Is GPT-4.5 really worth $75/month? Everything devs need to know.

Was GPT-4.5 the AI breakthrough we were promised ... or just an expensive letdown?

OpenAI framed it as a step toward more human-like intelligence: better reasoning, sharper accuracy, and even a hint of emotional awareness. But when developers finally got access, the response was more muted than OpenAI might have hoped.

What many expected to be a major leap forward turned out to be much more modest. Minor upgrades, subtle regressions, and a noticeably higher price tag left developers asking the obvious question: is this real progress—or just the latest example of diminishing returns at scale?

In today's newsletter, we'll cut through the hype to explore:

• What’s actually new in GPT-4.5

• How it performs in real-world development workflows

• Where it delivers—and where it still falls short

• And whether it justifies the $75/month price tag

Let's go.

GPT 4.5: What’s new?#

GPT-4.5 brought several new features and improvements, including:

• Improved emotional intelligence: The model better understands nuanced emotional contexts, allowing for more empathetic and context-aware interactions.

• Reduced hallucination rates: GPT-4.5 shows a significant decrease in fabricating facts, leading to more reliable outputs and fewer inaccuracies.

• Enhanced writing quality: The model delivers more fluent, coherent, and creative writing, making it more effective for various creative and analytical tasks.

• Expanded knowledge base: With an update in training data and compute, GPT-4.5 benefits from a broader and more current understanding of topics despite maintaining the same data cutoff as GPT-4.

• Better alignment with user intent: Improvements in instruction following and adherence to system prompts help GPT-4.5 generate responses that more closely match user expectations.

• Smoother conversational flow: Enhanced natural language generation results in more intuitive and human-like interactions.

• Optimized for creative tasks and agentic planning: GPT-4.5 is designed to excel in creative applications and strategic planning, setting it apart as a tool for innovative problem-solving.

• Improved multilingual capabilities: Early benchmarks indicate better performance on multilingual tasks, offering improved support for diverse languages.

But how do these improvements actually perform in practice? Let's take a look.

Performance compared to previous versions#

Incremental improvements#

GPT-4.5 delivered only modest gains over GPT-4 in most scenarios. OpenAI’s co-founder Andrej Karpathy noted that with GPT-4.5, “everything is a little bit better, and it’s awesome, but also not exactly in ways that are trivial to point to.” 

Tests showed GPT-4.5 performing slightly better than GPT-4 on various tasks, but not by a wide margin. For example, Box Inc. found that GPT-4.5 extracted 19% more fields correctly than GPT-4 in a contract analysis task—an improvement but not a revolutionary jump. This incremental progress left many observers underwhelmed.

Note: These comparisons were based on OpenAI's internal benchmarks and third-party testing across standard NLP tasks. The Box Inc. example specifically involved extracting structured data from legal documents using a sample size of approximately 500 contracts.

Underwhelming vs. expectations#

The anticipation for GPT-4.5 was substantial, given the prior performance of GPT-4, with expectations centered on introducing significant new functionalities.

However, the consensus suggests that GPT-4.5 primarily presents an iterative improvement upon its predecessor. Ethan Mollick, a professor at Wharton, acknowledged the model’s enhanced writing quality and creativity while noting a tendency toward diminished efficiency in intricate assignments.

Furthermore, his comparative observation aligning GPT-4.5 with Claude 3.7, and vice versa, implies a limited advancement relative to competitor benchmarks. Critical evaluations from figures such as Gary Marcus, who characterized GPT-4.5 as devoid of substantial improvement, further underscore this sentiment.

These critiques reflect a general perception of incremental enhancement rather than substantive innovation. Analogies likening GPT-4.5 to cosmetic upgrades without core functional alterations further illustrate this perspective.

No breakthrough in reasoning#

GPT-4.5’s failure to improve reasoning and complex problem-solving was a major letdown for many.

OpenAI confirmed that it’s not a chain-of-thought (CoT) reasoning model but simply a larger pretrained model, hindering its performance in areas where reasoning is critical (math, code, etc.). In tasks like mathematical word problems, logical puzzles, or coding challenges, GPT-4.5 performed similarly to GPT-4 and sometimes worse than smaller models optimized for reasoning.

Early benchmarks showed its coding abilities were comparable to OpenAI's earlier o3-mini model and significantly worse than rivals like Anthropic's Claude on certain tests. This underperformance in logic-heavy tasks made GPT-4.5 feel less capable than expected, considering it has 10× more training compute than GPT-4.

Model evaluation scores#

One of the most direct illustrations of GPT-4.5’s performance comes from model evaluation scores comparing GPT-4.5, an older GPT-4 (GPT-4o), and OpenAI’s smaller o3-mini (high) model. The table below highlights how GPT-4.5 sometimes edges out GPT-4o but still struggles to dominate every benchmark. In particular, it loses to o3-mini in certain categories (notably math), despite being far more expensive and resource-intensive.

From these results, a few patterns emerge:

• GPT-4.5 outperforms GPT-4o in certain areas, such as science QA (GPQA) and coding (SWE-Lancer Diamond). However, the margin is not overwhelmingly large.

• GPT-4.5 lags behind o3-mini in AIME (math) and in coding on SWE-Bench Verified, where o3-mini achieves 61.0% to GPT-4.5’s 38.0%. This is surprising given GPT-4.5’s far larger size and cost.

• GPT-4.5 does show improved multilingual understanding (MMMLU) at 85.1% over GPT-4o’s 81.5%. This suggests it might be better for certain language-focused applications, though still not a breakthrough level.

In essence, these numbers confirm the broader sentiment: GPT-4.5 provides incremental gains over GPT-4o, but not consistently and not in ways that justify its much higher operational costs.

The math category, in particular, highlights GPT-4.5’s ongoing struggles with reasoning-heavy tasks. Meanwhile, smaller and cheaper models sometimes match or surpass it on select benchmarks. This data reinforces the criticism that GPT-4.5’s improvements are too narrow to warrant the expense.

Accuracy and reliability of responses#

Fewer hallucinations#

The hallucination rate for GPT-4.5 is 37%, a notable decrease from GPT-4’s 62%. This reduction indicates that GPT-4.5 exhibits a lower propensity to generate fabricated content, enhancing its reliability for inquiries requiring factual accuracy.

Note: These percentages represent answers flagged as potentially containing fabricated information on test sets designed to challenge the models. In general usage, hallucination rates would likely be lower. The methodology involved human evaluators reviewing model outputs for factual correctness against verified information sources.

Honesty and guardrails#

Conversely, GPT-4.5’s responses appear more cautious and transparent when it doesn’t know something. Users report it is slightly better at admitting uncertainty or saying it cannot find an answer rather than hallucinating. This aligns with OpenAI's claim that GPT-4.5 is more “thoughtful, cautious and honest” in its response.

However, some have argued this caution can go too far— the model might refuse queries it previously handled or give very generic, safe answers, possibly due to stricter alignment tuning. That fine line between avoiding misinformation and providing useful specifics is one GPT-4.5 is still navigating. The bottom line on accuracy: improved, but far from perfect. Users must remain vigilant, as GPT-4.5 can and does still make mistakes—just a bit less often than GPT-4 did.

Speed and efficiency#

Slower response times#

A common complaint is that GPT-4.5 is noticeably slower than GPT-4. The model’s responses have more latency in the ChatGPT interface and via API. Early adopters observed that GPT-4.5 often pauses for a few seconds before replying, generating text sluggishly—only about 30–40% as fast as GPT-4 in token output speed.

This latency can be frustrating in interactive use, especially when users are accustomed to GPT-3.5 or even GPT-4‘s faster replies. Essentially, GPT-4.5 trades speed for subtle quality gains – a trade-off many users weren’t happy with.

High computational cost#

GPT-4.5 is resource-hungry and expensive to run. OpenAI staff described it as “very large and compute-intensive,” warning that it’s “not a replacement for GPT-4o” due to cost and practicality.

To put this in perspective, the API pricing for GPT-4.5 is about 30× higher per input token and 15× higher per output token than GPT-4. Compared to the least expensive GPT-3.5 model, GPT-4.5 can be hundreds of times more costly for the same task. These numbers mean that applications using GPT-4.5 will rack up huge bills unless usage is limited.

Even for ChatGPT Plus subscribers (who pay a flat fee), OpenAI initially restricted GPT-4.5’s availability because of the computational load. The inefficiency here is twofold: time (it’s slower to produce results) and compute (each result costs far more compute cycles).

From a developer standpoint, GPT-4.5’s cost-performance ratio was disappointing – marginal quality gains at a massive increase in cost.

Infrastructure strain#

The rollout of GPT-4.5 revealed infrastructure challenges due to its size. Sam Altman admitted that GPT-4.5 is such a “giant, expensive model” that OpenAI nearly ran out of the GPUs needed to serve it, leading to delays in making it widely available. Initially, GPT-4.5 was only offered to a subset of ChatGPT Pro users via a limited-preview API, specifically because OpenAI had to add “tens of thousands of GPUs” to handle demand.

Users noticed the strain in the form of slower responses and more frequent timeouts or errors when using the model in the first days of release. An article on Ars Technica flatly dubbed GPT-4.5 “big, expensive, and slow,” noting that its computational heft makes it impractical except for those with significant resources.

All of this underlined a key point: GPT-4.5 is inefficient to deploy. Its advantages come at the cost of speed and scalability. For many individual users and small developers, this made GPT-4.5 far less appealing than smaller, faster (even if slightly less advanced) models.

Bias and ethical concerns#

Bias and transparency#

Every AI model faces scrutiny about bias, and GPT-4.5 is no exception. While OpenAI has continuously worked on bias mitigation, GPT-4.5 still inherits biases in its vast training data. Any improvements in bias handling were not publicized, leading some to assume it hasn’t fundamentally changed from GPT-4. Pre-release speculation suggested that GPT-4.5 might include improved methods for detecting and reducing biases, but there is little concrete evidence to confirm this.

One challenge is that GPT-4.5 is a closed-source model—outsiders cannot inspect its training or alignment processes. This lack of transparency drew criticism from figures like Clement Delangue (CEO of Hugging Face), who responded to GPT-4.5’s launch with a “meh.” Delangue’s stance highlights an ethical concern: without open access, the community must trust OpenAI on safety and bias reduction claims, which some find unsatisfying. GPT-4.5’s closed nature makes verifying or remedying hidden biases harder, causing unease among AI ethicists and open-source advocates.

Misinformation and persuasion#

One of GPT-4.5’s selling points—its improved EQ and more human-like conversational style—can be seen as an ethical double-edged sword. The model is better at being emotionally convincing and empathetic in tone. While this is great for user experience (it can console someone feeling down or writing in a relatable way), it raises the risk of more persuasive misinformation.

Some experts worry about malicious actors using GPT-4.5’s persuasive power to generate propaganda or disinformation that could fool people. OpenAI must address how the model’s responses can be constrained or verified to prevent abuse.

Flaws, bugs, and regressions#

Beyond general performance issues, a few specific flaws and potential regressions in GPT-4.5’s behavior have been noted:

• System prompt obedience regression: Some developers have noticed that GPT-4.5 is less likely to follow system-level instructions than GPT-4. For example, one developer found that their chatbot, which typically assumes a specific role or persona based on a system prompt, became more generic and less aligned with the requested character when they switched to GPT-4.5. The developer described the GPT-4.5 bot as more simplistic and literal, overlooking some of the nuances of the prompt. This could be a side effect of the fine-tuning for thoughtfulness, where the model might prioritize a neutral and helpful tone over style instructions. Although not technically a bug, it’s a setback for those who rely on system prompts to control the AI’s personality or behavior. This raises concerns about which other instructions GPT-4.5 might disregard, potentially limiting developers’ control over the output.
  

• Lack of updated knowledge or context window: GPT-4.5 retained the same training data cutoff date (October 2023) and context window size (128k tokens) as its predecessor, GPT-4. This lack of improvement disappointed users who expected an expanded knowledge base or architectural enhancements, given the “GPT-4.5” branding and the time elapsed since GPT-4’s release. The fact that GPT-4.5 did not address the limitations of GPT-4 regarding recent events or large inputs led to perceptions of stagnation and decreased utility, as the model’s knowledge base failed to keep up with current events.
  

• Persistent weaknesses in reasoning: As discussed, GPT-4.5 didn’t improve reasoning-heavy tasks due to being a non-CoT model. One might frame this as a regression relative to expectations—because in the interim, other models (like OpenAI’s own CoT model “o1” or Anthropic’s Claude with its “thinking mode”) had shown better reasoning performance. GPT-4.5 often fell short of smaller, optimized models in benchmarks requiring multistep logical thinking. For example, on a coding challenge benchmark, GPT-4.5 scored 45%, whereas Anthropic’s Claude (Sonnet 3.7) achieved 60–65% when allowed to use a thinking chain-of-thought approach​. Also, OpenAI’s “o1” reasoning model scored about 44% on a simple QA hallucination test, not far behind GPT-4.5’s 37%​. So, while not a bug, it’s a notable shortcoming that GPT-4.5’s brute-force scale did not yield superiority in all areas—in fact, it lagged in some categories.
  

• Initial deployment hiccups: The launch of GPT-4.5 was marred by several genuine bugs, including Invalid URL / 404 errors when users tried to call the gpt-4.5-preview model on the API side. Although OpenAI engineers quickly resolved the issue, it highlighted the rushed nature of the rollout. Additionally, the initial unavailability of GPT-4.5 in the ChatGPT Playground interface for some Pro users confused. With slow speed and limited access, these technical glitches made the perception that GPT-4.5’s release was poorly executed.

Furthermore, GPT-4.5’s sluggishness and the need for OpenAI to throttle it indicate that it doesn’t run as smoothly as expected from a production AI service.

While GPT-4.5 didn’t experience catastrophic bugs or failures, the community noticed the small regressions and rough edges it introduced. The ignoring of system prompts, the lack of new knowledge, and OpenAI’s evaluation of whether to continue serving it in the API long-term suggest that GPT-4.5 might be an experiment or stopgap rather than a stable milestone. OpenAI’s mixed messaging and the model’s quirks contributed to a sense of instability and lack of confidence in the model.

So, is GPT-4.5 worth the money?#

For most developers, probably not.

GPT-4.5 doesn’t represent a breakthrough. It’s a marginal upgrade dressed in premium pricing—with improvements that are subtle, inconsistently delivered, and hard to justify given the cost. Yes, it hallucinates less and writes more fluently. But it’s slower, more expensive, and still underperforms in key areas like reasoning and code generation.

Many expected GPT-4.5 to set a new standard for dev workflows. Instead, it lags behind smaller, cheaper models in tasks where reasoning matters most—and introduces new limitations, from sluggish performance to reduced compliance with system prompts.

Unless your use case specifically requires its narrow set of strengths—like long-form content, multilingual tasks, or creative writing—it’s hard to recommend. Most developers will get more speed, flexibility, and value from GPT-4, Claude 3.5, or OpenAI’s own o3-mini.

So no, GPT-4.5 isn’t a scam. But it’s not a step forward either. It’s a reminder that in AI, more compute doesn’t mean more capability—and that bigger models don’t win if they can’t deliver where it counts.

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