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What GPT-4.5 was at launch
OpenAI announced GPT-4.5 on February 27, 2025, as a research preview and described it as its largest and most knowledgeable GPT model to date. The company presented it as a general-purpose model focused on natural interaction, broad knowledge, instruction-following, creativity and practical problem-solving—not as a reasoning-first system built around deliberate chains of thought like o1. OpenAI also said it was expensive to run and not a replacement for GPT-4o. OpenAI’s launch announcement framed it as a way to scale pretraining and post-training.
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At launch, Pro users received ChatGPT access first; OpenAI said Plus and Team access would follow the next week, then Enterprise and Edu the week after. The API preview, gpt-4.5-preview, supported function calling, Structured Outputs, streaming, system messages and image inputs. Its documented context window was 128,000 tokens and maximum output was 16,384 tokens. These specifications describe the preview, not a current mainstream ChatGPT offering. OpenAI’s API model page now marks it deprecated.
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The mismatch was between qualities people notice in conversation and qualities that dominate technical leaderboards. GPT-4.5 could seem more polished, sensitive to tone and adept at following subtle intent, yet it did not present a clear, general breakthrough in difficult mathematics, formal reasoning or competitive coding. Those improvements can matter in real work, but they are diffuse: a benchmark may not capture them, and a user’s impression can be hard to distinguish from preference or prompting effects.
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Early reactions illustrated both sides, but were impressions rather than controlled evaluations. Ethan Mollick praised its writing and interest while describing it as occasionally “oddly lazy” on complex projects. Andrej Karpathy saw many subtle improvements without a revolutionary advance in reasoning-heavy domains. Gary Marcus called it a “nothingburger,” while Hugging Face CEO Clément Delangue criticized the closed model and found it unimpressive. The disagreement was not proof that one camp was right; it showed how differently people value style, capability and measurable task performance. VentureBeat’s account of the launch reactions reports these views.
Where the gains might have mattered
OpenAI’s case for GPT-4.5 emphasized more natural interaction, broader knowledge, better intent-following, emotional intelligence, writing, programming, coaching, brainstorming and communication. It also expected fewer hallucinations and described improved agentic planning and execution. These were launch claims and goals, not a guarantee that every answer would be more accurate or every workflow would improve.
One concrete enterprise example came from Box, which reported a 19-percentage-point improvement over GPT-4o in an internal, single-shot metadata-extraction evaluation using 17,000 fields from commercial contracts. That result suggests why a company might test the model for a narrow, valuable document workflow. It is Box’s own evaluation, not an independent benchmark, and it does not establish that GPT-4.5 would outperform other models on unrelated extraction or general-purpose tasks.
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Rank #2
- Potentially valuable: high-stakes writing and editing, executive communications, nuanced customer interactions, brainstorming, and complex document interpretation—if testing shows a meaningful improvement.
- Less compelling: routine summaries, bulk classification, simple support replies and data transformations, where a cheaper model may meet the quality bar.
- Not automatically the right choice: difficult math, formal problem-solving or code-heavy work, where a reasoning-oriented model may better match the task.
The price made the distinction consequential
At launch, GPT-4.5 Preview’s API rates were unusually high for a model whose advantages could be difficult to quantify. The current API listing still shows these figures while marking the model deprecated:
| GPT-4.5 Preview API usage | Price per 1 million tokens |
|---|---|
| Input | $75 |
| Cached input | $37.50 |
| Output | $150 |
OpenAI’s early API discussion described a typical query as costing about $68 per million tokens on average, depending on the mix of input and output and whether caching applied. That is an illustrative average, not a universal rate. Output tokens cost twice as much as ordinary input tokens, so long answers, repeated attempts and verbose workflows could drive bills up quickly. Long-context document processing could also be expensive when substantial input was sent for each request. OpenAI’s API community announcement included the average-query discussion.
The sound comparison is cost per acceptable result, not price per token in isolation or a single ranking of which model is “smartest.” A premium model can pay for itself if it avoids costly mistakes or human review in a high-value workflow. If a less expensive model produces results that pass the same checks, the premium buys little. Pricing changes across models and over time, so a historical comparison should not be mistaken for a current price ranking.
Rank #3
Why release an expensive non-reasoning model?
OpenAI’s stated explanation was that GPT-4.5 explored scaling pretraining and post-training, while the company solicited real-world feedback during a research preview. OpenAI said the model was compute-intensive and that it was still evaluating whether to serve it in the API long-term. Its launch materials also cautioned that academic benchmarks might not fully represent practical usefulness.
Other explanations are plausible but remain interpretation, not confirmed company policy. GPT-4.5 could test whether scaling conventional training still improved interaction quality; serve a premium segment willing to pay for better tone or broad knowledge; and support enterprise trials where even a modest extraction or communication gain was valuable. Karpathy suggested it could be a stronger base for later reasoning training, but that was an inference rather than an announced roadmap. Public discussion linked rollout limits to GPU scarcity, and some users speculated that pricing could discourage distillation or control demand; neither speculation is a confirmed explanation for the price.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge a model like GPT-4.5
For a launch-era buyer, a small evaluation against a cheaper model would have been more informative than relying on general impressions. The key was to use representative prompts and judge outcomes that mattered to the business: correctness, editing time, customer satisfaction, review burden, latency and cost per successful task. Polished language can create false confidence, so factual checks and acceptance criteria matter even when the writing feels better.
- Route routine work to a less expensive model and reserve premium inference for tasks where quality gains are measurable.
- Set output limits, cache reusable prompt content where appropriate, and consider the Batch API for eligible asynchronous jobs; OpenAI listed a 50% Batch discount for the preview.
- Use structured outputs and validation when downstream systems require predictable fields, and measure end-to-end success rather than token cost alone.
- Keep a fallback and migration plan for any preview model whose continued API service OpenAI said it was evaluating.
Its present-day status
As of August 18, 2026, GPT-4.5 is no longer available in ChatGPT, including custom GPTs; OpenAI says that retirement took effect June 26, 2026. The API is separate: OpenAI says the ChatGPT retirement did not itself change API availability, but the API model page lists gpt-4.5-preview as deprecated and recommends GPT-4.1 or o3 for most uses. Availability in an API is not a reason by itself to build a new product around a legacy preview. Teams with an existing dependency should confirm current access and establish a migration path. OpenAI’s ChatGPT release notes describe the retirement; its model release notes distinguish API availability from the ChatGPT change.
Was GPT-4.5 worth its price?
For most routine or high-volume workloads, the price was difficult to justify against diffuse improvements and cheaper alternatives. For a narrow, high-value workflow where a controlled evaluation demonstrated better writing, extraction or interaction outcomes, it could have made economic sense despite the token rates. GPT-4.5 was neither simply worthless nor the reasoning breakthrough some expected: it was an expensive experiment in improving general conversational quality, with a value proposition that depended heavily on the task. Its later retirement from ChatGPT and deprecated API status make that distinction historical for most buyers, not a recommendation for new deployments.
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