![\"\"](\"https:\/\/i0.wp.com\/deepdocs.dev\/wp-content\/uploads\/2025\/08\/7fc2d389fa076a27ed2424bf0e0580f.png?resize=1024%2C626&ssl=1\")
<\/figure>\n\n\n\nGPT-5 is the latest version of OpenAI\u2019s language models, designed to push the boundaries of AI-driven capabilities. It combines deep reasoning, faster responses, and a more intuitive understanding of complex tasks, making it ideal for a wide range of applications, from software development to creative writing. Building on the achievements of GPT-4o, GPT-5 refines its predecessor\u2019s strengths and introduces powerful new features, including advanced tool integration and improved performance in coding, design, and scientific analysis.<\/p>\n\n\n\n
GPT-5 Performance<\/h2>\n\n\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/deepdocs.dev\/wp-content\/uploads\/2025\/08\/6fb9a93b317aa8562ad955f49579567.jpg?resize=361%2C429&ssl=1\")
<\/figure>\n<\/div>\n\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/deepdocs.dev\/wp-content\/uploads\/2025\/08\/chart-5.png?resize=1024%2C789&ssl=1\")
<\/figure>\n<\/div>\n<\/div>\n\n\n\nGPT-5 has shown significant improvements over previous models in real-world benchmarks. In the SWE-bench Verified test, a test to evaluate AI’s ability to solve real-world engineering problems in open-source Python repositories, GPT-5 scored 74.9%, outpacing GPT-4 by over 20 percentage points. While these numbers look impressive, they primarily assess if a model can fix issues within a real GitHub repository, not whether the solution is optimal or maintainable long-term.<\/p>\n\n\n\n
What’s really noteworthy here is that GPT-5’s increased accuracy isn\u2019t just theoretical. The model delivers faster results while using fewer tokens and fewer tool calls leading to a more cost-efficient experience for developers.<\/p>\n\n\n\n
Benchmarks: Reasoning, Math, and Science<\/h2>\n\n\n\n
![](\"https:\/\/i0.wp.com\/assets.apidog.com\/blog-next\/2025\/08\/chart.png?ssl=1\")
<\/p>\n\n\n\n
Efficiency Boosts: Less Is More<\/h2>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/deepdocs.dev\/wp-content\/uploads\/2025\/08\/chart-6.png?resize=1024%2C567&ssl=1\")
<\/figure>\n\n\n\nGPT-5\u2019s efficiency improvements make it an appealing option for teams looking to optimize their workflows. By using 22% fewer tokens than its predecessor, GPT-5 reduces the API calls needed for tasks, making development faster and cheaper. This means developers can rely on GPT-5 for more complex tasks without worrying about unnecessary overhead costs.<\/p>\n\n\n\n
When it comes to code editing, GPT-5 has also outperformed o3 by hitting an 88% accuracy rate, up from 81%. This improvement is crucial for developers looking for reliable code suggestions that can be implemented immediately.<\/p>\n\n\n\n
New GPT-5 API Features<\/h2>\n\n\n\n
GPT-5 not only brings improvements in performance but also introduces several new API features that empower developers with greater control and flexibility. These updates allow for more fine-tuned interactions with the model, making it easier to integrate GPT-5 into different workflows. Let’s dive into the key new features:<\/p>\n\n\n\n
1. Custom Tools<\/strong><\/h3>\n\n\n\nOne of the most anticipated updates in GPT-5 is the ability for developers to use plain text for function calls instead of having to deal with JSON formatting. This change streamlines the process of passing large code blocks to the model and eliminates the frustration of JSON escaping. The input format can now be defined using regular expressions (regex) or formal grammars, which gives developers greater flexibility and control when interacting with the model.<\/p>\n\n\n\n
2. Reasoning Effort Control<\/strong><\/h3>\n\n\n\nThe addition of a reasoning_effort<\/strong> parameter adds a new level of customization to the model\u2019s behavior. With four levels minimal, low, medium, and high developers can now adjust the speed vs. quality trade-off depending on the task at hand. For simple autocomplete tasks, the minimal<\/strong> setting might be sufficient, while more complex, high-level architectural decisions may require a high<\/strong> setting for more thoughtful output.<\/p>\n\n\n\n3. Verbosity Control<\/strong><\/h3>\n\n\n\nVerbosity control gives developers the ability to manage how detailed the model’s responses are. Whether you want just the raw code or a full explanation with context, you can now set the verbosity level to low<\/strong>, medium<\/strong>, or high<\/strong>. This feature is ideal for fine-tuning the balance between development speed and understanding when using GPT-5 in production environments.<\/p>\n\n\n\n4. Longer Context Support<\/strong><\/h3>\n\n\n\nAnother important API improvement is GPT-5\u2019s ability to handle significantly larger context windows. Developers can now work with up to 400,000 tokens<\/strong> (272K input + 128K output) in a single conversation. This is a game-changer for projects involving large codebases or long, complex tasks that require maintaining context over extended periods.<\/p>\n\n\n\n5. Improved Tool Use<\/strong><\/h3>\n\n\n\nGPT-5\u2019s ability to interact with external tools has also seen significant improvement. Whether you\u2019re working with deployment scripts, debugging tasks, or running multi-tool processes, GPT-5 can now manage these more complex workflows with greater accuracy. This enhanced tool integration is especially useful for automated development tasks and system management, where the model can now handle more intricate operations without losing track of the goal.<\/p>\n\n\n\n
6. Availability & Pricing<\/h3>\n\n\n\n
GPT-5 is available in three versions: gpt-5<\/strong>, gpt-5-mini<\/strong>, and gpt-5-nano<\/strong>. Each comes with different pricing structures to cater to various levels of computational power and usage needs. These models support reasoning_effort<\/strong> and verbosity<\/strong> API parameters, custom tools, parallel tool calling, and core API features such as streaming and structured outputs.<\/p>\n\n\n\n\n- gpt-5<\/strong>: $1.25 per 1M input tokens and $10 per 1M output tokens<\/li>\n\n\n\n
- gpt-5-mini<\/strong>: $0.25 per 1M input tokens and $2 per 1M output tokens<\/li>\n\n\n\n
- gpt-5-nano<\/strong>: $0.05 per 1M input tokens and $0.40 per 1M output tokens<\/li>\n\n\n\n
- gpt-5-chat-latest<\/strong> (non-reasoning version): $1.25 per 1M input tokens and $10 per 1M output tokens<\/li>\n<\/ul>\n\n\n\n
These models also support cost-saving features such as prompt caching<\/strong> and Batch API<\/strong>, which help reduce operational expenses for high-volume applications.<\/p>\n\n\n\nHere is a full price chart if you\u2019d like:<\/p>\n\n\n\nModel<\/th> Input Cost<\/th> Cached Input<\/th> Output Cost<\/th> <\/th><\/tr><\/thead> GPT-5<\/strong><\/td>$1.25<\/td> $0.125<\/td> $10.00<\/td> <\/td><\/tr> GPT-5-mini<\/strong><\/td>$0.25<\/td> $0.025<\/td> $2.00<\/td> <\/td><\/tr> GPT-5-nano<\/strong><\/td>$0.05<\/td> $0.005<\/td> $0.40<\/td> <\/td><\/tr> GPT-5-chat-latest<\/strong><\/td>$1.25<\/td> $0.125<\/td> $10.00<\/td> <\/td><\/tr> GPT-4.1<\/strong><\/td>$2.00<\/td> $0.50<\/td> $8.00<\/td> <\/td><\/tr> GPT-4.1-mini<\/strong><\/td>$0.40<\/td> $0.10<\/td> $1.60<\/td> <\/td><\/tr> GPT-4.0<\/strong><\/td>$2.50<\/td> $1.25<\/td> $10.00<\/td> <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nGPT-5 and Frontend Development<\/h2>\n\n\n\n
OpenAI has placed a strong emphasis on frontend development & design with GPT-5. In fact, preliminary reports from developers indicate that that GPT-5’s frontend output is vastly superior than the preent state-or-the-art models in 70% of cases. The model has been particularly praised for its ability to generate fully functional applications from a single prompt, like landing pages, interactive tools, and even games.<\/p>\n\n\n\n
However, as with any automated tool, it\u2019s not just about generating something quickly. Real frontend work involves responsive design, usability, integration with existing platforms, and maintaining code that is both clean and scalable. GPT-5 does well in initial stages, but the real test will be in its long-term integration into production-level projects.<\/p>\n\n\n\n
Here are a few examples we saw being built with GPT-5<\/p>\n\n\n\n
Example Projects Built with GPT-5<\/h3>\n\n\n\n
Espresso Lab Website<\/strong> A clean and modern coffee shop website, focusing on user experience and smooth animations. GPT-5 made quick work of this design with interactive product displays.<\/p>\n\n\n\n
<\/figure>\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\nGPT-5 has shown significant improvements over previous models in real-world benchmarks. In the SWE-bench Verified test, a test to evaluate AI’s ability to solve real-world engineering problems in open-source Python repositories, GPT-5 scored 74.9%, outpacing GPT-4 by over 20 percentage points. While these numbers look impressive, they primarily assess if a model can fix issues within a real GitHub repository, not whether the solution is optimal or maintainable long-term.<\/p>\n\n\n\n
What’s really noteworthy here is that GPT-5’s increased accuracy isn\u2019t just theoretical. The model delivers faster results while using fewer tokens and fewer tool calls leading to a more cost-efficient experience for developers.<\/p>\n\n\n\n
Benchmarks: Reasoning, Math, and Science<\/h2>\n\n\n\n
<\/p>\n\n\n\n
Efficiency Boosts: Less Is More<\/h2>\n\n\n\n<\/figure>\n\n\n\nGPT-5\u2019s efficiency improvements make it an appealing option for teams looking to optimize their workflows. By using 22% fewer tokens than its predecessor, GPT-5 reduces the API calls needed for tasks, making development faster and cheaper. This means developers can rely on GPT-5 for more complex tasks without worrying about unnecessary overhead costs.<\/p>\n\n\n\n
When it comes to code editing, GPT-5 has also outperformed o3 by hitting an 88% accuracy rate, up from 81%. This improvement is crucial for developers looking for reliable code suggestions that can be implemented immediately.<\/p>\n\n\n\n
New GPT-5 API Features<\/h2>\n\n\n\n
GPT-5 not only brings improvements in performance but also introduces several new API features that empower developers with greater control and flexibility. These updates allow for more fine-tuned interactions with the model, making it easier to integrate GPT-5 into different workflows. Let’s dive into the key new features:<\/p>\n\n\n\n
1. Custom Tools<\/strong><\/h3>\n\n\n\nOne of the most anticipated updates in GPT-5 is the ability for developers to use plain text for function calls instead of having to deal with JSON formatting. This change streamlines the process of passing large code blocks to the model and eliminates the frustration of JSON escaping. The input format can now be defined using regular expressions (regex) or formal grammars, which gives developers greater flexibility and control when interacting with the model.<\/p>\n\n\n\n
2. Reasoning Effort Control<\/strong><\/h3>\n\n\n\nThe addition of a reasoning_effort<\/strong> parameter adds a new level of customization to the model\u2019s behavior. With four levels minimal, low, medium, and high developers can now adjust the speed vs. quality trade-off depending on the task at hand. For simple autocomplete tasks, the minimal<\/strong> setting might be sufficient, while more complex, high-level architectural decisions may require a high<\/strong> setting for more thoughtful output.<\/p>\n\n\n\n3. Verbosity Control<\/strong><\/h3>\n\n\n\nVerbosity control gives developers the ability to manage how detailed the model’s responses are. Whether you want just the raw code or a full explanation with context, you can now set the verbosity level to low<\/strong>, medium<\/strong>, or high<\/strong>. This feature is ideal for fine-tuning the balance between development speed and understanding when using GPT-5 in production environments.<\/p>\n\n\n\n4. Longer Context Support<\/strong><\/h3>\n\n\n\nAnother important API improvement is GPT-5\u2019s ability to handle significantly larger context windows. Developers can now work with up to 400,000 tokens<\/strong> (272K input + 128K output) in a single conversation. This is a game-changer for projects involving large codebases or long, complex tasks that require maintaining context over extended periods.<\/p>\n\n\n\n5. Improved Tool Use<\/strong><\/h3>\n\n\n\nGPT-5\u2019s ability to interact with external tools has also seen significant improvement. Whether you\u2019re working with deployment scripts, debugging tasks, or running multi-tool processes, GPT-5 can now manage these more complex workflows with greater accuracy. This enhanced tool integration is especially useful for automated development tasks and system management, where the model can now handle more intricate operations without losing track of the goal.<\/p>\n\n\n\n
6. Availability & Pricing<\/h3>\n\n\n\n
GPT-5 is available in three versions: gpt-5<\/strong>, gpt-5-mini<\/strong>, and gpt-5-nano<\/strong>. Each comes with different pricing structures to cater to various levels of computational power and usage needs. These models support reasoning_effort<\/strong> and verbosity<\/strong> API parameters, custom tools, parallel tool calling, and core API features such as streaming and structured outputs.<\/p>\n\n\n\n\n- gpt-5<\/strong>: $1.25 per 1M input tokens and $10 per 1M output tokens<\/li>\n\n\n\n
- gpt-5-mini<\/strong>: $0.25 per 1M input tokens and $2 per 1M output tokens<\/li>\n\n\n\n
- gpt-5-nano<\/strong>: $0.05 per 1M input tokens and $0.40 per 1M output tokens<\/li>\n\n\n\n
- gpt-5-chat-latest<\/strong> (non-reasoning version): $1.25 per 1M input tokens and $10 per 1M output tokens<\/li>\n<\/ul>\n\n\n\n
These models also support cost-saving features such as prompt caching<\/strong> and Batch API<\/strong>, which help reduce operational expenses for high-volume applications.<\/p>\n\n\n\nHere is a full price chart if you\u2019d like:<\/p>\n\n\n\nModel<\/th> Input Cost<\/th> Cached Input<\/th> Output Cost<\/th> <\/th><\/tr><\/thead> GPT-5<\/strong><\/td>$1.25<\/td> $0.125<\/td> $10.00<\/td> <\/td><\/tr> GPT-5-mini<\/strong><\/td>$0.25<\/td> $0.025<\/td> $2.00<\/td> <\/td><\/tr> GPT-5-nano<\/strong><\/td>$0.05<\/td> $0.005<\/td> $0.40<\/td> <\/td><\/tr> GPT-5-chat-latest<\/strong><\/td>$1.25<\/td> $0.125<\/td> $10.00<\/td> <\/td><\/tr> GPT-4.1<\/strong><\/td>$2.00<\/td> $0.50<\/td> $8.00<\/td> <\/td><\/tr> GPT-4.1-mini<\/strong><\/td>$0.40<\/td> $0.10<\/td> $1.60<\/td> <\/td><\/tr> GPT-4.0<\/strong><\/td>$2.50<\/td> $1.25<\/td> $10.00<\/td> <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nGPT-5 and Frontend Development<\/h2>\n\n\n\n
OpenAI has placed a strong emphasis on frontend development & design with GPT-5. In fact, preliminary reports from developers indicate that that GPT-5’s frontend output is vastly superior than the preent state-or-the-art models in 70% of cases. The model has been particularly praised for its ability to generate fully functional applications from a single prompt, like landing pages, interactive tools, and even games.<\/p>\n\n\n\n
However, as with any automated tool, it\u2019s not just about generating something quickly. Real frontend work involves responsive design, usability, integration with existing platforms, and maintaining code that is both clean and scalable. GPT-5 does well in initial stages, but the real test will be in its long-term integration into production-level projects.<\/p>\n\n\n\n
Here are a few examples we saw being built with GPT-5<\/p>\n\n\n\n
Example Projects Built with GPT-5<\/h3>\n\n\n\n
Espresso Lab Website<\/strong> A clean and modern coffee shop website, focusing on user experience and smooth animations. GPT-5 made quick work of this design with interactive product displays.<\/p>\n\n\n\n
<\/figure>\n\n\n\nGPT-5\u2019s efficiency improvements make it an appealing option for teams looking to optimize their workflows. By using 22% fewer tokens than its predecessor, GPT-5 reduces the API calls needed for tasks, making development faster and cheaper. This means developers can rely on GPT-5 for more complex tasks without worrying about unnecessary overhead costs.<\/p>\n\n\n\n
When it comes to code editing, GPT-5 has also outperformed o3 by hitting an 88% accuracy rate, up from 81%. This improvement is crucial for developers looking for reliable code suggestions that can be implemented immediately.<\/p>\n\n\n\n
New GPT-5 API Features<\/h2>\n\n\n\n
GPT-5 not only brings improvements in performance but also introduces several new API features that empower developers with greater control and flexibility. These updates allow for more fine-tuned interactions with the model, making it easier to integrate GPT-5 into different workflows. Let’s dive into the key new features:<\/p>\n\n\n\n
1. Custom Tools<\/strong><\/h3>\n\n\n\nOne of the most anticipated updates in GPT-5 is the ability for developers to use plain text for function calls instead of having to deal with JSON formatting. This change streamlines the process of passing large code blocks to the model and eliminates the frustration of JSON escaping. The input format can now be defined using regular expressions (regex) or formal grammars, which gives developers greater flexibility and control when interacting with the model.<\/p>\n\n\n\n
2. Reasoning Effort Control<\/strong><\/h3>\n\n\n\nThe addition of a reasoning_effort<\/strong> parameter adds a new level of customization to the model\u2019s behavior. With four levels minimal, low, medium, and high developers can now adjust the speed vs. quality trade-off depending on the task at hand. For simple autocomplete tasks, the minimal<\/strong> setting might be sufficient, while more complex, high-level architectural decisions may require a high<\/strong> setting for more thoughtful output.<\/p>\n\n\n\n3. Verbosity Control<\/strong><\/h3>\n\n\n\nVerbosity control gives developers the ability to manage how detailed the model’s responses are. Whether you want just the raw code or a full explanation with context, you can now set the verbosity level to low<\/strong>, medium<\/strong>, or high<\/strong>. This feature is ideal for fine-tuning the balance between development speed and understanding when using GPT-5 in production environments.<\/p>\n\n\n\n4. Longer Context Support<\/strong><\/h3>\n\n\n\nAnother important API improvement is GPT-5\u2019s ability to handle significantly larger context windows. Developers can now work with up to 400,000 tokens<\/strong> (272K input + 128K output) in a single conversation. This is a game-changer for projects involving large codebases or long, complex tasks that require maintaining context over extended periods.<\/p>\n\n\n\n5. Improved Tool Use<\/strong><\/h3>\n\n\n\nGPT-5\u2019s ability to interact with external tools has also seen significant improvement. Whether you\u2019re working with deployment scripts, debugging tasks, or running multi-tool processes, GPT-5 can now manage these more complex workflows with greater accuracy. This enhanced tool integration is especially useful for automated development tasks and system management, where the model can now handle more intricate operations without losing track of the goal.<\/p>\n\n\n\n
6. Availability & Pricing<\/h3>\n\n\n\n
GPT-5 is available in three versions: gpt-5<\/strong>, gpt-5-mini<\/strong>, and gpt-5-nano<\/strong>. Each comes with different pricing structures to cater to various levels of computational power and usage needs. These models support reasoning_effort<\/strong> and verbosity<\/strong> API parameters, custom tools, parallel tool calling, and core API features such as streaming and structured outputs.<\/p>\n\n\n\n\n- gpt-5<\/strong>: $1.25 per 1M input tokens and $10 per 1M output tokens<\/li>\n\n\n\n
- gpt-5-mini<\/strong>: $0.25 per 1M input tokens and $2 per 1M output tokens<\/li>\n\n\n\n
- gpt-5-nano<\/strong>: $0.05 per 1M input tokens and $0.40 per 1M output tokens<\/li>\n\n\n\n
- gpt-5-chat-latest<\/strong> (non-reasoning version): $1.25 per 1M input tokens and $10 per 1M output tokens<\/li>\n<\/ul>\n\n\n\n
These models also support cost-saving features such as prompt caching<\/strong> and Batch API<\/strong>, which help reduce operational expenses for high-volume applications.<\/p>\n\n\n\nHere is a full price chart if you\u2019d like:<\/p>\n\n\n\nModel<\/th> Input Cost<\/th> Cached Input<\/th> Output Cost<\/th> <\/th><\/tr><\/thead> GPT-5<\/strong><\/td>$1.25<\/td> $0.125<\/td> $10.00<\/td> <\/td><\/tr> GPT-5-mini<\/strong><\/td>$0.25<\/td> $0.025<\/td> $2.00<\/td> <\/td><\/tr> GPT-5-nano<\/strong><\/td>$0.05<\/td> $0.005<\/td> $0.40<\/td> <\/td><\/tr> GPT-5-chat-latest<\/strong><\/td>$1.25<\/td> $0.125<\/td> $10.00<\/td> <\/td><\/tr> GPT-4.1<\/strong><\/td>$2.00<\/td> $0.50<\/td> $8.00<\/td> <\/td><\/tr> GPT-4.1-mini<\/strong><\/td>$0.40<\/td> $0.10<\/td> $1.60<\/td> <\/td><\/tr> GPT-4.0<\/strong><\/td>$2.50<\/td> $1.25<\/td> $10.00<\/td> <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nGPT-5 and Frontend Development<\/h2>\n\n\n\n
OpenAI has placed a strong emphasis on frontend development & design with GPT-5. In fact, preliminary reports from developers indicate that that GPT-5’s frontend output is vastly superior than the preent state-or-the-art models in 70% of cases. The model has been particularly praised for its ability to generate fully functional applications from a single prompt, like landing pages, interactive tools, and even games.<\/p>\n\n\n\n
However, as with any automated tool, it\u2019s not just about generating something quickly. Real frontend work involves responsive design, usability, integration with existing platforms, and maintaining code that is both clean and scalable. GPT-5 does well in initial stages, but the real test will be in its long-term integration into production-level projects.<\/p>\n\n\n\n
Here are a few examples we saw being built with GPT-5<\/p>\n\n\n\n
Example Projects Built with GPT-5<\/h3>\n\n\n\n
Espresso Lab Website<\/strong> A clean and modern coffee shop website, focusing on user experience and smooth animations. GPT-5 made quick work of this design with interactive product displays.<\/p>\n\n\n\n
The addition of a reasoning_effort<\/strong> parameter adds a new level of customization to the model\u2019s behavior. With four levels minimal, low, medium, and high developers can now adjust the speed vs. quality trade-off depending on the task at hand. For simple autocomplete tasks, the minimal<\/strong> setting might be sufficient, while more complex, high-level architectural decisions may require a high<\/strong> setting for more thoughtful output.<\/p>\n\n\n\n Verbosity control gives developers the ability to manage how detailed the model’s responses are. Whether you want just the raw code or a full explanation with context, you can now set the verbosity level to low<\/strong>, medium<\/strong>, or high<\/strong>. This feature is ideal for fine-tuning the balance between development speed and understanding when using GPT-5 in production environments.<\/p>\n\n\n\n Another important API improvement is GPT-5\u2019s ability to handle significantly larger context windows. Developers can now work with up to 400,000 tokens<\/strong> (272K input + 128K output) in a single conversation. This is a game-changer for projects involving large codebases or long, complex tasks that require maintaining context over extended periods.<\/p>\n\n\n\n GPT-5\u2019s ability to interact with external tools has also seen significant improvement. Whether you\u2019re working with deployment scripts, debugging tasks, or running multi-tool processes, GPT-5 can now manage these more complex workflows with greater accuracy. This enhanced tool integration is especially useful for automated development tasks and system management, where the model can now handle more intricate operations without losing track of the goal.<\/p>\n\n\n\n GPT-5 is available in three versions: gpt-5<\/strong>, gpt-5-mini<\/strong>, and gpt-5-nano<\/strong>. Each comes with different pricing structures to cater to various levels of computational power and usage needs. These models support reasoning_effort<\/strong> and verbosity<\/strong> API parameters, custom tools, parallel tool calling, and core API features such as streaming and structured outputs.<\/p>\n\n\n\n These models also support cost-saving features such as prompt caching<\/strong> and Batch API<\/strong>, which help reduce operational expenses for high-volume applications.<\/p>\n\n\n\n Here is a full price chart if you\u2019d like:<\/p>\n\n\n\n OpenAI has placed a strong emphasis on frontend development & design with GPT-5. In fact, preliminary reports from developers indicate that that GPT-5’s frontend output is vastly superior than the preent state-or-the-art models in 70% of cases. The model has been particularly praised for its ability to generate fully functional applications from a single prompt, like landing pages, interactive tools, and even games.<\/p>\n\n\n\n However, as with any automated tool, it\u2019s not just about generating something quickly. Real frontend work involves responsive design, usability, integration with existing platforms, and maintaining code that is both clean and scalable. GPT-5 does well in initial stages, but the real test will be in its long-term integration into production-level projects.<\/p>\n\n\n\n Here are a few examples we saw being built with GPT-5<\/p>\n\n\n\n Espresso Lab Website<\/strong> A clean and modern coffee shop website, focusing on user experience and smooth animations. GPT-5 made quick work of this design with interactive product displays.<\/p>\n\n\n\n3. Verbosity Control<\/strong><\/h3>\n\n\n\n
4. Longer Context Support<\/strong><\/h3>\n\n\n\n
5. Improved Tool Use<\/strong><\/h3>\n\n\n\n
6. Availability & Pricing<\/h3>\n\n\n\n
\n
Model<\/th> Input Cost<\/th> Cached Input<\/th> Output Cost<\/th> <\/th><\/tr><\/thead> GPT-5<\/strong><\/td> $1.25<\/td> $0.125<\/td> $10.00<\/td> <\/td><\/tr> GPT-5-mini<\/strong><\/td> $0.25<\/td> $0.025<\/td> $2.00<\/td> <\/td><\/tr> GPT-5-nano<\/strong><\/td> $0.05<\/td> $0.005<\/td> $0.40<\/td> <\/td><\/tr> GPT-5-chat-latest<\/strong><\/td> $1.25<\/td> $0.125<\/td> $10.00<\/td> <\/td><\/tr> GPT-4.1<\/strong><\/td> $2.00<\/td> $0.50<\/td> $8.00<\/td> <\/td><\/tr> GPT-4.1-mini<\/strong><\/td> $0.40<\/td> $0.10<\/td> $1.60<\/td> <\/td><\/tr> GPT-4.0<\/strong><\/td> $2.50<\/td> $1.25<\/td> $10.00<\/td> <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n GPT-5 and Frontend Development<\/h2>\n\n\n\n
Example Projects Built with GPT-5<\/h3>\n\n\n\n
![\"\"](\"https:\/\/i0.wp.com\/deepdocs.dev\/wp-content\/uploads\/2025\/08\/Screenshot-2025-08-08-152134.png?resize=1024%2C659&ssl=1\")
<\/figure>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/deepdocs.dev\/wp-content\/uploads\/2025\/08\/Screenshot-2025-08-08-152220.png?resize=1024%2C647&ssl=1\")
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