GPT-4.5: A Shiny Upgrade That Misses the Mark

Key Points

Research suggests GPT-4.5, released by OpenAI on February 27, 2025, has not met expectations in complex reasoning and benchmark performance.
It seems likely that the model's focus on conversational abilities and emotional intelligence, rather than core reasoning, has led to disappointment.
The evidence leans toward high costs and incremental improvements being key factors in user dissatisfaction.

What is GPT-4.5 and the Hype?

GPT-4.5, launched by OpenAI, was billed as their largest and most powerful AI model yet, with enhanced conversational skills and reduced hallucinations. The hype was fueled by its size and the promise of significant advancements over previous models like GPT-4 and GPT-4o.

Why Hasn't It Met Expectations?

Despite its strengths in natural dialogue and factual accuracy, GPT-4.5 falls short in complex reasoning tasks, such as math and science, where it is outpaced by smaller, specialized models like o3-mini. Its high operational costs, with API pricing at $75 per million input tokens and $150 per million output tokens, have also deterred users, especially when compared to the more affordable GPT-4o at $2.50/$10 (Wikipedia: GPT-4.5). Additionally, the improvements are seen as incremental, not revolutionary, leading to a perception of limited innovation.

Unexpected Detail: Emotional Focus

An unexpected aspect is GPT-4.5's emphasis on emotional intelligence and conversational tone, which, while enhancing user experience in casual interactions, does not address the needs of users seeking advanced problem-solving capabilities, contributing to the mixed reception.

Survey Note: Detailed Analysis of GPT-4.5's Performance and Expectations

Introduction and Context

As of March 12, 2025, the AI community has been abuzz with discussions around OpenAI's latest release, GPT-4.5, which debuted on February 27, 2025. Marketed as the company's largest and most knowledgeable model yet, it was anticipated to be a significant leap forward in the evolution of large language models (LLMs). However, early reviews and benchmark results suggest that GPT-4.5 has not fully met the high expectations set by its predecessors and the AI community, leading to a nuanced discussion on its capabilities and limitations.

Expectations Set for GPT-4.5

The expectations for GPT-4.5 were shaped by several factors:

Size and Compute Power: Given its description as the largest model OpenAI has built, with increased computational resources and training data, there was an expectation that it would outperform previous models across all domains, including mathematics, writing, and coding (TechCrunch: OpenAI unveils GPT-4.5 'Orion,' its largest AI model yet).
Benchmark Performance: The community anticipated strong performance on standard AI benchmarks, particularly in reasoning, math, and science, areas where AI models are often evaluated for progress (DataCamp: GPT 4.5: Features, Access, GPT-4o Comparison & More).
Improvements Over Predecessors: With the success of GPT-4 and GPT-4o, users expected GPT-4.5 to build on these foundations, offering revolutionary changes rather than incremental updates (WIRED: Hands-On With GPT-4.5, OpenAI’s Most Powerful Model Yet).

Actual Performance and Benchmark Results

Upon release, GPT-4.5 has shown a mixed bag of performance, with strengths in certain areas but notable weaknesses in others:

Conversational Abilities: The model excels in natural dialogue, offering concise and emotionally intelligent responses. Human testers employed by OpenAI preferred it for everyday queries, professional queries, and creative tasks, such as poetry and ASCII art (MIT Technology Review: OpenAI just released GPT-4.5 and says it is its biggest and best chat model yet). For instance, when asked why the ocean is salty, GPT-4.5 provided a clear, memorable explanation, contrasting with the verbose responses of earlier models (DataCamp: GPT 4.5: Features, Access, GPT-4o Comparison & More).
Factual Accuracy and Hallucinations: It shows improved accuracy on factual questions, with a 37.1% hallucination rate on SimpleQA, compared to 59.8% for GPT-4o and 80.3% for o3-mini, indicating better knowledge retention (Helicone.ai: GPT 4.5 Released: Here Are the Benchmarks).
Reasoning Capabilities: However, in complex reasoning tasks, GPT-4.5 lags behind. Benchmark results reveal it is outpaced by models like o3-mini in math and science, with specific scores showing a +27.4% improvement in math over GPT-4o but still not leading in these areas (Vellum.ai: GPT 4.5 is here: Better, but not the best). For example, on the SWE-Lancer Diamond benchmark, it performs better than o3-mini (32.6% vs. 23.3%), but this is more due to broader world knowledge rather than structured reasoning.

The following table summarizes key benchmark comparisons:

Benchmark	GPT-4.5 Performance	Comparison to GPT-4o	Comparison to o3-mini
SimpleQA Accuracy	62.5%	Outperforms (38.2%)	Outperforms (15%)
Math Improvement	+27.4% over GPT-4o	Better	Worse
Science Improvement	+17.8% over GPT-4o	Better	Worse
Hallucination Rate	37.1%	Better (59.8%)	Better (80.3%)

(Helicone.ai: GPT 4.5 Released: Here Are the Benchmarks, Vellum.ai: GPT 4.5 is here: Better, but not the best)

Cost and Efficiency: GPT-4.5 is notably expensive, with API costs at $75 per million input tokens and $150 per million output tokens, compared to GPT-4o's $2.50/$10, making it 2900% more expensive for input and 1300% dearer for output (Wikipedia: GPT-4.5). This high cost has led to concerns about its practicality, especially for developers and startups (Medium: OpenAI GPT4.5: It’s bad. Don't pay for OpenAI GPT4.5).

Reasons for Disappointment

Several factors contribute to the perception that GPT-4.5 has not met expectations:

Incremental Improvements: Critics argue that the model feels like a "shiny new coat of paint on the same old car," with improvements perceived as incremental rather than revolutionary (Medium: OpenAI GPT4.5: It’s bad. Don't pay for OpenAI GPT4.5). For instance, while it improves on conversational tone, it does not significantly enhance core capabilities like reasoning, which was a major expectation.
High Costs for Limited Gains: The substantial increase in API costs has not been justified by proportional performance gains, leading to dissatisfaction among users who find it prohibitively expensive for integration into projects (TechCrunch: OpenAI's GPT-4.5 AI model comes to more ChatGPT users).
Focus on Non-Essential Aspects: The model's emphasis on emotional intelligence and conversational tone, while enhancing user experience in casual interactions, does not address the needs of users seeking advanced problem-solving capabilities. This pivot from "bland assistant" to "AI bestie" has been noted as a trade-off, with benchmarks showing it lags in structured reasoning compared to models like Claude 3.5 Sonnet (Medium: The Great Paradox: Why OpenAI’s Most Expensive Model GPT-4.5 Falls Short of Expectations).
Comparisons with Competitors: Other models, such as those from Anthropic and DeepSeek, have shown competitive or better performance in certain benchmarks, diminishing the perceived uniqueness of GPT-4.5. For example, on math benchmarks, it is outpaced by newer reasoning models, leading to the view that it is not a "frontier model" (Hacker News: GPT-4.5: "Not a frontier model"?).

Implications and Future Outlook

The mixed reception of GPT-4.5 highlights the challenges in scaling AI models and the diminishing returns from simply increasing model size without corresponding innovations. It suggests a need for a balanced approach, where models are optimized for both conversational fluency and reasoning capabilities. As OpenAI continues to develop, future releases may see a blend of these aspects, with the company potentially retiring models like GPT-4.5 in favor of more cost-effective, capable alternatives (The Algorithmic Bridge: GPT-4.5 Feels Like a Letdown But It’s OpenAI’s Biggest Bet Yet).

For users, this means evaluating models based on specific needs—GPT-4.5 may be suitable for casual, conversational tasks, but for advanced problem-solving, alternatives like o3-mini or Claude 3.5 Sonnet might be preferable. The AI landscape remains dynamic, with ongoing developments likely to address these gaps in future iterations.

Key Citations

Published: 2025-03-12 12:00:00.000Z