Reviewing OpenAI Model - GPT-4.5

 Unpacking GPT-4.5: Review of OpenAI's Latest Model

In February 2025, OpenAI added another contender to the ring: GPT-4.5. Codenamed "Orion" during its development, this model arrived not long after competitors like Anthropic's Claude 3.7 and xAI's Grok 3, signaling intense competition in the AI space. Let's dig into what GPT-4.5 brings to the table, how it's being received, and what it costs to use.

The Genesis of GPT-4.5: Release and Development

GPT-4.5 was showed as a research preview on February 27, 2025. OpenAI positioned it as their largest and most capable chat model at the time, surpassing the abilities of its predecessors like GPT-4 and GPT-4o. Unlike the "o" series models (o1, o3), which focus on explicit step-by-step reasoning, GPT-4.5 was designed differently. It leans heavily on scaled-up unsupervised learning, aiming for a more intuitive, "innately smarter" feel, prioritizing natural conversation and emotional intelligence (EQ) over demonstrable reasoning processes.

Its development involved training on Microsoft's Azure AI supercomputers, utilizing scaled-up data and compute alongside innovations in architecture. OpenAI CEO Sam Altman described it frankly as a "giant, expensive model," and acknowledged that GPU shortages presented a challenge during its phased rollout.

GPT-4.5 wasn't just an incremental update; OpenAI positioned it as a shift in focus towards more intuitive and human-like interaction, backed by specific capabilities.

Here’s a closer look at those features, incorporating research claims and user feed back on GPT 4.5

1. GPT 4.5 Has Claimed To Have More Natural Conversation:

OpenAI  claims that it designed GPT-4.5 explicitly to move beyond robotic responses, aiming for interactions that feel more fluid, nuanced, and genuinely conversational, as if talking to a knowledgeable human.

For some user feedback often aligned with OpenAI's goals. Some users have showcased conversations where GPT-4.5 demonstrated a remarkable ability to maintain context, understand subtle cues, and respond in a less formulaic way than previous models. Reports described it feeling "more like collaborating with a thoughtful person." However, other users complained that while the quality of conversation might be higher, the slower response times sometimes detracted from the feeling of fluidity, especially when compared to faster models like GPT-4o or competitors. The high cost also led some to question if the improved naturalness justified the expense for everyday chats.

2. Also Enhanced Emotional Intelligence (EQ):

As OpenAI Marked It, The Model's core design principle was integrating deeper sentiment analysis and emotional understanding. The goal was for GPT-4.5 to better grasp the emotional tone of a query and respond with appropriate empathy and nuance.

Users have found this enhanced EQ noticeable, particularly in applications like coaching, therapy simulations, or drafting sensitive communications. Its ability to pick up on subtle emotional context was praised. Some showcased its effectiveness in generating responses that felt more considerate and less detached. While direct complaints about the EQ feature itself were less common, the overall mixed reviews on performance suggest that its application wasn't consistently groundbreaking for all users across all scenarios.

3. The Model Have Broader Knowledge & Improved Intent Following:

The model has been trained on a significantly larger and potentially more diverse dataset, GPT-4.5 was expected to possess a wider general knowledge base and be better at discerning the user's underlying intent, even if not explicitly stated.

4. Multilingual Proficiency: The benchmarks indicated superior performance across multiple languages compared to previous models, particularly on complex tasks represented by evaluations like MMLU (Massive Multitask Language Understanding).

5. API Support And Price: Model integration with standard API features like function calling, structured JSON outputs, streaming responses, and system message customization was included.

Developers acknowledged the presence of these necessary features. However, the overwhelming feedback regarding the API was its prohibitive cost. While the capabilities were there, the price point ($75/1M input tokens, $150/1M output tokens) made leveraging these features financially unviable for many applications, overshadowing the technical implementation itself.

Cached input offers a discount ($37.50 / 1M tokens), and the Batch API provides a 50% discount.