The pricing for the various OpenAI models can be quite confusing. According to the information provided, the API costs are as follows:

• GPT-4.5: $75 per million tokens
• GPT-4: $25 per million tokens
• GPT-4 Mini: $15 cents per million tokens
• CLA 3.7: $150 per million tokens

The author notes that the pricing for GPT-4.5 is particularly high and not at all usable for anyone looking to build applications or automate tasks using the AI API. The author also encountered an issue where the initial information provided by the model about the pricing was incorrect, highlighting the potential for hallucination and inaccuracies.

Overall, the pricing differences between the models are significant, and it's important for developers to carefully consider the costs when choosing which model to use for their projects.

The author conducted a hallucination test to assess the accuracy of GPT-4.5's responses. They created a fictional "orange cream" mango variety and asked the model to describe it, both with and without the search function enabled.

The results were concerning - even with the search function turned on, GPT-4.5 provided completely fabricated details about the non-existent "orange cream" mango. This suggests that the model has a tendency to hallucinate information, rather than admitting when it lacks factual knowledge.

The author also noted that in a previous test, GPT-4.5 provided inaccurate cost information for its API usage, further highlighting the model's propensity for generating incorrect data. These findings raise doubts about the reliability of GPT-4.5's responses, especially for tasks that require factual accuracy.

GPT-4.5 demonstrates strong emotional intelligence and writing ability in the provided tests. When asked to write a sincere message to employees about layoffs, the model crafted a thoughtful and empathetic response, highlighting its ability to communicate with emotional intelligence. The message was well-formatted, appropriately lengthy, and effectively conveyed the desired tone.

Similarly, when tasked with providing specific tips to improve laptop battery life, GPT-4.5 delivered a concise and straightforward response, breaking down the recommendations into clear categories. The language used was direct and informative, without any unnecessary fluff or promotional elements.

Compared to GPT-4.0, the newer model did not show a significant improvement in the technical writing task, as both models provided similar comprehensive lists of tips. However, the emotional intelligence demonstration highlighted a subtle yet meaningful enhancement in GPT-4.5's capabilities.

Overall, the results suggest that GPT-4.5 excels at tasks requiring emotional awareness and natural language generation, while its technical writing abilities are on par with the previous iteration. These findings indicate that the model's strengths lie in its ability to communicate effectively and empathetically, making it a valuable tool for applications that involve human-like interactions and content creation.

When tasked with generating ideas for a new AI-powered business, GPT-4.5 demonstrated strong capabilities. It provided five distinct and relevant business concepts:

1. AI-Powered Financial Forecasting Assistant: An AI-powered tool to help businesses with financial planning and analysis.
2. Automated AI Customer Service Agent: An AI-powered customer service solution to enhance the user experience.
3. AI Content and Social Media Manager: An AI system to assist with content creation and social media management.
4. AI-Powered Inventory Manager: An AI-powered solution for inventory tracking and optimization.
5. AI Business Insight Platform: A centralized platform to analyze data from various business functions using AI.

Upon further probing, GPT-4.5 provided additional details on the fifth concept, outlining the target audience, core functionalities, technology stack, monetization model, and key benefits. This level of comprehensive business planning demonstrates the model's ability to ideate and flesh out viable business concepts.

While the ideas generated were not groundbreaking, they were practical and aligned with common pain points faced by small and medium-sized businesses. This suggests that GPT-4.5 can serve as a valuable ideation partner, helping to quickly generate a diverse set of business concepts for further exploration and development.

When provided with a boilerplate NDA document, GPT-4.5 demonstrated strong legal analysis capabilities. The model was able to identify several key areas for improvement, including:

• Suggesting a termination clause with a defined time period (e.g., 3-5 years) rather than an indefinite agreement.
• Recommending the inclusion of a clause to address compelled disclosure.
• Noting the absence of consequences for breach of the agreement.
• Identifying the lack of a clause addressing third-party access.

Compared to the analysis provided by GPT-4.0, GPT-4.5's response was more comprehensive, addressing a wider range of legal considerations. The model was able to provide specific, actionable feedback that could be used to strengthen the NDA document.

This suggests that GPT-4.5 has a strong grasp of legal concepts and the ability to apply them to real-world scenarios. Its attention to detail and ability to identify potential issues make it a valuable tool for tasks such as contract review, legal document analysis, and providing initial legal guidance.

GPT-4.5, the latest language model released by OpenAI, has been touted as the "largest and best model for chat." However, the author's testing has revealed a significant drawback - its speed performance.

Compared to other models like GPT-4, GPT-3.7, and Clauder, GPT-4.5 is noticeably slower. The author conducted side-by-side tests, sending the same prompts to these models, and found that GPT-4.5 consistently took longer to generate responses.

The author notes that this is particularly concerning, as GPT-4.5 is not a reasoning model. Reasoning models, such as Gemini Flash, are expected to be slower due to the need for accurate analysis. However, a non-reasoning model like GPT-4.5 should prioritize speed, which is not the case in the author's experience.

Furthermore, the author found that the responses from Clauder were often more comprehensive than those from GPT-4.5, despite the latter's claims of improved writing and emotional intelligence capabilities.

The author concludes that the speed issue with GPT-4.5 is a significant drawback, especially for developers and users who require fast AI-powered solutions. The author is hopeful that future iterations, such as GPT-5, will address this problem and provide a more balanced performance across speed, accuracy, and advanced capabilities.

After extensively testing the new GPT-4.5 model, the key takeaways are:

• GPT-4.5 is the largest and most capable chatbot model released by OpenAI so far, but it is not a reasoning model like GPT-4 or Anthropic's Delphi.
• The model excels at tasks that require emotional intelligence, empathy, and natural language generation, producing high-quality and coherent responses.
• However, it struggles with factual accuracy and can hallucinate information, especially when dealing with made-up or obscure topics.
• The API pricing for GPT-4.5 is significantly higher than previous models, making it less viable for many developers and applications.
• While GPT-4.5 represents an incremental improvement over GPT-4, the author feels that the upcoming GPT-5 model, which is promised to combine reasoning and language capabilities, will be a more significant leap forward.
• Until then, the author recommends sticking with GPT-4 or exploring other models like Anthropic's Claude, which may offer better performance and value for certain use cases.