Upgrading to GPT-4o: What You Need to Know

OpenAI released gpt-4o two days ago, their new flagship model. The big question now is: Should you upgrade?

OpenAI’s Claims

Let’s start with what OpenAI claims:

• Omnimodel (audio, vision, text)
• GPT-4-turbo quality on text and code
• Better at non-English languages
• 2x faster and 50% cheaper than GPT-4-turbo

(Audio and real-time features not yet available)

Should you upgrade to GPT-4o? Will you need to change your prompts? I asked a few PromptLayer customers and did some research myself.

🚦 Mixed feedback

GPT-4o has only been out for two days, so take results with a grain of salt. Some customers switched without an issue, while others had to roll back.

⚡️ Faster and less yapping

GPT-4o isn’t as verbose, and the speed improvement can be a game-changer.

🧩 Struggling with hard problems

GPT-4o doesn’t seem to perform quite as well as GPT-4 or Claude-opus on hard coding problems. I updated my model in Cursor to GPT-4o. It’s been great to have much quicker replies, and I’ve been able to do more, but I’ve found GPT-4o getting stuck on some things Claude-Opus solves in one shot.

😵‍💫 Worse instruction following

Some customers ended up rolling back to GPT-4-turbo after upgrading. Make sure to monitor logs closely to see if anything breaks.

Customers have seen use-case-specific regressions with regard to things like:

• JSON serialization
• Language-related edge cases
• Outputting in specialized formats

In other words, if you spent time prompt engineering on GPT-4-turbo, the wins might not carry over. Your prompts are likely overfit to GPT-4-turbo and can be shortened for GPT-4o.

Conclusion

GPT-4o shows promise with its speed and cost improvements, but it’s essential to approach the upgrade cautiously. Monitor your use cases closely and be prepared to roll back if necessary. As more people experiment with GPT-4o, we’ll better understand its strengths and weaknesses.

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