LlamaIndex’s Post

🔥 Introducing GPT-4o + LlamaParse 🔥 GPT-4o is the state-of-the-art model for multimodal understanding, meaning it also has state-of-the-art document parsing capabilities. LlamaParse is the platform for enabling LLM-powered parsing - it uses LLMs to extract documents from any file type in a performant, reliable fashion, offering state-of-the-art response quality for advanced document RAG. We’re excited to offer GPT-4o as an explicit option in LlamaParse, which will use GPT-4o for extraction per page into markdown, instead of using our default parsers/models. Why:  - GPT-4o is very good at parsing very complex documents into well-formatted markdown. Oftentimes it outperforms our default approaches. - This means that it can turn documents with very complex tables / charts into clean, indexable data for your RAG pipeline - higher response quality, lower hallucinations 📈 Tradeoffs / Caveats ⚠️:  - It’s expensive 💵: Due to the cost of inference, using GPT-4o is currently $0.60 USD per page (while by default LlamaParse is $0.003 per page). This cost can spike quickly - beware!  - You can specify your OpenAI key, in which case the marginal cost per page goes down to 0.3c per page.  - This is a beta feature. Given the cost and latency, use this with caution! If you want to give this a shot, signup for an account and check out our UI: https://lnkd.in/gbkxQAQd Notebook: https://lnkd.in/grwUVr-G

🔥 Introducing GPT-4o + LlamaParse 🔥 GPT-4o is the state-of-the-art model for multimodal understanding, meaning it also has state-of-the-art document parsing capabilities. LlamaParse is the platform for enabling LLM-powered parsing - it uses LLMs to extract documents from any file type in a performant, reliable fashion, offering state-of-the-art response quality for advanced document RAG. We’re excited to offer GPT-4o as an explicit option in LlamaParse, which will use GPT-4o for extraction per page into markdown, instead of using our default parsers/models. Why:  - GPT-4o is very good at parsing very complex documents into well-formatted markdown. Oftentimes it outperforms our default approaches. - This means that it can turn documents with very complex tables / charts into clean, indexable data for your RAG pipeline - higher response quality, lower hallucinations 📈 Tradeoffs / Caveats ⚠️:  - It’s expensive 💵: Due to the cost of inference, using GPT-4o is currently $0.60 USD per page (while by default LlamaParse is $0.003 per page). This cost can spike quickly - beware!  - You can specify your OpenAI key, in which case the marginal cost per page goes down to 0.3c per page.  - This is a beta feature. Given the cost and latency, use this with caution! If you want to give this a shot, signup for an account and check out our UI: https://lnkd.in/gbkxQAQd Notebook: https://lnkd.in/grwUVr-G

GPT-o1 vs. GPT4o: It's important to understand that OpenAI's new o1 model is not necessarily better than GPT4o, but designed for different purposes. Let me explain: GPT-o1 - with its "advanced reasoning capabilities" - is a major breakthrough for tackling tasks that LLMs weren't so good at before: math, temporal understanding, logic - complex things where you need to think through something slowly, in steps. However, GPT-o1 isn't necessarily better across all fronts. Interestingly, it can perform worse in areas where LLMs were typically quite strong. Code completion, for example: As you can see on this benchmark table, o1 ranks behind Claude-3.5 Sonnet, and even behind GPT4o! (Same for creative writing, but that's just my anecdotal evidence). At the same time, GPT-o1 is ALWAYS more expensive than anything else because it generates more output - called reasoning tokens - before arriving at an answer. Clearly, this isn't something you want to have an easy conversation with. Most chatbot use cases won't work. Instead, GPT-o1 seems to work EXTREMELY well in "here's everything I know, process this carefully, and whenever you're ready get back to me with an answer–oh also tell me why" scenarios. As AI becomes more advanced, knowing which model to use for different business applications will become a key skill. GPT-o1 may not be the right tool for every job, but in the right situation, it could be a game changer for use cases that were previously pretty much impossible.

GPT-o1 vs. GPT4o: It's important to understand that OpenAI's new o1 model is not necessarily better than GPT4o, but designed for different purposes. Let me explain: GPT-o1 - with its "advanced reasoning capabilities" - is a major breakthrough for tackling tasks that LLMs weren't so good at before: math, temporal understanding, logic - complex things where you need to think through something slowly, in steps. However, GPT-o1 isn't necessarily better across all fronts. Interestingly, it can perform worse in areas where LLMs were typically quite strong. Code completion, for example: As you can see on this benchmark table, o1 ranks behind Claude-3.5 Sonnet, and even behind GPT4o! (Same for creative writing, but that's just my anecdotal evidence). At the same time, GPT-o1 is ALWAYS more expensive than anything else because it generates more output - called reasoning tokens - before arriving at an answer. Clearly, this isn't something you want to have an easy conversation with. Most chatbot use cases won't work. Instead, GPT-o1 seems to work EXTREMELY well in "here's everything I know, process this carefully, and whenever you're ready get back to me with an answer–oh also tell me why" scenarios. As AI becomes more advanced, knowing which model to use for different business applications will become a key skill. GPT-o1 may not be the right tool for every job, but in the right situation, it could be a game changer for use cases that were previously pretty much impossible.

GPT-o1 vs. GPT4o: It's important to understand that OpenAI's new o1 model is not necessarily better than GPT4o, but designed for different purposes. Let me explain: GPT-o1 - with its "advanced reasoning capabilities" - is a major breakthrough for tackling tasks that LLMs weren't so good at before: math, temporal understanding, logic - complex things where you need to think through something slowly, in steps. However, GPT-o1 isn't necessarily better across all fronts. Interestingly, it can perform worse in areas where LLMs were typically quite strong. Code completion, for example: As you can see on this benchmark table, o1 ranks behind Claude-3.5 Sonnet, and even behind GPT4o! (Same for creative writing, but that's just my anecdotal evidence). At the same time, GPT-o1 is ALWAYS more expensive than anything else because it generates more output - called reasoning tokens - before arriving at an answer. Clearly, this isn't something you want to have an easy conversation with. Most chatbot use cases won't work. Instead, GPT-o1 seems to work EXTREMELY well in "here's everything I know, process this carefully, and whenever you're ready get back to me with an answer–oh also tell me why" scenarios. As AI becomes more advanced, knowing which model to use for different business applications will become a key skill. GPT-o1 may not be the right tool for every job, but in the right situation, it could be a game changer for use cases that were previously pretty much impossible.

GPT-o1 vs. GPT4o: It's important to understand that OpenAI's new o1 model is not necessarily better than GPT4o, but designed for different purposes. Let me explain: GPT-o1 - with its "advanced reasoning capabilities" - is a major breakthrough for tackling tasks that LLMs weren't so good at before: math, temporal understanding, logic - complex things where you need to think through something slowly, in steps. However, GPT-o1 isn't necessarily better across all fronts. Interestingly, it can perform worse in areas where LLMs were typically quite strong. Code completion, for example: As you can see on this benchmark table, o1 ranks behind Claude-3.5 Sonnet, and even behind GPT4o! (Same for creative writing, but that's just my anecdotal evidence). At the same time, GPT-o1 is ALWAYS more expensive than anything else because it generates more output - called reasoning tokens - before arriving at an answer. Clearly, this isn't something you want to have an easy conversation with. Most chatbot use cases won't work. Instead, GPT-o1 seems to work EXTREMELY well in "here's everything I know, process this carefully, and whenever you're ready get back to me with an answer–oh also tell me why" scenarios. As AI becomes more advanced, knowing which model to use for different business applications will become a key skill. GPT-o1 may not be the right tool for every job, but in the right situation, it could be a game changer for use cases that were previously pretty much impossible.

GPT-o1 vs. GPT4o: It's important to understand that OpenAI's new o1 model is not necessarily better than GPT4o, but designed for different purposes. Let me explain: GPT-o1 - with its "advanced reasoning capabilities" - is a major breakthrough for tackling tasks that LLMs weren't so good at before: math, temporal understanding, logic - complex things where you need to think through something slowly, in steps. However, GPT-o1 isn't necessarily better across all fronts. Interestingly, it can perform worse in areas where LLMs were typically quite strong. Code completion, for example: As you can see on this benchmark table, o1 ranks behind Claude-3.5 Sonnet, and even behind GPT4o! (Same for creative writing, but that's just my anecdotal evidence). At the same time, GPT-o1 is ALWAYS more expensive than anything else because it generates more output - called reasoning tokens - before arriving at an answer. Clearly, this isn't something you want to have an easy conversation with. Most chatbot use cases won't work. Instead, GPT-o1 seems to work EXTREMELY well in "here's everything I know, process this carefully, and whenever you're ready get back to me with an answer–oh also tell me why" scenarios. As AI becomes more advanced, knowing which model to use for different business applications will become a key skill. GPT-o1 may not be the right tool for every job, but in the right situation, it could be a game changer for use cases that were previously pretty much impossible.