Large language models (LLMs) can revolutionize your workflows and creative pursuits or provide endless entertainment. In either case, some LLMs are better than others at certain tasks.
Let’s take a quick look at the top large language models in 2024. These LLMs are popular and capable, with large online communities sharing their knowledge so you can always find the solution you need.
Top 9 AI Large Language Models in 2024
1. ChatGPT: GPT-4o (omni)
ChatGPT’s new flagship model, GPT-4o, is now a clear leader in the LLM field. With some of the best performance and multimodal capabilities, it also solidified ChatGPT’s continuing position as the most popular AI platform.
At the end of 2024, many enterprises only have a handful of options, and GPT-4 and GPT-4o are among them. GPT-4o offers greater capacity with a higher context length than previous models. It is the most capable option that ChatGPT has presented for handling complex tasks.
While enterprises can apply the GPT-4o API for seemingly limitless applications, anyone can benefit. With a simple $20 monthly subscription, individuals, professionals, and small businesses can get limited but extensive use of this newest model. A Team subscription of $25 per user unlocks unlimited use of GPT-4o and other premium models. Enterprise users can also customize their GPT subscription for additional training for tasks like:
Producing text
Editing large bodies of text
Image generation and editing
Coding functions of all kinds
Translation
Data analysis
More
Enhanced Workflows
ChatGPT has been focusing its newest models on business workflows. To that end, GPT-4o offers a much higher capacity, but with faster speeds and lower costs. Efficiency is the biggest change for enterprise customers looking for the right AI models to integrate.
Analysis
GPT-4o is an enhanced natural language processing model that can better understand complex inputs. More so than other popular ChatGPT models, GPT-4o can analyze and understand large, complex, professional documentation. It can complete complex queries, perform demanding summarization, search, or perform similar tasks in shorter time frames.
Crucially, GPT-4o also performs these tasks better in multilingual environments. It can switch between supported languages, performing tasks like analyzing complex documentation in one language and summarizing it in another.
Also, importantly, the multimodal capabilities of GPT-4o have vastly improved. Charts, graphs, and other infographics can be understood and remade, edited, or transferred into text summaries, often with one concise prompt.
Accuracy
In many professional and enterprise cases, GPT-4o’s level of knowledge is among the most important factors. For LLMs, factual accuracy is among their most controversial aspects. So far, several large-scale tests have been conducted on different subjects, from elementary school to post-grad level. In most cases, GPT-4o is among the best.
The MMLU benchmark ranks GPT-4o in 2nd place. That implies that it has a solid understanding of 57 academic subjects up to the professional level.
GPT-4 models, including GPT-4o, also perform well on several benchmarks like MATH and GSM8K. For small or large-scale coding tasks, GPT-4o is more effective. It can solve text-based mathematical and coding problems better than many popular alternatives. While it can’t beat many dedicated coding LLMs, GPT-4o can be customized to better understand your unique needs.
2. Claude: Claude 3.5 Sonnet
Claude 3.5 Sonnet is Claude’s new flagship model, developed by Anthropic, taking the reins from Claude 3 Opus. In addition to the high accuracy that Claude has come to produce in its models, it offers balanced processing and one of the safest approaches to generative AI.
Like Claude 3 Opus, using Claude 3.5 Sonnet extensively requires a $18 monthly Claude Pro subscription. For that, you receive several important benefits offered by Claude 3.5 Sonnet, but you can test and experience these benefits for free.
Accuracy
Remember the broadest benchmarks we just covered? Well, Claude 3.5 Sonnet currently tops the list for the MMLU benchmark. It also excels in reasoning (GPQA) and coding (HumanEval). The excellent training and improved safety measures ensure that what other LLMs get wrong, Claude 3.5 Sonnet gets right.
Of course, there is still a large chance that all Claude models get their facts wrong. The chatbot’s disclaimer will constantly remind you of this while using it. But it’s always better to avoid outputs that can get you in trouble if you’re not careful.
Coding Excellence
Claude 3.5 Sonnet stands among the leaders in the HumanEval benchmark. It can handle complex coding tasks, conducting most of the workflow with only a little guidance. Claude 3.5 Sonnet is great at iterating and providing fresh code for any task you explain to it. It also provides excellent images to demonstrate the code’s intended use cases.
Balanced Performance
LLMs have to balance speed and efficiency with accuracy and performance capacity. Claude 3.5 Sonnet beats other Claude models and other chatbot models. But it’s also a fast and cost-effective option, relative to what it offers in terms of knowledge and reasoning.
According to Anthropic, Claude 3.5 Sonnet operates twice the speed of the company’s former flagship model. This claim is difficult to confirm, but Claude 3.5 Sonnet is a fast model and is fully capable of being given complex tasks like customer service chatbot or analyzing vast quantities of data.
For professionals and enterprises, Claude 3.5 Sonnet also stands out for its steerability. It can be trained on unique datasets and be taught specific instructions to follow in its workflows. This includes good reasoning and autonomous troubleshooting.
Safety Balanced with Fun
Claude LLMs stand apart thanks to Anthropic’s Constitutional AI. Constitutional AI functions as a self-improving set of safeguards. The idea is to offer “harmless” AI use that doesn’t produce offensive or negligent content. Anthropic does this with a process of self-critique and user feedback, improving its safeguards over time.
A notorious problem with older Claude models was that these extra safeguards would go too far. Claude LLMs would refuse completely innocent and harmless requests. Constitutional AI would accidentally set up barriers where they were wholly unnecessary.
These issues have been corrected with Claude 3.5 Sonnet. Now, it can better understand human language and provide more “human” responses. It has a sense of humor, and its ability to censor dangerous requests has been fine-tuned. Claude 3.5 Sonnet is better at safeguarding users, while also displaying a sense of humor and more personal responses.
3. ChatGPT: GPT-4
Despite the much greater accuracy and potential of GPT-4o, some individuals and businesses still prefer GPT-4. GPT-4o beats its predecessor on every benchmark, but benchmarks don’t tell the full story.
Some users notice that GPT-4o will lose focus during long, complex tasks. Some criticize GPT-4o for being “stubborn.”
In practice, this speaks to how it may take more guidance to get GPT-4o to complete complex tasks. One common complaint that you can test for yourself is more common than other complaints:
You list out 30 tasks in one input.
GPT-4o focuses on less than 30 tasks, giving more attention to some, while possibly ignoring one or two.
The result is that parts of your request are completed with excellence, while some small parts are largely ignored.
Simplicity
This isn’t a deal breaker on its own; with some patience and guidance, GPT-4o still produces the best outputs. However, for some text-based instructions, GPT-4 is easier to work with. For coding and some other types of tasks, GPT-4o is clearly superior.
In some situations, it can be worth it to compare how GPT-4 and GPT-4o perform on a “typical” task that you require them to complete. If you notice a trade-off between GPT-4o’s features and GPT-4’s simplicity and reliability, you can compare the pros and cons for yourself.
Competitive Performance
It’s also important to point out that while GPT-4o demonstrates greater knowledge and reasoning, GPT-4 still stands as a highly competitive LLM. Its multimodal capabilities may not be as good, but its reasoning, language processing, and other abilities still surpass most alternatives. The benchmarks don’t reflect the entire story, but they still reflect positively for GPT-4.
4. Google Gemini: Gemini Ultra
Google Gemini offers 2 models that have been changing the LLM space. Between Gemini 1.5 Pro and Gemini 1.0 Ultra, the latter stands out in a few ways.
Gemini Ultra is one of the 4 model umbrellas released by Gemini. Compared to the others, Gemini Ultra was designed for higher capacities and more complex tasks. It’s not the fastest or necessarily the most cost-effective option. However, Gemini Ultra brings the highest performance regarding accuracy and the complex reasoning it’s capable of.
Exceptional Reasoning
Gemini Ultra stands out for its mathematical and scientific reasoning. On the model’s page, Google DeepMind boasts of Ultra’s ability to beat human experts in multi-disciplinary examinations. Compared to the overall performance of human experts, Gemini beat the average by 0.2%, an exceptional feat for any LLM.
Gemini Ultra has also undergone extensive, transparent testing in mathematical reasoning. It’s proven capable of performing in competitive mathematics, demonstrating strong linguistic abilities to analyze problems.
Multimodal reasoning
Gemini’s reasoning abilities extend to Gemini Ultra’s multimodal capabilities. Gemini works with, understands, and can translate between text, images, and audio. These capabilities are being built into Google’s applications, offering more accessible and useful search options.
At the same time, Gemini’s image generator produces high-quality images, making Gemini Ultra a well-rounded tool for creating, altering, and analyzing any image.
5. Claude: Claude 3 Opus
Among the models that have become “outdated,” but people still swear by, there is Claude 3 Opus. It looks great on paper, performs well in benchmarks, has all the features you need, and so on. However, the small differences make it a favorite among professionals.
Work Assistance
The Claude 3 Opus interface comes equipped with a timer, buttons to control it, and other workflow-enhancing features. These simple additions may not sound amazing, but they are a large part of why Claude models have consistently remained popular.
Claude 3 Opus even comes with a customizable Pomodoro Timer. Few other features could advertise a tool’s purpose so clearly.
Text-based Assistance
While Claude 3.5 Sonnet is a superior customer support chatbot, Claude 3 Opus is better suited for end work. This can include use cases like summarizing healthcare documentation, content creation for marketing, and business strategizing.
Professionals in the legal, software, and healthcare fields are already accustomed to applying Claude 3 Opus in workflows like these. Speaking of software, Claude 3 Opus is not as good at coding as its newer sibling, but it still brings concise, clear code generation to the table.
Overall, Claude 3 Opus is more often chosen for deep analysis and contextually heavy workflows.
6. Google Gemini: Gemini Pro
In some important ways, Gemini Pro is more proficient than Gemini Ultra. Gemini Ultra offers the most capable and complex option from Google DeepMind. However, Gemini Pro was designed for adaptability, scalability, and efficiency.
Some users prefer Gemini Pro for its variability. Being able to adapt AI to your own needs is a prized quality for an LLM. This is where Google Pro shines.
Better Efficiency
Gemini Pro is a “lightweight” LLM that can quickly adapt to new tasks. It’s not the most capable model in terms of knowledge, but it is fast streamlined for operations.
This efficiency is focused on building workflows in situations that require flexibility. However, you can also see Gemini Pro’s efficiency in approaching normal conversations. It can, in some cases, take lines of thought in less conventional directions.
Performance
Gemini Pro stands out at handling comprehensive tasks and seemingly off-the-cuff user interactions.
In terms of its ability to provide knowledge-based responses that stand up to scrutiny, it’s a mixed bag. In general, Gemini Ultra beats it here, but not by a staggering amount. For example, Gemini Pro doesn’t sacrifice accuracy, performing decently on the MMLU benchmark.
7. Perplexity.ai: Using GPT-4o
Perplexity is an AI LLM-based search engine. Like ChatGPT and other platforms, Perplexity offers a chat-based interface where you can type inputs, and it will provide a relevant output. The main differences between Perplexity.ai and other platforms are:
Live access to the internet
Transparent citations for substantial claims
When you ask Perplexity a question, it searches the web for results and provides numerical citations. That way, you can explore its responses further, going straight to the source of the information.
Perplexity works with different LLMs, some of them for free and others paid. With a Pro subscription, you can use the Perplexity search engine chatbot with GPT-4o. With that, you get a highly capable version of GPT-4o, but with a smaller context window.
Enhanced Research
With the power of the World Wide Web and GPT-4o, Perplexity helps you get to the bottom of any subject you’re studying. While it isn’t always right and may produce faulty responses, it does better overall with GPT-4o’s performance capabilities.
If you’re unsure of any of Perplexity’s claims, you can quickly confirm them by clicking on the citation. Perplexity directs you right to the source of the information they’re providing. In some cases, it’s unimpressive; it may pull from a Wikipedia article or an Encyclopedia Britannica page. But it sometimes pulls from academic research or more niche authorities on different subjects.
These enhanced research features make Perplexity an outstanding research and writing assistant.
8. ChatGPT: GPT-4 Turbo
GPT-4 Turbo is another new model from ChatGPT. As the name suggests, it’s optimized for speed. But it also offers more user control and steerability. At the same time, especially earlier in its launch, some users complained that it was a “lazy” model. By that, they mean it provided shorter, simpler answers and required more effort in prompts to get more value.
In any case, GPT-4 Turbo is well-known for its ability to learn. It scores among the highest on the HumanEval benchmark, being a reliable coding assistant.
Steerability
GPT-4 Turbo comes with new features and optimizations that give developers more control. While some complain that it provides answers that are too concise, that is what it was designed for. GPT-4 Turbo follows precise instructions incredibly well.
Compared to other GPT models, Turbo is most notable for improved function calling. You can call several functions in a single message. After the recent updates, a concise set of function calls will normally result in a streamlined and accurate response.
Context Window
A large part of GPT-4 Turbo’s enhanced steerability simply comes from a larger context window. Due to the 128,000-token window, it can build a more thorough understanding of a task than most other models.
9. Claude: Claude 3 Haiku
Coming in at the bottom of our list, Claude 3 Haiku is an LLM that doesn’t perform well on paper but serves its niche well. At launch, it was one of the few LLMs that could run nearly instantly.
Customer Service
While not designed only for customer support roles, Haiku stands out for a few reasons. It offers very high speeds and is more cost-effective. These qualities make it ideal for the fast needs of customers seeking support.
At the same time, Haiku is a great choice for large internal tasks. For example, it can quickly scan financial or legal documentation at a low cost. According to Anthropic, you can use it to scan 400 Supreme Court cases for $1.
What is a Large Language Model (LLM)?
Large language models (LLMs) are machine-learning tools that understand and generate text in human languages. Pre-trained on large quantities of data, they use a predictive model to complete queries that users provide.
The machine learning behind an LLM is a neural network known as a transformer model. Transformer models work with sequences within language and learn context.
While LLM-generated text sometimes seems very human, it’s important to remember that it is not. If you’re curious whether content was produced by an AI large language model, AI Detector can help.Large language models (LLMs) can revolutionize your workflows and creative pursuits or provide endless entertainment. In either case, some LLMs are better than others at certain tasks.
Let’s take a quick look at the top large language models in 2024. These LLMs are popular and capable, with large online communities sharing their knowledge so you can always find the solution you need.
Top 9 AI Large Language Models in 2024
1. ChatGPT: GPT-4o (omni)
ChatGPT’s new flagship model, GPT-4o, is now a clear leader in the LLM field. With some of the best performance and multimodal capabilities, it also solidified ChatGPT’s continuing position as the most popular AI platform.
At the end of 2024, many enterprises only have a handful of options, and GPT-4 and GPT-4o are among them. GPT-4o offers greater capacity with a higher context length than previous models. It is the most capable option that ChatGPT has presented for handling complex tasks.
While enterprises can apply the GPT-4o API for seemingly limitless applications, anyone can benefit. With a simple $20 monthly subscription, individuals, professionals, and small businesses can get limited but extensive use of this newest model. A Team subscription of $25 per user unlocks unlimited use of GPT-4o and other premium models. Enterprise users can also customize their GPT subscription for additional training for tasks like:
Producing text
Editing large bodies of text
Image generation and editing
Coding functions of all kinds
Translation
Data analysis
More
Enhanced Workflows
ChatGPT has been focusing its newest models on business workflows. To that end, GPT-4o offers a much higher capacity, but with faster speeds and lower costs. Efficiency is the biggest change for enterprise customers looking for the right AI models to integrate.
Analysis
GPT-4o is an enhanced natural language processing model that can better understand complex inputs. More so than other popular ChatGPT models, GPT-4o can analyze and understand large, complex, professional documentation. It can complete complex queries, perform demanding summarization, search, or perform similar tasks in shorter time frames.
Crucially, GPT-4o also performs these tasks better in multilingual environments. It can switch between supported languages, performing tasks like analyzing complex documentation in one language and summarizing it in another.
Also, importantly, the multimodal capabilities of GPT-4o have vastly improved. Charts, graphs, and other infographics can be understood and remade, edited, or transferred into text summaries, often with one concise prompt.
Accuracy
In many professional and enterprise cases, GPT-4o’s level of knowledge is among the most important factors. For LLMs, factual accuracy is among their most controversial aspects. So far, several large-scale tests have been conducted on different subjects, from elementary school to post-grad level. In most cases, GPT-4o is among the best.
The MMLU benchmark ranks GPT-4o in 2nd place. That implies that it has a solid understanding of 57 academic subjects up to the professional level.
GPT-4 models, including GPT-4o, also perform well on several benchmarks like MATH and GSM8K. For small or large-scale coding tasks, GPT-4o is more effective. It can solve text-based mathematical and coding problems better than many popular alternatives. While it can’t beat many dedicated coding LLMs, GPT-4o can be customized to better understand your unique needs.
2. Claude: Claude 3.5 Sonnet
Claude 3.5 Sonnet is Claude’s new flagship model, developed by Anthropic, taking the reins from Claude 3 Opus. In addition to the high accuracy that Claude has come to produce in its models, it offers balanced processing and one of the safest approaches to generative AI.
Like Claude 3 Opus, using Claude 3.5 Sonnet extensively requires a $18 monthly Claude Pro subscription. For that, you receive several important benefits offered by Claude 3.5 Sonnet, but you can test and experience these benefits for free.
Accuracy
Remember the broadest benchmarks we just covered? Well, Claude 3.5 Sonnet currently tops the list for the MMLU benchmark. It also excels in reasoning (GPQA) and coding (HumanEval). The excellent training and improved safety measures ensure that what other LLMs get wrong, Claude 3.5 Sonnet gets right.
Of course, there is still a large chance that all Claude models get their facts wrong. The chatbot’s disclaimer will constantly remind you of this while using it. But it’s always better to avoid outputs that can get you in trouble if you’re not careful.
Coding Excellence
Claude 3.5 Sonnet stands among the leaders in the HumanEval benchmark. It can handle complex coding tasks, conducting most of the workflow with only a little guidance. Claude 3.5 Sonnet is great at iterating and providing fresh code for any task you explain to it. It also provides excellent images to demonstrate the code’s intended use cases.
Balanced Performance
LLMs have to balance speed and efficiency with accuracy and performance capacity. Claude 3.5 Sonnet beats other Claude models and other chatbot models. But it’s also a fast and cost-effective option, relative to what it offers in terms of knowledge and reasoning.
According to Anthropic, Claude 3.5 Sonnet operates twice the speed of the company’s former flagship model. This claim is difficult to confirm, but Claude 3.5 Sonnet is a fast model and is fully capable of being given complex tasks like customer service chatbot or analyzing vast quantities of data.
For professionals and enterprises, Claude 3.5 Sonnet also stands out for its steerability. It can be trained on unique datasets and be taught specific instructions to follow in its workflows. This includes good reasoning and autonomous troubleshooting.
Safety Balanced with Fun
Claude LLMs stand apart thanks to Anthropic’s Constitutional AI. Constitutional AI functions as a self-improving set of safeguards. The idea is to offer “harmless” AI use that doesn’t produce offensive or negligent content. Anthropic does this with a process of self-critique and user feedback, improving its safeguards over time.
A notorious problem with older Claude models was that these extra safeguards would go too far. Claude LLMs would refuse completely innocent and harmless requests. Constitutional AI would accidentally set up barriers where they were wholly unnecessary.
These issues have been corrected with Claude 3.5 Sonnet. Now, it can better understand human language and provide more “human” responses. It has a sense of humor, and its ability to censor dangerous requests has been fine-tuned. Claude 3.5 Sonnet is better at safeguarding users, while also displaying a sense of humor and more personal responses.
3. ChatGPT: GPT-4
Despite the much greater accuracy and potential of GPT-4o, some individuals and businesses still prefer GPT-4. GPT-4o beats its predecessor on every benchmark, but benchmarks don’t tell the full story.
Some users notice that GPT-4o will lose focus during long, complex tasks. Some criticize GPT-4o for being “stubborn.”
In practice, this speaks to how it may take more guidance to get GPT-4o to complete complex tasks. One common complaint that you can test for yourself is more common than other complaints:
You list out 30 tasks in one input.
GPT-4o focuses on less than 30 tasks, giving more attention to some, while possibly ignoring one or two.
The result is that parts of your request are completed with excellence, while some small parts are largely ignored.
Simplicity
This isn’t a deal breaker on its own; with some patience and guidance, GPT-4o still produces the best outputs. However, for some text-based instructions, GPT-4 is easier to work with. For coding and some other types of tasks, GPT-4o is clearly superior.
In some situations, it can be worth it to compare how GPT-4 and GPT-4o perform on a “typical” task that you require them to complete. If you notice a trade-off between GPT-4o’s features and GPT-4’s simplicity and reliability, you can compare the pros and cons for yourself.
Competitive Performance
It’s also important to point out that while GPT-4o demonstrates greater knowledge and reasoning, GPT-4 still stands as a highly competitive LLM. Its multimodal capabilities may not be as good, but its reasoning, language processing, and other abilities still surpass most alternatives. The benchmarks don’t reflect the entire story, but they still reflect positively for GPT-4.
4. Google Gemini: Gemini Ultra
Google Gemini offers 2 models that have been changing the LLM space. Between Gemini 1.5 Pro and Gemini 1.0 Ultra, the latter stands out in a few ways.
Gemini Ultra is one of the 4 model umbrellas released by Gemini. Compared to the others, Gemini Ultra was designed for higher capacities and more complex tasks. It’s not the fastest or necessarily the most cost-effective option. However, Gemini Ultra brings the highest performance regarding accuracy and the complex reasoning it’s capable of.
Exceptional Reasoning
Gemini Ultra stands out for its mathematical and scientific reasoning. On the model’s page, Google DeepMind boasts of Ultra’s ability to beat human experts in multi-disciplinary examinations. Compared to the overall performance of human experts, Gemini beat the average by 0.2%, an exceptional feat for any LLM.
Gemini Ultra has also undergone extensive, transparent testing in mathematical reasoning. It’s proven capable of performing in competitive mathematics, demonstrating strong linguistic abilities to analyze problems.
Multimodal reasoning
Gemini’s reasoning abilities extend to Gemini Ultra’s multimodal capabilities. Gemini works with, understands, and can translate between text, images, and audio. These capabilities are being built into Google’s applications, offering more accessible and useful search options.
At the same time, Gemini’s image generator produces high-quality images, making Gemini Ultra a well-rounded tool for creating, altering, and analyzing any image.
5. Claude: Claude 3 Opus
Among the models that have become “outdated,” but people still swear by, there is Claude 3 Opus. It looks great on paper, performs well in benchmarks, has all the features you need, and so on. However, the small differences make it a favorite among professionals.
Work Assistance
The Claude 3 Opus interface comes equipped with a timer, buttons to control it, and other workflow-enhancing features. These simple additions may not sound amazing, but they are a large part of why Claude models have consistently remained popular.
Claude 3 Opus even comes with a customizable Pomodoro Timer. Few other features could advertise a tool’s purpose so clearly.
Text-based Assistance
While Claude 3.5 Sonnet is a superior customer support chatbot, Claude 3 Opus is better suited for end work. This can include use cases like summarizing healthcare documentation, content creation for marketing, and business strategizing.
Professionals in the legal, software, and healthcare fields are already accustomed to applying Claude 3 Opus in workflows like these. Speaking of software, Claude 3 Opus is not as good at coding as its newer sibling, but it still brings concise, clear code generation to the table.
Overall, Claude 3 Opus is more often chosen for deep analysis and contextually heavy workflows.
6. Google Gemini: Gemini Pro
In some important ways, Gemini Pro is more proficient than Gemini Ultra. Gemini Ultra offers the most capable and complex option from Google DeepMind. However, Gemini Pro was designed for adaptability, scalability, and efficiency.
Some users prefer Gemini Pro for its variability. Being able to adapt AI to your own needs is a prized quality for an LLM. This is where Google Pro shines.
Better Efficiency
Gemini Pro is a “lightweight” LLM that can quickly adapt to new tasks. It’s not the most capable model in terms of knowledge, but it is fast streamlined for operations.
This efficiency is focused on building workflows in situations that require flexibility. However, you can also see Gemini Pro’s efficiency in approaching normal conversations. It can, in some cases, take lines of thought in less conventional directions.
Performance
Gemini Pro stands out at handling comprehensive tasks and seemingly off-the-cuff user interactions.
In terms of its ability to provide knowledge-based responses that stand up to scrutiny, it’s a mixed bag. In general, Gemini Ultra beats it here, but not by a staggering amount. For example, Gemini Pro doesn’t sacrifice accuracy, performing decently on the MMLU benchmark.
7. Perplexity.ai: Using GPT-4o
Perplexity is an AI LLM-based search engine. Like ChatGPT and other platforms, Perplexity offers a chat-based interface where you can type inputs, and it will provide a relevant output. The main differences between Perplexity.ai and other platforms are:
Live access to the internet
Transparent citations for substantial claims
When you ask Perplexity a question, it searches the web for results and provides numerical citations. That way, you can explore its responses further, going straight to the source of the information.
Perplexity works with different LLMs, some of them for free and others paid. With a Pro subscription, you can use the Perplexity search engine chatbot with GPT-4o. With that, you get a highly capable version of GPT-4o, but with a smaller context window.
Enhanced Research
With the power of the World Wide Web and GPT-4o, Perplexity helps you get to the bottom of any subject you’re studying. While it isn’t always right and may produce faulty responses, it does better overall with GPT-4o’s performance capabilities.
If you’re unsure of any of Perplexity’s claims, you can quickly confirm them by clicking on the citation. Perplexity directs you right to the source of the information they’re providing. In some cases, it’s unimpressive; it may pull from a Wikipedia article or an Encyclopedia Britannica page. But it sometimes pulls from academic research or more niche authorities on different subjects.
These enhanced research features make Perplexity an outstanding research and writing assistant.
8. ChatGPT: GPT-4 Turbo
GPT-4 Turbo is another new model from ChatGPT. As the name suggests, it’s optimized for speed. But it also offers more user control and steerability. At the same time, especially earlier in its launch, some users complained that it was a “lazy” model. By that, they mean it provided shorter, simpler answers and required more effort in prompts to get more value.
In any case, GPT-4 Turbo is well-known for its ability to learn. It scores among the highest on the HumanEval benchmark, being a reliable coding assistant.
Steerability
GPT-4 Turbo comes with new features and optimizations that give developers more control. While some complain that it provides answers that are too concise, that is what it was designed for. GPT-4 Turbo follows precise instructions incredibly well.
Compared to other GPT models, Turbo is most notable for improved function calling. You can call several functions in a single message. After the recent updates, a concise set of function calls will normally result in a streamlined and accurate response.
Context Window
A large part of GPT-4 Turbo’s enhanced steerability simply comes from a larger context window. Due to the 128,000-token window, it can build a more thorough understanding of a task than most other models.
9. Claude: Claude 3 Haiku
Coming in at the bottom of our list, Claude 3 Haiku is an LLM that doesn’t perform well on paper but serves its niche well. At launch, it was one of the few LLMs that could run nearly instantly.
Customer Service
While not designed only for customer support roles, Haiku stands out for a few reasons. It offers very high speeds and is more cost-effective. These qualities make it ideal for the fast needs of customers seeking support.
At the same time, Haiku is a great choice for large internal tasks. For example, it can quickly scan financial or legal documentation at a low cost. According to Anthropic, you can use it to scan 400 Supreme Court cases for $1.
What is a Large Language Model (LLM)?
Large language models (LLMs) are machine-learning tools that understand and generate text in human languages. Pre-trained on large quantities of data, they use a predictive model to complete queries that users provide.
The machine learning behind an LLM is a neural network known as a transformer model. Transformer models work with sequences within language and learn context.
While LLM-generated text sometimes seems very human, it’s important to remember that it is not. If you’re curious whether content was produced by an AI large language model, AI Detector can help.
