blog
June 15, 2023
•6 min read
A Deep Dive into GPT-4 vs GPT-3.5 Differences and Ability to Revolutionize Software Development
ChatGPT-3.5 and ChatGPT-4 are both natural language processing (NLP) models developed by OpenAI, but there are some key differences between the two that software developers should be aware of. Let's demonstrate how these differences affect the data processing and output generated by these models on specific examples.
GPT-4 vs GPT-3.5-turbo: the key differences
While GPT-3.5 is a text-to-text model, GPT-4 is more of a data-to-text model. This means that GPT-4 can process more than just text inputs which makes it more suitable for complex tasks. Here are the key differences between the two models:
- Model Advancement: GPT-4 is significantly more advanced than GPT-3.5, with a 10 times improvement. This allows it to better understand context and distinguish nuances, leading to more accurate and coherent responses.
- Token Limit Increase: GPT-4 has a maximum token limit of 32,000, a substantial increase from GPT-3.5's 4,000 tokens.
- Handling of Longer Conversations: GPT-4 can manage longer prompts and conversations than GPT-3.5, making it more suitable for complex dialog systems.
- Model Size Comparison: GPT-4, rumored to have 1 trillion parameters, is significantly larger than GPT-3.5, which has 175 billion parameters. This allows GPT-4 to generate more accurate responses.
- Contextual Understanding Capability: GPT-4 can answer complex questions by synthesizing information from multiple sources, whereas GPT-3.5 may struggle to connect the dots.
- Linguistic Capabilities: GPT-4 can generate more sophisticated and nuanced language than GPT-3.5, making it a more powerful tool for text generation.
- Accuracy of Responses: GPT-4 is 82% less likely to respond to requests for disallowed content and 40% more likely to produce factual responses than GPT-3.5, according to internal evaluations.
- Multimodal Input Handling: GPT-4 can handle both text and images, while GPT-3.5 is a text-to-text model, making GPT-4 more versatile in tasks requiring comprehension of longer documents or mixed text and image data.
- Cost Comparison: GPT-4 is available through the OpenAI website as part of its ChatGPT Plus subscription, which costs $20 a month, while GPT-3.5 is free to use.
GPT-4 vs GPT-3.5-turbo: A Comparison of outputs
To compare the capabilities of the GPT-4 and GPT-3.5-turbo models, we'll examine their outputs when generating Class Diagrams and Entity Relationship Diagrams using notation of Gleek, the diagramming tool for developers.
Example 1: Generating Class Diagrams
When asked to show a sample online ecommerce website admin panel process, GPT-4 generates a detailed diagram that includes four classes which are then related via inheritance and implementation relationships. The diagram generated by GPT-4 gives a concrete and detailed visual representation of the online eCommerce website admin panel process.
Request: Show me a sample online e-commerce website admin panel process
Response GPT-4:
Classes (abstract/service/interface/none)
ClassAdminPanel:abstract
+Username
+Password
+Login()
ClassProduct
+ProductID
+ProductName
+Price
+AddProduct()
+UpdateProduct()
+DeleteProduct()
ClassOrder:service
+OrderID
+CustomerID
+OrderStatus
+ProcessOrder()
ClassCustomer
+CustomerID
+CustomerName
+AddCustomer()
+UpdateCustomer()
+DeleteCustomer()
Relationships
ClassAdminPanel-contains-ClassProduct
ClassAdminPanel-contains-ClassOrder
ClassAdminPanel-contains-ClassCustomer
ClassOrder {1}-has-{1..n} ClassProduct
ClassAdminPanel--*>ClassProduct (inheritance)
ClassProduct-.-*>ClassOrder (implementation)
After minor syntax changes (underlined), the generated diagram is following:
On the other hand, GPT-3.5 was not able to generate a diagram with samples but provided a general idea of what an eCommerce website admin panel process might look like. Instead of a detailed diagram, it describes the key components of the process, including managing products, orders, customers, payments, and integrating with payment gateways and shipping providers.
Response GPT-3.5-turbo:
Sorry, as an AI language model, I am not able to show you a sample online ecommerce website admin panel process.
However, I can provide you with a general idea of what an ecommerce website admin panel process might include.
An ecommerce website admin panel process typically involves managing products, orders, customers, and payments.
The admin panel may include features such as adding/editing products, managing inventory, processing orders, managing customer accounts, and generating reports.
The process may also involve integrating with payment gateways and shipping providers.
Example 2: Generating Entity Relationship Diagrams
When asked to show a company management system, GPT-4 generates a comprehensive diagram that outlines the entities involved in such a system (along with their respective attributes and relationships), respectively: Company, Employee, Department, and Project. The diagram generated by GPT-4 is highly detailed and provides a clear visual representation of the system.
Request: Show me a company management system
Response GPT-4:
Company
String id
String name
String address
String industry
Company {1}-contains-{1..n} Employee
Company {1}-has-{1..n} Department
Company {1}-undertakes-{1..n} Project
Employee
String id
String firstName
String lastName
String email
String phone
String position
String department
String salary
String startDate
String endDate
Employee {1}-memberOf-{1} Department
Employee {1..n}-participatesIn-{1..n} Project
Department
String id
String name
Department {1}-managedBy-{1} Employee (manager)
Department {1}-includes-{1..n} Employee
Project
String id
String name
String description
String startDate
String endDate
String status
Project {1}-involves-{1..n} Employee (teamMembers)
After minor changes (we removed the underlined), this diagram was provided by Gleek:
In contrast, GPT-3.5-turbo generates a much simpler diagram that includes only the entities and their attributes, without specifying their relationships. The diagram generated by GPT-3.5-turbo requires a significant amount of syntax changes to produce a legible visualization.
Response GPT-3.5-turbo:
EntityOne: Company
Datatype id
Datatype name
Datatype address
Datatype industry
EntityOne {1}-relationDescription-{1..n} EntityTwo
EntityTwo: Employee
Datatype id
Datatype firstName
Datatype lastName
Datatype email
Datatype phone
Datatype position
Datatype department
Datatype salary
Datatype startDate
Datatype endDate
EntityTwo: Department
Datatype id
Datatype name
EntityTwo {1}-relationDescription-{1} EntityTwo
EntityTwo: Project
Datatype id
Datatype name
Datatype description
Datatype startDate
Datatype endDate
Datatype status
EntityTwo {1}-relationDescription-{1..n} EntityTwo
After quite a lot syntax changes (underlined), outputted diagram was:
Overall, the comparison between GPT-4 and GPT-3.5-turbo shows that while both models are capable of generating diagrams, GPT-4 provides more detailed and comprehensive outputs compared to GPT-3.5-turbo. As the technology continues to advance, we can expect that future iterations of AI language processing models will continue to improve in generating high-quality visual representations of complex processes.
But what does this all mean for you as a developer or a business owner? How can you leverage these language models and the advancements in AI technology to your advantage? This is where Blocshop comes in.
Harnessing the Power of AI with a dedicated team of AI software developers
At Blocshop, we pride ourselves on staying ahead of the curve when it comes to cutting-edge technology. As a leading custom software development studio, our team of experienced professionals is dedicated to providing the most advanced AI solutions available today.
As part of our ongoing commitment to innovation, we have closely examined the differences between various LLMs to be able to create even more powerful and intelligent applications for your business based on our findings.
Using our extensive tech stack, including Angular, Microsoft Azure, Ionic, Kotlin, Kubernetes, Progressive Web Apps, MongoDB, Java programming language, Amazon Web Services, Microsoft SQL Server, React, Net Core, LLM, and of course the OpenAI products, we're constantly pushing the boundaries of what's possible in custom software development.
At Blocshop, we understand that the future of business relies on harnessing the power of AI. By staying informed about the latest advancements in large language models (LLM) development, we ensure that our clients receive the most innovative and effective solutions for their unique needs.
Choose Blocshop as your custom AI software development partner and gain an unfair advantage in today's drastically changing competitive marketplace.
Learn more from our insights
NOVEMBER 3, 2025 • 7 min read
CE marking software under the EU AI Act – who needs it and how to prepare a conformity assessment
From 2026, AI systems classified as high-risk under the EU Artificial Intelligence Act (Regulation (EU) 2024/1689) will have to undergo a conformity assessment and obtain a CE marking before being placed on the EU market or put into service.
October 19, 2025 • 7 min read
EU and UK AI regulation compared: implications for software, data, and AI projects
Both the European Union and the United Kingdom are shaping distinct—but increasingly convergent—approaches to AI regulation.
For companies developing or deploying AI solutions across both regions, understanding these differences is not an academic exercise. It directly affects how software and data projects are planned, documented, and maintained.
October 9, 2025 • 5 min read
When AI and GDPR meet: navigating the tension between AI and data protection
When AI-powered systems process or generate personal data, they enter a regulatory minefield — especially under the EU’s General Data Protection Regulation (GDPR) and the emerging EU AI Act regime
September 17, 2025 • 4 min read
6 AI integration use cases enterprises can adopt for automation and decision support
The question for most companies is no longer if they should use AI, but where it will bring a measurable impact.
The journey to your
custom software
solution starts here.
Services
© 2025 Blocshop s.r.o. All rights reserved.
Let's talk!
blog
June 15, 2023
•6 min read
A Deep Dive into GPT-4 vs GPT-3.5 Differences and Ability to Revolutionize Software Development
ChatGPT-3.5 and ChatGPT-4 are both natural language processing (NLP) models developed by OpenAI, but there are some key differences between the two that software developers should be aware of. Let's demonstrate how these differences affect the data processing and output generated by these models on specific examples.
GPT-4 vs GPT-3.5-turbo: the key differences
While GPT-3.5 is a text-to-text model, GPT-4 is more of a data-to-text model. This means that GPT-4 can process more than just text inputs which makes it more suitable for complex tasks. Here are the key differences between the two models:
- Model Advancement: GPT-4 is significantly more advanced than GPT-3.5, with a 10 times improvement. This allows it to better understand context and distinguish nuances, leading to more accurate and coherent responses.
- Token Limit Increase: GPT-4 has a maximum token limit of 32,000, a substantial increase from GPT-3.5's 4,000 tokens.
- Handling of Longer Conversations: GPT-4 can manage longer prompts and conversations than GPT-3.5, making it more suitable for complex dialog systems.
- Model Size Comparison: GPT-4, rumored to have 1 trillion parameters, is significantly larger than GPT-3.5, which has 175 billion parameters. This allows GPT-4 to generate more accurate responses.
- Contextual Understanding Capability: GPT-4 can answer complex questions by synthesizing information from multiple sources, whereas GPT-3.5 may struggle to connect the dots.
- Linguistic Capabilities: GPT-4 can generate more sophisticated and nuanced language than GPT-3.5, making it a more powerful tool for text generation.
- Accuracy of Responses: GPT-4 is 82% less likely to respond to requests for disallowed content and 40% more likely to produce factual responses than GPT-3.5, according to internal evaluations.
- Multimodal Input Handling: GPT-4 can handle both text and images, while GPT-3.5 is a text-to-text model, making GPT-4 more versatile in tasks requiring comprehension of longer documents or mixed text and image data.
- Cost Comparison: GPT-4 is available through the OpenAI website as part of its ChatGPT Plus subscription, which costs $20 a month, while GPT-3.5 is free to use.
GPT-4 vs GPT-3.5-turbo: A Comparison of outputs
To compare the capabilities of the GPT-4 and GPT-3.5-turbo models, we'll examine their outputs when generating Class Diagrams and Entity Relationship Diagrams using notation of Gleek, the diagramming tool for developers.
Example 1: Generating Class Diagrams
When asked to show a sample online ecommerce website admin panel process, GPT-4 generates a detailed diagram that includes four classes which are then related via inheritance and implementation relationships. The diagram generated by GPT-4 gives a concrete and detailed visual representation of the online eCommerce website admin panel process.
Request: Show me a sample online e-commerce website admin panel process
Response GPT-4:
Classes (abstract/service/interface/none)
ClassAdminPanel:abstract
+Username
+Password
+Login()
ClassProduct
+ProductID
+ProductName
+Price
+AddProduct()
+UpdateProduct()
+DeleteProduct()
ClassOrder:service
+OrderID
+CustomerID
+OrderStatus
+ProcessOrder()
ClassCustomer
+CustomerID
+CustomerName
+AddCustomer()
+UpdateCustomer()
+DeleteCustomer()
Relationships
ClassAdminPanel-contains-ClassProduct
ClassAdminPanel-contains-ClassOrder
ClassAdminPanel-contains-ClassCustomer
ClassOrder {1}-has-{1..n} ClassProduct
ClassAdminPanel--*>ClassProduct (inheritance)
ClassProduct-.-*>ClassOrder (implementation)
After minor syntax changes (underlined), the generated diagram is following:
On the other hand, GPT-3.5 was not able to generate a diagram with samples but provided a general idea of what an eCommerce website admin panel process might look like. Instead of a detailed diagram, it describes the key components of the process, including managing products, orders, customers, payments, and integrating with payment gateways and shipping providers.
Response GPT-3.5-turbo:
Sorry, as an AI language model, I am not able to show you a sample online ecommerce website admin panel process.
However, I can provide you with a general idea of what an ecommerce website admin panel process might include.
An ecommerce website admin panel process typically involves managing products, orders, customers, and payments.
The admin panel may include features such as adding/editing products, managing inventory, processing orders, managing customer accounts, and generating reports.
The process may also involve integrating with payment gateways and shipping providers.
Example 2: Generating Entity Relationship Diagrams
When asked to show a company management system, GPT-4 generates a comprehensive diagram that outlines the entities involved in such a system (along with their respective attributes and relationships), respectively: Company, Employee, Department, and Project. The diagram generated by GPT-4 is highly detailed and provides a clear visual representation of the system.
Request: Show me a company management system
Response GPT-4:
Company
String id
String name
String address
String industry
Company {1}-contains-{1..n} Employee
Company {1}-has-{1..n} Department
Company {1}-undertakes-{1..n} Project
Employee
String id
String firstName
String lastName
String email
String phone
String position
String department
String salary
String startDate
String endDate
Employee {1}-memberOf-{1} Department
Employee {1..n}-participatesIn-{1..n} Project
Department
String id
String name
Department {1}-managedBy-{1} Employee (manager)
Department {1}-includes-{1..n} Employee
Project
String id
String name
String description
String startDate
String endDate
String status
Project {1}-involves-{1..n} Employee (teamMembers)
After minor changes (we removed the underlined), this diagram was provided by Gleek:
In contrast, GPT-3.5-turbo generates a much simpler diagram that includes only the entities and their attributes, without specifying their relationships. The diagram generated by GPT-3.5-turbo requires a significant amount of syntax changes to produce a legible visualization.
Response GPT-3.5-turbo:
EntityOne: Company
Datatype id
Datatype name
Datatype address
Datatype industry
EntityOne {1}-relationDescription-{1..n} EntityTwo
EntityTwo: Employee
Datatype id
Datatype firstName
Datatype lastName
Datatype email
Datatype phone
Datatype position
Datatype department
Datatype salary
Datatype startDate
Datatype endDate
EntityTwo: Department
Datatype id
Datatype name
EntityTwo {1}-relationDescription-{1} EntityTwo
EntityTwo: Project
Datatype id
Datatype name
Datatype description
Datatype startDate
Datatype endDate
Datatype status
EntityTwo {1}-relationDescription-{1..n} EntityTwo
After quite a lot syntax changes (underlined), outputted diagram was:
Overall, the comparison between GPT-4 and GPT-3.5-turbo shows that while both models are capable of generating diagrams, GPT-4 provides more detailed and comprehensive outputs compared to GPT-3.5-turbo. As the technology continues to advance, we can expect that future iterations of AI language processing models will continue to improve in generating high-quality visual representations of complex processes.
But what does this all mean for you as a developer or a business owner? How can you leverage these language models and the advancements in AI technology to your advantage? This is where Blocshop comes in.
Harnessing the Power of AI with a dedicated team of AI software developers
At Blocshop, we pride ourselves on staying ahead of the curve when it comes to cutting-edge technology. As a leading custom software development studio, our team of experienced professionals is dedicated to providing the most advanced AI solutions available today.
As part of our ongoing commitment to innovation, we have closely examined the differences between various LLMs to be able to create even more powerful and intelligent applications for your business based on our findings.
Using our extensive tech stack, including Angular, Microsoft Azure, Ionic, Kotlin, Kubernetes, Progressive Web Apps, MongoDB, Java programming language, Amazon Web Services, Microsoft SQL Server, React, Net Core, LLM, and of course the OpenAI products, we're constantly pushing the boundaries of what's possible in custom software development.
At Blocshop, we understand that the future of business relies on harnessing the power of AI. By staying informed about the latest advancements in large language models (LLM) development, we ensure that our clients receive the most innovative and effective solutions for their unique needs.
Choose Blocshop as your custom AI software development partner and gain an unfair advantage in today's drastically changing competitive marketplace.
Learn more from our insights
NOVEMBER 3, 2025 • 7 min read
CE marking software under the EU AI Act – who needs it and how to prepare a conformity assessment
From 2026, AI systems classified as high-risk under the EU Artificial Intelligence Act (Regulation (EU) 2024/1689) will have to undergo a conformity assessment and obtain a CE marking before being placed on the EU market or put into service.
October 19, 2025 • 7 min read
EU and UK AI regulation compared: implications for software, data, and AI projects
Both the European Union and the United Kingdom are shaping distinct—but increasingly convergent—approaches to AI regulation.
For companies developing or deploying AI solutions across both regions, understanding these differences is not an academic exercise. It directly affects how software and data projects are planned, documented, and maintained.
October 9, 2025 • 5 min read
When AI and GDPR meet: navigating the tension between AI and data protection
When AI-powered systems process or generate personal data, they enter a regulatory minefield — especially under the EU’s General Data Protection Regulation (GDPR) and the emerging EU AI Act regime
September 17, 2025 • 4 min read
6 AI integration use cases enterprises can adopt for automation and decision support
The question for most companies is no longer if they should use AI, but where it will bring a measurable impact.
The journey to your
custom software
solution starts here.
Services
Head Office
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© 2025 Blocshop s.r.o. All rights reserved.
Let's talk!
blog
June 15, 2023
•6 min read
A Deep Dive into GPT-4 vs GPT-3.5 Differences and Ability to Revolutionize Software Development
ChatGPT-3.5 and ChatGPT-4 are both natural language processing (NLP) models developed by OpenAI, but there are some key differences between the two that software developers should be aware of. Let's demonstrate how these differences affect the data processing and output generated by these models on specific examples.
GPT-4 vs GPT-3.5-turbo: the key differences
While GPT-3.5 is a text-to-text model, GPT-4 is more of a data-to-text model. This means that GPT-4 can process more than just text inputs which makes it more suitable for complex tasks. Here are the key differences between the two models:
- Model Advancement: GPT-4 is significantly more advanced than GPT-3.5, with a 10 times improvement. This allows it to better understand context and distinguish nuances, leading to more accurate and coherent responses.
- Token Limit Increase: GPT-4 has a maximum token limit of 32,000, a substantial increase from GPT-3.5's 4,000 tokens.
- Handling of Longer Conversations: GPT-4 can manage longer prompts and conversations than GPT-3.5, making it more suitable for complex dialog systems.
- Model Size Comparison: GPT-4, rumored to have 1 trillion parameters, is significantly larger than GPT-3.5, which has 175 billion parameters. This allows GPT-4 to generate more accurate responses.
- Contextual Understanding Capability: GPT-4 can answer complex questions by synthesizing information from multiple sources, whereas GPT-3.5 may struggle to connect the dots.
- Linguistic Capabilities: GPT-4 can generate more sophisticated and nuanced language than GPT-3.5, making it a more powerful tool for text generation.
- Accuracy of Responses: GPT-4 is 82% less likely to respond to requests for disallowed content and 40% more likely to produce factual responses than GPT-3.5, according to internal evaluations.
- Multimodal Input Handling: GPT-4 can handle both text and images, while GPT-3.5 is a text-to-text model, making GPT-4 more versatile in tasks requiring comprehension of longer documents or mixed text and image data.
- Cost Comparison: GPT-4 is available through the OpenAI website as part of its ChatGPT Plus subscription, which costs $20 a month, while GPT-3.5 is free to use.
GPT-4 vs GPT-3.5-turbo: A Comparison of outputs
To compare the capabilities of the GPT-4 and GPT-3.5-turbo models, we'll examine their outputs when generating Class Diagrams and Entity Relationship Diagrams using notation of Gleek, the diagramming tool for developers.
Example 1: Generating Class Diagrams
When asked to show a sample online ecommerce website admin panel process, GPT-4 generates a detailed diagram that includes four classes which are then related via inheritance and implementation relationships. The diagram generated by GPT-4 gives a concrete and detailed visual representation of the online eCommerce website admin panel process.
Request: Show me a sample online e-commerce website admin panel process
Response GPT-4:
Classes (abstract/service/interface/none)
ClassAdminPanel:abstract
+Username
+Password
+Login()
ClassProduct
+ProductID
+ProductName
+Price
+AddProduct()
+UpdateProduct()
+DeleteProduct()
ClassOrder:service
+OrderID
+CustomerID
+OrderStatus
+ProcessOrder()
ClassCustomer
+CustomerID
+CustomerName
+AddCustomer()
+UpdateCustomer()
+DeleteCustomer()
Relationships
ClassAdminPanel-contains-ClassProduct
ClassAdminPanel-contains-ClassOrder
ClassAdminPanel-contains-ClassCustomer
ClassOrder {1}-has-{1..n} ClassProduct
ClassAdminPanel--*>ClassProduct (inheritance)
ClassProduct-.-*>ClassOrder (implementation)
After minor syntax changes (underlined), the generated diagram is following:
On the other hand, GPT-3.5 was not able to generate a diagram with samples but provided a general idea of what an eCommerce website admin panel process might look like. Instead of a detailed diagram, it describes the key components of the process, including managing products, orders, customers, payments, and integrating with payment gateways and shipping providers.
Response GPT-3.5-turbo:
Sorry, as an AI language model, I am not able to show you a sample online ecommerce website admin panel process.
However, I can provide you with a general idea of what an ecommerce website admin panel process might include.
An ecommerce website admin panel process typically involves managing products, orders, customers, and payments.
The admin panel may include features such as adding/editing products, managing inventory, processing orders, managing customer accounts, and generating reports.
The process may also involve integrating with payment gateways and shipping providers.
Example 2: Generating Entity Relationship Diagrams
When asked to show a company management system, GPT-4 generates a comprehensive diagram that outlines the entities involved in such a system (along with their respective attributes and relationships), respectively: Company, Employee, Department, and Project. The diagram generated by GPT-4 is highly detailed and provides a clear visual representation of the system.
Request: Show me a company management system
Response GPT-4:
Company
String id
String name
String address
String industry
Company {1}-contains-{1..n} Employee
Company {1}-has-{1..n} Department
Company {1}-undertakes-{1..n} Project
Employee
String id
String firstName
String lastName
String email
String phone
String position
String department
String salary
String startDate
String endDate
Employee {1}-memberOf-{1} Department
Employee {1..n}-participatesIn-{1..n} Project
Department
String id
String name
Department {1}-managedBy-{1} Employee (manager)
Department {1}-includes-{1..n} Employee
Project
String id
String name
String description
String startDate
String endDate
String status
Project {1}-involves-{1..n} Employee (teamMembers)
After minor changes (we removed the underlined), this diagram was provided by Gleek:
In contrast, GPT-3.5-turbo generates a much simpler diagram that includes only the entities and their attributes, without specifying their relationships. The diagram generated by GPT-3.5-turbo requires a significant amount of syntax changes to produce a legible visualization.
Response GPT-3.5-turbo:
EntityOne: Company
Datatype id
Datatype name
Datatype address
Datatype industry
EntityOne {1}-relationDescription-{1..n} EntityTwo
EntityTwo: Employee
Datatype id
Datatype firstName
Datatype lastName
Datatype email
Datatype phone
Datatype position
Datatype department
Datatype salary
Datatype startDate
Datatype endDate
EntityTwo: Department
Datatype id
Datatype name
EntityTwo {1}-relationDescription-{1} EntityTwo
EntityTwo: Project
Datatype id
Datatype name
Datatype description
Datatype startDate
Datatype endDate
Datatype status
EntityTwo {1}-relationDescription-{1..n} EntityTwo
After quite a lot syntax changes (underlined), outputted diagram was:
Overall, the comparison between GPT-4 and GPT-3.5-turbo shows that while both models are capable of generating diagrams, GPT-4 provides more detailed and comprehensive outputs compared to GPT-3.5-turbo. As the technology continues to advance, we can expect that future iterations of AI language processing models will continue to improve in generating high-quality visual representations of complex processes.
But what does this all mean for you as a developer or a business owner? How can you leverage these language models and the advancements in AI technology to your advantage? This is where Blocshop comes in.
Harnessing the Power of AI with a dedicated team of AI software developers
At Blocshop, we pride ourselves on staying ahead of the curve when it comes to cutting-edge technology. As a leading custom software development studio, our team of experienced professionals is dedicated to providing the most advanced AI solutions available today.
As part of our ongoing commitment to innovation, we have closely examined the differences between various LLMs to be able to create even more powerful and intelligent applications for your business based on our findings.
Using our extensive tech stack, including Angular, Microsoft Azure, Ionic, Kotlin, Kubernetes, Progressive Web Apps, MongoDB, Java programming language, Amazon Web Services, Microsoft SQL Server, React, Net Core, LLM, and of course the OpenAI products, we're constantly pushing the boundaries of what's possible in custom software development.
At Blocshop, we understand that the future of business relies on harnessing the power of AI. By staying informed about the latest advancements in large language models (LLM) development, we ensure that our clients receive the most innovative and effective solutions for their unique needs.
Choose Blocshop as your custom AI software development partner and gain an unfair advantage in today's drastically changing competitive marketplace.
Learn more from our insights
NOVEMBER 20, 2025 • 7 min read
The ultimate CTO checklist for planning a custom software or AI project in 2026
In 2026, planning a successful project means understanding five essential dimensions before any code is written. These five questions define scope, architecture, delivery speed, and budget more accurately than any traditional project brief.
NOVEMBER 13, 2025 • 7 min read
The quiet cost of AI: shadow compute budgets and the new DevOps blind spot
AI projects rarely fail because the model “isn’t smart enough.” They fail because the money meter spins where few teams are watching: GPU hours, token bills, data egress, and serving inefficiencies that quietly pile up after launch.
NOVEMBER 3, 2025 • 7 min read
CE marking software under the EU AI Act – who needs it and how to prepare a conformity assessment
From 2026, AI systems classified as high-risk under the EU Artificial Intelligence Act (Regulation (EU) 2024/1689) will have to undergo a conformity assessment and obtain a CE marking before being placed on the EU market or put into service.
October 19, 2025 • 7 min read
EU and UK AI regulation compared: implications for software, data, and AI projects
Both the European Union and the United Kingdom are shaping distinct—but increasingly convergent—approaches to AI regulation.
For companies developing or deploying AI solutions across both regions, understanding these differences is not an academic exercise. It directly affects how software and data projects are planned, documented, and maintained.
October 9, 2025 • 5 min read
When AI and GDPR meet: navigating the tension between AI and data protection
When AI-powered systems process or generate personal data, they enter a regulatory minefield — especially under the EU’s General Data Protection Regulation (GDPR) and the emerging EU AI Act regime
September 17, 2025 • 4 min read
6 AI integration use cases enterprises can adopt for automation and decision support
The question for most companies is no longer if they should use AI, but where it will bring a measurable impact.
NOVEMBER 13, 2025 • 7 min read
The quiet cost of AI: shadow compute budgets and the new DevOps blind spot
AI projects rarely fail because the model “isn’t smart enough.” They fail because the money meter spins where few teams are watching: GPU hours, token bills, data egress, and serving inefficiencies that quietly pile up after launch.
NOVEMBER 13, 2025 • 7 min read
The quiet cost of AI: shadow compute budgets and the new DevOps blind spot
AI projects rarely fail because the model “isn’t smart enough.” They fail because the money meter spins where few teams are watching: GPU hours, token bills, data egress, and serving inefficiencies that quietly pile up after launch.
N 19, 2025 • 7 min read
CE Marking Software Under the EU AI Act – Who Needs It and How to Prepare a Conformity Assessment
When AI-powered systems process or generate personal data, they enter a regulatory minefield — especially under the EU’s General Data Protection Regulation (GDPR) and the emerging EU AI Act regime
NOVEMBER 13, 2025 • 7 min read
The quiet cost of AI: shadow compute budgets and the new DevOps blind spot
When AI-powered systems process or generate personal data, they enter a regulatory minefield — especially under the EU’s General Data Protection Regulation (GDPR) and the emerging EU AI Act regime
N 19, 2025 • 7 min read
CE Marking Software Under the EU AI Act – Who Needs It and How to Prepare a Conformity Assessment
When AI-powered systems process or generate personal data, they enter a regulatory minefield — especially under the EU’s General Data Protection Regulation (GDPR) and the emerging EU AI Act regime
NOVEMBER 13, 2025 • 7 min read
The quiet cost of AI: shadow compute budgets and the new DevOps blind spot
When AI-powered systems process or generate personal data, they enter a regulatory minefield — especially under the EU’s General Data Protection Regulation (GDPR) and the emerging EU AI Act regime
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