Further expanding its utility, GPT-4.5 (Preview) is a multimodal model, capable of processing both text and image inputs. This 'vision' capability unlocks a new class of applications that were previously impossible with text-only models. It can analyze financial charts, describe the contents of a photograph, interpret diagrams, or extract text from an image, all within the same conversational interface. This fusion of language and vision makes it a powerful tool for data analysis, accessibility applications, and content moderation.
Perhaps most strikingly for a model of this caliber, its current pricing is listed at $0.00 for both input and output tokens. This free access during the preview period presents an unprecedented opportunity for innovation and experimentation without financial barriers. However, developers should proceed with caution. This pricing is temporary and will almost certainly be replaced by a premium pricing structure upon general release. Furthermore, key performance metrics like speed and latency are not yet available, making it difficult to assess its suitability for real-time, user-facing applications. It is a model for those on the bleeding edge, willing to trade the stability of a production model for a first look at the future of AI.
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| Spec | Details |
|---|---|
| Model Owner | OpenAI |
| License | Proprietary |
| Context Window | 128,000 tokens |
| Knowledge Cutoff | September 2023 |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Model Type | Large Language Model (LLM) |
| Architecture | Transformer-based |
| Fine-Tuning | Expected to be supported via API |
| System Prompt | Supported |
| JSON Mode | Supported |
| Tool Use / Function Calling | Supported |
As a preview model, GPT-4.5 is exclusively available through its creator, OpenAI. This simplifies the choice of provider to a single option, focusing the decision instead on how to best leverage the model within the OpenAI ecosystem.
This direct access ensures developers get the most authentic and up-to-date version of the model, without any modifications or wrappers from third-party providers. However, it also means being subject to OpenAI's specific rate limits, availability, and eventual pricing structure. For now, the choice is not which provider to use, but whether to embrace the opportunities and risks of OpenAI's preview track.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Top Priority | Pick | Why | Tradeoff |
| Official Access | OpenAI API | The sole official provider for the preview model, ensuring direct access to the latest updates and authentic model behavior. | Subject to OpenAI's rate limits, potential waitlists for access, and future pricing changes. |
| Ease of Integration | OpenAI API | Utilizes the same well-documented and widely adopted API structure as other OpenAI models, simplifying integration for existing users. | Lack of third-party provider dashboards or value-add services that might simplify management or billing. |
| Best Performance | OpenAI API | Direct access should theoretically provide the best possible performance, as there are no intermediate layers adding latency. | Performance metrics (latency, throughput) are currently unknown and not guaranteed during the preview phase. |
| Lowest Cost | OpenAI API | Currently free during the preview period, offering unparalleled value for a state-of-the-art model. | This pricing is temporary. Future costs are unknown and could be substantial, creating budget uncertainty. |
Note: The provider landscape is subject to change. As GPT-4.5 (Preview) moves towards a general release, it may become available through other API providers and platforms, such as Microsoft Azure.
While the current token cost for GPT-4.5 (Preview) is zero, understanding token consumption is crucial for future-proofing your application's budget. The following examples illustrate how different real-world scenarios translate into token usage, which will directly impact costs once pricing is established.
These estimates are based on typical tokenization patterns where one token is approximately 4 characters or 0.75 words. The 'Estimated Cost' reflects the current free preview pricing, but tracking these token counts is key to forecasting future expenses.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Scenario | Input | Output | What it represents | Estimated cost |
| Email Summarization | ~750 tokens (a 1000-word email) | ~150 tokens (a 200-word summary) | Condensing a long customer support email into key points for an agent. | $0.00 |
| Code Generation | ~200 tokens (a detailed function description) | ~400 tokens (a block of Python code) | Generating a complex utility function based on a natural language specification. | $0.00 |
| Multimodal Analysis | ~1,200 tokens (a 500-word prompt + a detailed image) | ~300 tokens (a structured JSON output) | Analyzing a financial chart image and extracting key trends into a JSON object. | $0.00 |
| RAG Document Q&A | ~8,000 tokens (a 7,500-word context document + a 50-word question) | ~250 tokens (a concise answer) | Answering a specific question using a provided technical document as context. | $0.00 |
| Creative Writing | ~50 tokens (a short story prompt) | ~2,000 tokens (a 1,500-word short story) | Generating a chapter of a story based on a creative brief. | $0.00 |
The key takeaway is that while the model is currently free, token usage varies dramatically by task. Applications involving large context (like RAG) or verbose generation (like creative writing) will become significantly more expensive than short, transactional tasks once pricing is implemented. Developers should track token consumption now to forecast future operational costs accurately.
Although GPT-4.5 (Preview) is currently free, this is a temporary state. Proactive cost management is essential to avoid budget shocks when the model moves to a paid tier. A smart strategy involves not just minimizing token counts, but also optimizing how and when you call the model to ensure long-term financial viability.
The following strategies provide a playbook for building cost-efficient applications with this powerful model, ensuring your project remains sustainable long after the preview period ends.
The first step to cost management is measurement. Even with zero cost, you should:
The 128k context window is a powerful tool, not a default setting. Abusing it will be a recipe for high costs later. Instead:
Many applications receive repetitive queries. Calling the model for the same question multiple times is wasteful. A caching layer can dramatically reduce costs.
An easy way to control output costs is to prevent the model from being overly verbose. The max_tokens parameter is your best tool for this.
max_tokens value to prevent unnecessarily long responses.Not every task requires the world's most powerful model. A 'model cascade' or 'router' can intelligently delegate tasks to the most appropriate (and cost-effective) model.
GPT-4.5 (Preview) is a next-generation large language model from OpenAI. It is currently in a limited preview phase, offering early access to its advanced capabilities, which include superior reasoning, a 128k token context window, and multimodal (text and image) input.
It is positioned as a successor to the GPT-4 series. It achieves a higher score (38) on the Artificial Analysis Intelligence Index, suggesting more advanced reasoning and problem-solving skills than GPT-4 Turbo. It represents the next architectural step forward from OpenAI.
Yes, during the current preview period, API usage for this model is free of charge. However, this is a temporary promotional measure. It is widely expected to transition to a premium pricing model upon its general, stable release.
A 128,000-token context window allows the model to process and 'remember' a very large amount of information in a single request. This is equivalent to roughly 100,000 words or about 300 pages of text, enabling deep analysis of long documents, books, or codebases.
GPT-4.5 (Preview) can accept both text and images as input within the same prompt. This allows it to perform tasks like describing an image, analyzing a chart, or reading text from a picture. Its output is currently limited to text.
This model is ideal for developers, researchers, and businesses working on cutting-edge applications that require the highest level of AI reasoning. It is best suited for those who need to solve complex problems or leverage multimodal analysis and are comfortable working with a preview-stage product that may change.
The primary risks include: 1) unannounced changes to model behavior or performance, 2) lack of guarantees for speed, latency, or uptime, 3) potential for bugs or unexpected outputs, and 4) complete uncertainty about future pricing, which makes long-term budgeting impossible.