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In the ever-evolving world of artificial intelligence, two models have been making waves: Chat GPT4 and GPT4 Turbo. Both developed by OpenAI, these models are the latest in a line of advancements designed to push the boundaries of what AI can achieve.

Chat GPT4, released on November 6th, is the newest model, hot on the heels of its predecessor, GPT4 Turbo. While both models share a common lineage, they each bring unique features and capabilities to the table. The question on everyone’s mind is: which one outperforms the other?

Chat GPT4 vs GPT4 Turbo

FeatureChat GPT4GPT4 Turbo
Context Window8k tokens (around 24 pages of text)128k tokens (around 300 pages of text)
MultimodalityText-only inputAccepts both text and image inputs
Pricing (per token)Input: $0.03, Output: $0.06Input: $0.01, Output: $0.03
Other FeaturesChat optimizationImproved instruction following, JSON mode, reproducible outputs, parallel function calling


  • GPT4 Turbo is a newer and more advanced version of Chat GPT4.
  • GPT4 Turbo has a significantly larger context window, allowing it to understand and respond to prompts based on a much broader range of information.
  • GPT4 Turbo can accept both text and images as input, while Chat GPT4 is limited to text input only.
  • GPT4 Turbo is currently cheaper to use than Chat GPT4.

It’s important to note that GPT4 Turbo is still a preview model and may not be available to all users yet.

Whether you’re a developer fine-tuning your AI models or a tech enthusiast curious about the latest developments, this comparison of Chat GPT4 and GPT4 Turbo will shed light on their strengths, weaknesses, and overall performance. Let’s dive into the world of AI and see how these two models stack up against each other.

Key Takeaways

  • OpenAI’s Chat GPT4 and GPT4 Turbo are highly efficient AI models with unique strengths and weaknesses, setting new benchmarks in the AI industry.
  • Chat GPT4 excels in handling complex tasks, adhering more to user instructions, and performing well in generating HTML, specifying HTML tag attributes, and determining the tone and style of text.
  • Despite being impressive, Chat GPT4 may inaccurately use some non-English words and sometimes provide inaccurate responses, but these issues can be fixed by asking the model to output its reasoning path.
  • GPT4 Turbo is known for its cost-effectiveness in terms of training and usage costs, with plans to introduce a fine-tuning interface to enhance user experience.
  • GPT4 Turbo can handle prompt intricacies more proficiently compared to Chat GPT4, which sometimes partially ignores specific HTML tags or text data during the generation process.
  • When comparing these models, GPT4 Turbo stands short in terms of context window length compared to the industry-leading 200k token context window of Claude 21, limiting its ability to process larger documents.

Overview of Chat GPT4

Diving into the ocean of AI, we get to see fascinating waves of technology. Chat GPT4, an AI model developed by OpenAI, is without a doubt a wave worth surfing. This model extends the boundary of the AI world, showing us just how versatile it can be.

In comparison with its predecessor, GPT4 Turbo, GPT4 has features that make it stand out in a crowd. One of such features includes its ability to handle complex prompts more efficiently. When tasked with generating HTML, specifying HTML tag attributes or determining the tone and style of text data, Chat GPT4 performs quite well. It doesn’t often ignore specified directions like its older model, making it more reliable for the user.

For example, creating HTML and texts were problems associated with GPT4 Turbo and even its ancestor, GPT3.5. They struggled with dealing with the nuances prompts contained but with the advent of Chat GPT4, this no longer seems to be a daunting task.

However, Chat GPT4 isn’t without its shortcomings. Despite its vast terminology awareness in non-English languages, it sometimes uses words incorrectly. Users have noted that it can be fixed by asking the model to output its reasoning path.

User experiences often vary. Some users have reported receiving inaccurate responses from GPT4 Turbo. Interestingly, upon asking the same question again, the results were more accurate. Less token usage in comparison with other models was also noticed when GPT4 was used, indicating its cost-effectiveness.

Innovation never stops and OpenAI has hinted at an experimental access program for GPT4 finetuning. Some preliminary results suggest that GPT4 finetuning still needs work for significant improvement over the base model. No doubt OpenAI is committed to pushing the boundaries for this impressive AI model further.

Key Features of Chat GPT4

Chat GPT4, the latest AI model release from OpenAI, has shown its strong efficiency in handling complex tasks. It is even skilled in generating HTML and determining the tone of the spoken text. What sets it apart from its predecessor, GPT4 Turbo, is its enhanced adherence to user instructions. With it, unwanted ‘instruction-ignoring’ incidences are lesser.

However, the evolution isn’t without flaws. Some of these include occasional misuse of non-English words and occasional inaccuracies. Those inaccuracies, according to user feedback, are found in prompt responses. But don’t fret—they can be corrected by simply asking for the model’s reasoning.

Users of ChatGPT plus, the pro variant, have been granted access to GPT4 turbo, enhancing its capability. At the forefront of its strengths is enabling users to make the most of the full power of the model through the API—an offering typical of OpenAI’s models, like the good old GPT4.

Remember, it’s always about more than just interaction. With Chat GPT4, interactions are less limited than before. This model wards off common challenges like limited response sizes, overloading due to larger context windows, and the inability of users to change the model’s temperament, which determines creativity.

One hitch remains though—the lack of interaction flexibility in a chat interface typical of AI models can be seen in Chat GPT4 as well. It is something potential users may want to take into consideration.

Pros and Cons of Chat GPT4

Delving straight into the heart of the matter, Chat GPT4 displays an impressive knack for handling tasks deemed complex. A key strength lies in its capacity to generate HTML effectively and analyze the tone of the text. This capability is what sets it apart from previous renditions.

GPT4’s heightened attention to user instructions significantly reduces instances where prompts are ignored. This makes it a reliable tool in many scenarios. Moreover, interestingly, it’s been reported that the daily token cost when using GPT4 is generally lower compared to other days, indicating potential cost-effectiveness over time.

However, nothing is without flaw and Chat GPT4 is no exception. A close inspection reveals potential drawbacks such as occasional misuse of non-English words. This can lead to certain inaccuracies in the output, but such issues can be quick-fixes by simply asking the model for its reasoning. That said, it’s still an important factor users must remain aware of.

Another point is GPT4’s potential issues with prompt accuracy. Although it shines in user instruction adherence, instances arise where responses fail to truly capture the intent of the prompt. It’s found that repeating the question often results in a more accurate response, but this iterative process can be time-consuming and less efficient.

While it does display superb ability in many realms, GPT4 faces challenges when it comes to flexibility within a chat interface. This overall interaction flexibility can affect the user’s experience and is thus crucial.

When weighing the pros and cons, it’s also worth highlighting Claude 21’s model, which pulls ahead with a 200k token context window. This context length nearly doubles that of GPT4 Turbo, enabling it to handle larger documents up to 150,000 tokens long.

As we navigate the digital realm of AI models, Chat GPT4 indeed proves to be a dynamic tool. Yet, as with any tool, it’s about how well it serves the user’s specific needs and goals. It’s not about whether it’s the best, but rather if it’s the best fit. This user-centric perspective can form the basis for successful adoption and optimization.

GPT Versions
GPT Versions

Overview of GPT4 Turbo

The emergence of GPT4 Turbo has been a game-changer in the AI industry. Compared to its predecessor, GPT3.5, it presents an astronomical leap in accuracy and efficiency. However, it’s not without its share of challenges.

GPT4 Turbo: Cost-Effectiveness and Functionality

Undeniably, one of the critical aspects to consider when fine-tuning an AI model is the cost. When we break down the cost elements, GPT4 Turbo’s training and usage costs come to a total of 0.0008 per 1000 tokens for training, 0.0012 per 1000 tokens for usage input, and 0.0016 per 1000 input tokens for usage output. Comparatively, GPT3.5 Turbo 4K and 16K configurations bear slightly different costs for input and output: 0.0003 and 0.0006 respectively.

Configuration Training Cost (per 1000 tokens) Usage Input Cost (per 1000 tokens) Usage Output Cost (per 1000 tokens)
GPT4 Turbo 0.0008 0.0012 0.0016
GPT3.5 Turbo 4K and 16K 0.0008 0.0003 0.0006

Preparing, uploading data, creating an adjustment job and using a fine-tuned model are integral steps of the fine-tuning process, thereby making GPT4 Turbo an accessible AI tool for developers. OpenAI plans to launch a user interface that will make the fine-tuning process efficient and seamless, maintaining the same shared rarity limits as the initial model for production use.

GPT4 Turbo vs. GPT4 Finetuning

In comparison to the substantial gains that GPT3.5 experienced with fine-tuning, the progression with GPT4 has been an uphill battle. Preliminary results indicate that GPT4 fine-tuning requires more work for achieving significant improvements. As quality and safety for GPT4 increases, developers who are using GPT3.5 fine-tuning will have the opportunity to apply to the GPT4 program within their fine-tuning console.

Key Features of GPT4 Turbo

The GPT4 Turbo has brought noteworthy advancements to the AI industry, showing duly significant strides in both accuracy and efficiency. Among the key features that make it stand out is its cost-effectiveness, especially when considering training and usage costs. The breakdown of the tuning cost goes as such:

Type Cost (per 1000 tokens)
Initial Training 0.008
Usage Input 0.012
Usage Output 0.016

For instance, a GPT3.5 Turbo model undergoing a tune-up job would bear an expected cost of 240 tokens per 100,000 in the training file. Expect this file to be useful for three epochs.

Looking ahead, OpenAI plans to roll out a fine-tuning user interface. This platform aims to facilitate developers in accessing vital information related to ongoing fine-tuning jobs, finished models, and more. Post-completion of the tuning process, the fine-tuned model can expect the same shared rarity limits as the underlying one, making this model ready for production use.

Another key aspect of GPT4 Turbo is the robust experimental access program being developed for future fine-tuning. Early results suggest that more effort is needed to achieve palpable improvements with GPT4 fine-tuning compared to GPT3.5. But as quality and safety measures continue to improve, developers already using GPT3.5 will have the chance to transition to GPT4, making this adaptation seamlessly align with their current development practices.

Not to overlook, one of the strong features where GPT4 Turbo contrast is in terms of its context window length. Unlike the industry-leading 200k token context window of Claude 21, GPT4 Turbo stands a bit short with its 120k token limit. Though sizable, it falls behind when processing large documents of up to 150,000 words or about 500 pages long. However, it’s significant to note that GPT4 Turbo still brings unparalleled potential to process weighty, critical documents like research papers, financial reports, and literature works. This positions GPT4 Turbo as a potent tool in the realm of AI, laying the foundation for the next generation of language models.

Pros and Cons of GPT4 Turbo

When it comes to efficiency and accuracy, GPT4 Turbo shines. An important aspect about the model is its cost-effectiveness in the AI industry. The monetary implications of fine-tuning in this system are broken down into two parts: the initial training cost and the usage cost. In this regard, you’re looking at a training cost of 00081000 tokens, a usage input cost of 00121000 tokens, and a usage output cost of 00161000 tokens.

Cost Factor Token Count
Training Cost 00081000
Usage Input Cost 00121000
Usage Output Cost 00161000

An example sheds light on this cost structure. Consider a GPT3.5 Turbo model set for a tune-up, the expected cost stands at 240 training file tokens for three epochs. This illustrates GPT4 Turbo’s cost-friendly nature, especially when compared to similar models.

OpenAI’s commitment to enhancing user experience manifests in its plans to roll out a fine-tuning interface in the future. This tool aims to make it easy for developers to access critical information about their ongoing fine-tuning jobs, completed models and more. Once the fine-tuning process wraps up, it aligns with shared rarity limits of the underlying model, ready for production use.

While GPT4 Turbo has its strengths, it must be noted that there are areas where other models pull ahead. Claude 21, for instance, boasts an industry-leading 200k token context window. This impressive range allows Claude 21 to process massive documents nearly twice the length that GPT4 Turbo can handle, at up to 150000 words or up to 500 pages long.

Model Token Context Window
GPT4 Turbo 128k
Claude 21 200k

GPT4 Turbo’s 128k token context capacity, although large, is overshadowed by Claude’s prowess. Despite its lesser capability, GPT4 Turbo is an advantageous tool in the AI domain, particularly for document processing tasks. Users should weigh these characteristics when considering this option for their needs.

Comparison between Chat GPT4 and GPT4 Turbo

When talking about giants in the artificial intelligence industry, few names are as prominent as GPT4 Turbo and Chat GPT4. Their capabilities and efficiency in processing language make them valuable tools in today’s AI-dominated landscape. However, they’re not created equal, and understanding their differences can help users maximize their benefits.

Diving into the specifics, the first notable difference between this AI pair is their token context window size. Claude 21, a part of the chat GPT4 series, boasts an industry-leading 200,000 token context window. This nearly doubles the context length of GPT4 Turbo, which has a substantially large but comparatively lesser 128,000 token context window. This aspect enables Claude 21 to handle extensive data, processing documents up to 150,000 words, or 500 pages, long. The ability to analyze in-depth textual information from long documents like research papers, financial reports, and literature works gives Chat GPT4 an edge.

On the other hand, GPT4 Turbo brings to the table its cost-effectiveness in terms of training and usage costs, making it an attractive choice for developers working on a budget. Training cost is broken down into three components: an initial training cost of 00081000 tokens, a usage input cost of 00121000 tokens, and a usage output cost of 00161000 tokens. For instance, a GPT3.5 Turbo model with a tune-up job will have an expected cost of 240.

OpenAI has also planned the introduction of a fine-tuning interface to enhance user experience. This innovation will offer developers easy access to ongoing fine-tuning jobs, completed models, and more. After the fine-tuning process, the completed model will be ready for production use, adhering to the same shared rarity limits as the base model.

Contrasting experiences reveal GPT4 Turbo’s proficiency in handling prompt intricacies, where its predecessor might hit a snag. For example, Chat GPT4 often partially ignores HTML tag attributes and text data in the generation process. GPT4 Turbo does not display the same flaw, adding to its value proposition.

Engaging with these AI models is an iterative process, and understanding their strengths could guide usage. But it’s essential to remember that AI’s capacities expand every day, continually reshaping possibilities.


While Chat GPT4’s larger token context window makes it a powerhouse for handling extensive data, GPT4 Turbo’s cost-effectiveness and proficiency in prompt intricacies can’t be overlooked. It’s clear that both models have their unique strengths, and the choice between them largely depends on individual project requirements. OpenAI’s upcoming fine-tuning interface is set to further enhance the user experience with these models. As the AI industry continues to evolve, so too will the capabilities of models like Chat GPT4 and GPT4 Turbo. The key takeaway here is the importance of staying adaptable and open to the iterative nature of engaging with AI models. This will ensure that developers can harness the full potential of these powerful tools in their AI projects.

Frequently Asked Questions

What are the main differences between GPT4 Turbo and Chat GPT4?

GPT4 Turbo and Chat GPT4 largely differ in their token context window and cost-efficiency. While Chat GPT4 has a larger context window for processing more extensive data, GPT4 Turbo is more cost-effective and excels in handling prompt complexities.

What does the larger token context window mean?

The larger token context window in Chat GPT4 means that it can take in and process more information at once. This makes it more effective for tasks involving lengthy documents.

Is GPT4 Turbo more cost-effective than Chat GPT4?

Yes, GPT4 Turbo stands out for its cost-effectiveness. It offers cheaper training and usage costs, making it more appealing to budget-conscious developers.

How does OpenAI plan to enhance user experience with GPT4 models?

OpenAI plans to introduce a fine-tuning interface to improve the user experience with GPT4 models. This iterative approach aims to continuously improve and adapt AI functionalities based on user interaction.

Which model is better for handling prompt intricacies?

According to the article, GPT4 Turbo has shown proficiency in handling prompt intricacies without some of the flaws seen in Chat GPT4, giving it an edge in this regard.

Does the larger context window in Chat GPT4 mean it’s superior to GPT4 Turbo?

While Chat GPT4 does have a larger context window, this doesn’t automatically make it superior. Both models have their own strengths depending on the use case, such as GPT4 Turbo’s cost-effectiveness and ability to handle prompt intricacies.

How does GPT4 Turbo offer a valuable proposition despite its smaller context window?

GPT4 Turbo’s ability to handle prompt complexities without the flaws that Chat GPT4 has, along with its cost-effectiveness, make it a very valuable option for developers, regardless of its smaller context window.

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