GPT 5.5 just did what no other model could
Episode
23 min
Read time
2 min
Topics
Productivity, Artificial Intelligence, Software Development
AI-Generated Summary
Key Takeaways
- ✓GPT-5.5 Pricing vs. ROI: GPT-5.5 costs $5 per million input tokens and $30 per million output tokens; the Pro tier runs $30 input and $180 output. Evaluate cost against ambition, not just speed — if the model solves problems no other tool could, the token cost is justified by capability unlocked rather than time saved alone.
- ✓Batch Technical Debt Remediation: Feed GPT-5.5 in Codex a CSV export of security or technical debt issues and instruct it to group thematic problems, propose architectural fixes, and implement them in one pass. This approach cleared a full security backlog and produced a clean annual penetration test result without addressing issues one by one.
- ✓Autonomous 6-Hour Agent Loops: GPT-5.5 in Codex ran a self-directed sub-agent loop for nearly six hours with zero follow-up prompts, testing a production-like dataset of 2 million rows for legacy data format edge cases. The result was one unresolved edge case from millions of rows, dropping the application error rate to near zero in Sentry monitoring.
- ✓Proprietary Protocol Reverse Engineering: When Claude Opus and GPT-5.4 both failed to decode a proprietary Bluetooth device's communication protocol, GPT-5.5 succeeded after being given Bluetooth packet sniffer logs. Use hardware packet capture data as context input — this unlocks reverse-engineering tasks previously considered unsolvable through AI-assisted coding alone.
- ✓Codex Personality Customization: GPT-5.5 in Codex defaults to a flat, minimal communication style. Running the slash-personality command inside Codex allows users to switch to a more conversational tone. For teams doing long autonomous sessions, adjusting this setting improves the feedback experience without affecting the model's underlying reasoning or output quality.
What It Covers
Claire Vaux reviews GPT-5.5 and GPT-5.5 Pro after two weeks of early access testing, focusing on Codex-based autonomous coding tasks. She demonstrates three real-world use cases: security remediation, a 2-million-row data migration, and reverse-engineering a proprietary Bluetooth device — comparing results against Claude and GPT-5.4.
Key Questions Answered
- •GPT-5.5 Pricing vs. ROI: GPT-5.5 costs $5 per million input tokens and $30 per million output tokens; the Pro tier runs $30 input and $180 output. Evaluate cost against ambition, not just speed — if the model solves problems no other tool could, the token cost is justified by capability unlocked rather than time saved alone.
- •Batch Technical Debt Remediation: Feed GPT-5.5 in Codex a CSV export of security or technical debt issues and instruct it to group thematic problems, propose architectural fixes, and implement them in one pass. This approach cleared a full security backlog and produced a clean annual penetration test result without addressing issues one by one.
- •Autonomous 6-Hour Agent Loops: GPT-5.5 in Codex ran a self-directed sub-agent loop for nearly six hours with zero follow-up prompts, testing a production-like dataset of 2 million rows for legacy data format edge cases. The result was one unresolved edge case from millions of rows, dropping the application error rate to near zero in Sentry monitoring.
- •Proprietary Protocol Reverse Engineering: When Claude Opus and GPT-5.4 both failed to decode a proprietary Bluetooth device's communication protocol, GPT-5.5 succeeded after being given Bluetooth packet sniffer logs. Use hardware packet capture data as context input — this unlocks reverse-engineering tasks previously considered unsolvable through AI-assisted coding alone.
- •Codex Personality Customization: GPT-5.5 in Codex defaults to a flat, minimal communication style. Running the slash-personality command inside Codex allows users to switch to a more conversational tone. For teams doing long autonomous sessions, adjusting this setting improves the feedback experience without affecting the model's underlying reasoning or output quality.
Notable Moment
After months of failed attempts using Claude Opus and GPT-5.4, GPT-5.5 decoded a Chinese Bluetooth speaker's proprietary bitmap-based transport protocol using only packet sniffer logs as input — producing a working command-line tool that displays custom messages on the device's screen.
Episode Transcript
Welcome back to How I AI. I'm Claire Vaux, product leader and AI obsessive, here on a mission to help you build better with these new tools. Today, I have a very special episode for you where I'm gonna tell you everything I think about the new GPT 5.5 model, which I've been able to test for the past couple weeks. Spoiler alert, it is a powerhouse, and I've been able to do things with this model, especially around advanced coding, that I haven't been able to do before with any other model on the market. And I'm gonna show you how it breaks my personal high-tech eval hacking into this little computer. Let's get to it. So before I tell you what I built with GPT 5.5, let me tell you a little bit about the model itself. So today, OpenAI is releasing GPT 5.5 and GPT 5.5 Pro into codecs and ChatGPT, not available in the API quite yet. And this model, I've been testing for the past couple weeks, and I will tell you what OpenAI is saying is true. They're saying that it has a higher capacity for complex work and is more efficient, including being more token efficient getting that work done. And so the whole idea with this model is it's smarter and it's more efficient, so you're gonna get more done. And that has really been my experience. Now I'm glad it's more efficient because it is expensive. GPT 5.5 is $5 per million input tokens and $30 for output tokens. And GPT 5.5 pro, which has powered all this work that I've been doing, is 30 for a million input tokens and a $180 for output tokens. So this is a pricey one, but when I reflect on what I was able to achieve with this model in early testing, I'm gonna I'm gonna pay I'm gonna pay the intelligence tax because I think what I was able to achieve is really important. And this is one of the things that I think about a lot when I'm testing these new models or testing these new tools. You know, everything has an ROI, and there can be an ROI in terms of speed. So can I get the things done that I wanna get done faster? And that's certainly been an accelerant from an AI tooling perspective and something we've all experienced for the past couple years. But where GPT 5.5 really helps me is ambition. It has been able to do things that literally I have not been able to do before for a couple reasons. One, just intelligence higher is solved problems that other models and other harnesses other than codex have really had a hard time with. The second thing I've experienced is because the efficiency is higher, I'm able to do more faster without losing context of what I'm working on because it's happening really quickly, or it's being more autonomous so I don't have to babysit as …
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Books, tools, and gear mentioned in this episode
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Tools
- CodexRecommended
by OpenAI
“Feed GPT-5.5 in Codex a CSV export of security or technical debt issues and instruct it to group thematic problems, propose architectural fixes, and implement them in one pass.”
by Anthropic
“After months of failed attempts using Claude Opus and GPT-5.4, GPT-5.5 decoded a Chinese Bluetooth speaker's proprietary bitmap-based transport protocol.”
- GPT 5.5Recommended
by OpenAI
“Claire Vaux reviews GPT-5.5 and GPT-5.5 Pro after two weeks of early access testing, focusing on Codex-based autonomous coding tasks.”
- GPT-5.5 ProRecommended
by OpenAI
“Claire Vaux reviews GPT-5.5 and GPT-5.5 Pro after two weeks of early access testing, focusing on Codex-based autonomous coding tasks.”
by Sentry
“The result was one unresolved edge case from millions of rows, dropping the application error rate to near zero in Sentry monitoring.”
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