OpenAI shook up the world of large language models once again with the release of GPT-4 in March 2023. Building on the game-changing GPT-3, this new flagship model promises significantly improved performance across a wide variety of tasks. But just how much better is GPT-4 compared to its esteemed predecessor GPT-3.5 (or text-davinci-003 as it was previously called)? And more importantly, is the increased capability worth the higher pricing that OpenAI is charging? Let's dive into the benchmark data to find out.
The highly respected LMSYS Chatbot Arena has been rigorously evaluating GPT-4 and other leading language models through over 1 million human pairwise comparisons. Their leaderboard, which ranks models on an Elo scale using the Bradley-Terry model, provides some fascinating insights into the GPT-4 vs GPT-3.5 debate.
At a high level, GPT-4 comes out as the overall quality leader, scoring an impressive 1650 on the Elo scale. This is a decent jump from GPT-3.5's still formidable 1585 rating. So in the Arena's comprehensive evaluations across a multitude of language tasks, GPT-4 does demonstrate a tangible edge over its predecessor.
But the aggregate number doesn't tell the full story. By digging into the specific strengths and weaknesses across different task categories, we can better understand the nuances of when the GPT-4 premium may be worthwhile versus when GPT-3.5 could still be a more cost-effective choice.
In the Arena's "Reading Comprehension" category, which tests abilities like answering questions, interpreting context and following instructions accurately, GPT-4 truly shines with a rating of 1750 compared to GPT-3.5's 1580. This is one area where the model's advanced language understanding and reasoning capabilities pay clear dividends for applications that demand high precision.
The gap is even larger for "Open-Ended" tasks that rely more on generation, ideation, and creative expression. GPT-4 scores a stellar 1820 here, dwarfing GPT-3.5's 1490 rating. If you need an LLM for fiction writing, freeform brainstorming, poetry composition or other imaginative use cases, GPT-4 looks to be vastly superior based on these results.
However, when it comes to mathematical and coding tasks that require strong STEM skills, the GPT-4 advantage appears less pronounced. In the "Math Word Problems" and "Basic Coding" categories, GPT-4 still leads but by narrower margins - 1670 vs 1565 and 1735 vs 1660 respectively. This indicates that while GPT-4 offers improvements in these technical domains, GPT-3.5 may still suffice for many standard engineering and analytics workloads depending on your requirements.
Another fascinating data point is the performance on multi-lingual and multi-modal tasks. GPT-4 demonstrates robust cross-lingual transfer learning capabilities, able to handle prompts seamlessly across dozens of languages at high quality thanks to its 100+ language training data. And its multimodal skills to perceive and analyze images, documents and other data modes are significantly upgraded compared to GPT-3.5.
From a safety perspective, OpenAI has also made strides in imbuing GPT-4 with stronger principles around truthfulness, ethical reasoning, and avoiding potentially harmful outputs. While no model is perfect, GPT-4 shows markedly improved performance on the Arena's "Truthfulness" category over GPT-3.5.
Of course, with increased performance comes increased cost for OpenAI's cloud services. While pricing can vary based on factors like batch sizing and commitment levels, general estimates peg GPT-4 API calls around 3-4x more expensive than the previous GPT-3.5 model for many usage scenarios.
So whether the GPT-4 premium is worth it really depends on the specific requirements of your application or use case. For mission-critical production workloads where accuracy and quality are paramount, as well as open-ended creative tasks, the higher price tag may be justifiable to leverage GPT-4's improved capabilities. Businesses working in highly regulated industries like finance and healthcare could also find value in GPT-4's enhanced truthfulness and ethical standards.
However, if you're an individual developer experimenting with LLMs, or a company with more standardized language processing needs, the cost-benefit tradeoff may favor sticking with GPT-3.5 for now. The gap likely isn't wide enough for many basic use cases to merit the higher expense.
It's also worth noting that other LLM providers like Anthropic and Google are rapidly catching up with their own flagship models that could potentially outperform GPT-4 in certain areas or provide stronger cost/performance value in the near future.
Ultimately, like many emerging technologies, deciding between GPT-4 and GPT-3.5 will require carefully evaluating your specific goals, risk tolerance, and ROI calculations. GPT-4 unquestionably represents a new frontier for large language models in terms of general performance and capabilities. But whether it's the right solution for your needs comes down to weighing its strengths against the increased costs compared to still highly capable prior models like GPT-3.5.
For a comparison of rankings and prices across different LLM APIs, you can refer to LLMCompare.