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George Miloradovich
Researcher, Copywriter & Usecase Interviewer
February 25, 2025
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AI is evolving fast, but not in the way most people think. Flashy announcements and marketing hype tend to focus on raw power and model size, but the real breakthroughs happen in how AI integrates into everyday problem-solving, how it understands complexity, and how it adapts to human logic.
ChatGPT 4.5 is arriving at this exact moment of transition. This large language model signals a shift from AI as a response generator to AI as a structured thinker. But what does that mean in practice? And more importantly, how does it fit into the broader trends shaping AI, automation, and the way businesses use low-code tools as Latenode?
The simplest way to describe ChatGPT 4.5 is this: it’s the last step before OpenAI fully integrates reasoning-driven intelligence into its models. For years, large language models have relied on brute force – billions of parameters, massive training datasets, and statistical patterns. But real intelligence isn’t only about volume; it’s about structure.
One of the biggest limitations of AI has always been its tendency to jump to conclusions. It provides answers without really showing its reasoning, like a student who memorized the answer key but never understood the subject. While ChatGPT 4.5 will remain highly optimized and efficient, it won’t engage in structured reasoning the way future models like ChatGPT-5 will.
This progression highlights a key moment: ChatGPT 4.5 is the last non-CoT model before OpenAI moves entirely to structured reasoning systems.
Right now, OpenAI offers multiple models: GPT-4, GPT-4o, DALL-E, and experimental O-series models. This has led to complexity in choosing the right model for different tasks. According to OpenAI’s official roadmap, GPT-5 will fully unify these models so users no longer need to pick between depth, speed, or specialized capabilities.
ChatGPT 4.5 will not introduce this unified system, but it will serve as the last iteration before the shift happens. This shift will be significant: AI will no longer be a set of different tools but rather a single system that intelligently adapts to the needs of the user, switching seamlessly between quick responses and deep reasoning when required.
Over the years, OpenAI has refined each generation to improve reasoning, efficiency, and usability. GPT-3.5 was the first step toward smoother and more human-like interactions, but it struggled with logical consistency. While it could generate text fluidly, it lacked structured reasoning, often producing responses that sounded plausible but were logically flawed.
GPT-4 improved upon this, introducing multimodal capabilities through DALL-E integration and better context retention, making it more suitable for complex problem-solving. Still, it remained constrained by computational efficiency and lacked robust step-by-step reasoning.
The release of GPT-4o brought a balance between depth and speed, making AI models more responsive. However, its reasoning was still implicit rather than structured, meaning it relied on probabilistic generation rather than breaking problems into logical steps. The recent update of 4o made the brought a change to the model, allowing it generate more human-like responses, with some users even admitting that it feels like an old friend.
Meanwhile, OpenAI experimented with chain-of-thought (CoT) models in o1 and o3, pioneering AI systems capable of decomposing problems into structured reasoning chains. These models improved performance in problem-solving tasks by internally breaking down complex queries into logical steps before generating responses.
Finally, the release of ChatGPT-4.5, the last non-chain-of-thought model, would lay the groundwork for ChatGPT-5, which is expected to integrate reasoning-based processing fully. ChatGPT-5 will merge OpenAI’s previous models into a unified system, enabling AI to determine when to engage in deep thought and when to provide quick, intuitive answers.
ChatGPT 4.5 and ChatGPT 5 will be released on Latenode as plun-and-play integrations with need for API tokens or account credentials. In the meantime, see our current OpenAI integration.
Not just ChatGPT 4.5, but all models are rapidly evolving in reasoning, efficiency, and multimodal capabilities. While OpenAI refines its approach with structured intelligence, competitors like Anthropic, DeepSeek, and Google are introducing models with distinctive strengths. Here’s how ChatGPT 4.5 measures up against the latest AI innovations.
Claude 3.7 Sonnet is a hybrid reasoning model, blending rapid intuition with deep structured problem-solving. One of its key innovations is full-swing reasoning, which makes its thought process visible, improving transparency and trust in decision-making. It has set new benchmarks in complex reasoning, particularly in programming, legal analysis, and scientific applications. This model is soon to be released on Latenode as a plug-and-play integration.
ChatGPT 4.5 is expected to enhance its performance in math problem-solving, general reasoning, and coding, building upon the advancements demonstrated in OpenAI's o3 model. Notably, o3 achieved a 49.3% success rate on the SWE-bench Verified benchmark, which evaluates real-world software engineering problems, surpassing o1's 48.9% success rate.
See Current Claude Integrations on Latenode
DeepSeek R1 has made a strong impression with its advanced mathematical and logical reasoning capabilities. Despite operating with fewer computational resources than its Western competitors, DeepSeek R1 competes directly with OpenAI’s O-series models in structured problem-solving, as shown in an MMLU-Pro (Reasoning and Knowledge) benchmark from Artificial Analysis, for example.
Even after ChatGPT 4.5 and GPT-5 are released, DeepSeek R1 will stay as a serious competitor. Designed to handle creative, scientific and technical challenges, this model benefits from a 128K context window, making it a compelling alternative for complex AI-driven reasoning tasks. However, it’s one of the slowest models on the market, generating only 29 tokens per second according to an AA’s benchmark testing the output speed.
In comparison, OpenAI models have always been known for their fine speed. For example, o3-mini features 160 tokens per second, which is the second-best result as of February 2025. GPT-4o offers 63 tokens per second, which is more than twice DeepSeek’s speed. ChatGPT 4.5 is likely to fall between o3 and its competitors, with an approximate rate of 120–140.
See AI DeepSeek integration, including both DeepSeek R1 and DeepSeek V3 in a single node.
Google’s Gemini series stands out due to its multimodal capabilities, allowing it to process text, images, and files. Gemini 1.5 and 2.0 has a 1 million-token context window, enabling it to retain and analyze massive amounts of data. It’s hard to imagine that ChatGPT 4.5 will have such a large context window.
In addition, one of Google's most recent models, Gemini 2.0 Flash, is one of the fastest and cheapest on the market. Due to the generation difference, it beats GPT-4o in GPQA Diamond, focused on testing the model on complex scientific tasks, and most other benchmarks. Considering that ChatGPT 4.5 is going to be a transitional model after the O-series, we should expect results at least not worse than those of Gemini.
See Google AI Integration on Latenode, including Gemini 1.5 and Gemini 2.0.
All automation scenarios face challenges – not in how they are built, but in how AI-generated content, decisions, and integrations perform in business cases. When ChatGPT 4.5 will be released, it might help solve major pain points that have made AI-powered automation unreliable or inefficient for many users. Here’s what you might expect:
Many automation processes depend on AI to generate blog posts, customer emails, chatbot responses, or product descriptions. However, existing models often struggle with maintaining factual accuracy, adhering to brand tone, and avoiding generic or inconsistent outputs.
Use case: Imagine a scenario automating email responses to customer inquiries using workflows that integrate ChatGPT, Intercom, or Gmail. Sometimes, AI-generated replies need human review because they contain inaccuracies or sound robotic.
Considering the shift towards more depth and accuracy in responses that we can see in all new models developed by OpenAI and other companies, it’s safe to assume that with ChatGPT 4.5, such a scenario on Latenode would maintain more factual accuracy, adapt its tone based on brand voice guidelines, and even reference internal company data if it’s connected to a database to avoid hallucinations.
A major task in low-code automation is that AI doesn’t just generate words – it also processes and transforms data. Current AI models sometimes misinterpret structured information, introduce inaccuracies when summarizing profiles, or fabricate missing details in professional records.
Use case: Suppose an HR automation system enriches employee records using a LinkedIn Data Scraper combined with a company database in Google Sheets. The goal is to update job titles, skills, and professional summaries, and then send cold outreach emails. AI might misinterpret LinkedIn data, assuming outdated roles as current ones or incorrectly matching job descriptions to employee profiles.
Considering the trend toward improved handling of context and prompts in the development of all AI models, it can be assumed that ChatGPT 4.5 will perform multi-level verification of extracted data before generating an answer to ensure the descriptions remain factual.
Many automation tools use AI to summarize long emails, legal documents, or customer feedback. However, existing models often miss key details or fail to identify what’s actually relevant.
Use case: A financial startup processes invoices using automation on Latenode that integrates ChatGPT for processing, Google Sheets as a database, and Gmail for sending the invoices to clients. Some AI models might summarize data incorrectly, missing crucial details, which is why they still need human review.
Although ChatGPT 4.5 will not have full reasoning, it will be trained with stronger context understanding, factual accuracy and prompt adherence. It can help better structure responses, ensuring automation extracts what truly matters in complex documents.
ChatGPT 4.5 is the last iteration before AI models make the jump to full chain-of-thought reasoning and self-optimization. We’re heading toward a time where AI will not just assist with automation but proactively improve it. Where it won’t just execute tasks – it will suggest better ideas, act faster, and offer better ways to get things done.
This is the shift happening in AI right now. Not just bigger models. Not just better accuracy. But AI that thinks before it speaks and plans before it acts. The question isn’t whether this will change automation. The question is: will you be ahead of the curve, or will AI automation on Latenode help optimize your workflows before you even realize the shift has happened? Try it now, and see if it suits your needs!
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