(I work at OpenAI.)

And that backdoor API has GPT-5.5.

So here's a pelican: https://simonwillison.net/2026/Apr/23/gpt-5-5/#and-some-peli...

I used this new plugin for LLM: https://github.com/simonw/llm-openai-via-codex

UPDATE: I got a much better pelican by setting the reasoning effort to xhigh: https://gist.github.com/simonw/a6168e4165a258e4d664aeae8e602...

I recommend anybody in offensive/defensive cybersecurity to experiment with this. This is the real data point we needed - without the hype!

Never thought I'd say this but OpenAI is the 'open' option again.

https://developers.openai.com/codex/pricing?codex-usage-limi...

Note the Local Messages between 5.3, 5.4, and 5.5. And, yes, I did read the linked article and know they're claiming that 5.5's new efficient should make it break-even with 5.4, but the point stands, tighter limits/higher prices.

> To better utilize GPUs, Codex analyzed weeks’ worth of production traffic patterns and wrote custom heuristic algorithms to optimally partition and balance work. The effort had an outsized impact, increasing token generation speeds by over 20%.

The ability for agentic LLMs to improve computational efficiency/speed is a highly impactful domain I wish was more tested than with benchmarks. From my experience Opus is still much better than GPT/Codex in this aspect, but given that OpenAI is getting material gains out of this type of performancemaxxing and they have an increasing incentive to continue doing so given cost/capacity issues, I wonder if OpenAI will continue optimizing for it.

The game that this prompt generated looks pretty decent visually. A big part of this likely due to the fact the meshes were created using a seperate tool (probably meshy, tripo.ai, or similiar) and not generated by 5.5 itself.

It really seems like we could be at the dawn of a new era similiar to flash, where any gamer or hobbyist can generate game concepts quickly and instantly publish them to the web. Three.js in particular is really picking up as the primary way to design games with AI, in spite of the fact it's not even a game engine, just a web rendering library.

                          Mythos     5.5
    SWE-bench Pro          77.8%*   58.6%
    Terminal-bench-2.0     82.0%    82.7%*
    GPQA Diamond           94.6%*   93.6%
    H. Last Exam           56.8%*   41.4%
    H. Last Exam (tools)   64.7%*   52.2%    
    BrowseComp             86.9%    84.4%  (90.1% Pro)*
    OSWorld-Verified       79.6%*   78.7%

Source: https://artificialanalysis.ai/models?omniscience=omniscience...

*I work at OAI.

This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive.

This matches my own experience and unease with these tools. I don't really have the patience to write code anymore because I can one shot it with frontier models 10x faster. My role has shifted, and while it's awesome to get so much working so quickly, the fact is, when the tokens run out, I'm basically done working.

It's literally higher leverage for me to go for a walk if Claude goes down than to write code because if I come back refreshed and Claude is working an hour later then I'll make more progress than mentally wearing myself out reading a bunch of LLM generated code trying to figure out how to solve the problem manually.

Anyway, it continues to make me uneasy, is all I'm saying.

As long as tokens count roughly equally towards subscription plan usage between 5.5 & 5.4, you can look at this as effectively a 5x increase in usage limits.

Seems so to me - see GPT-5.4[1] and 5.2[2] announcements.

Might be an tacit admission of being behind.

[1] https://openai.com/index/introducing-gpt-5-4/ [2] https://openai.com/index/introducing-gpt-5-2/

That's a wild statement to put into your announcement. Are LLM providers now openly bragging about our collective dependency on their models?

https://github.com/williamcotton/space-trader/commit/0859c65...

https://cdn.openai.com/pdf/18a02b5d-6b67-4cec-ab64-68cdfbdde...

The efficiency gap is enormous. Maybe it's the difference between GB200 NVL72 and an Amazon Tranium chip?

So much bench-maxxing is just giving the model a ton of tokens so it can inefficiently explore the solution space.

I'd not be surprised if this is the year where some models simply stop being available as a plain API, while foundation model companies succeed at capturing more use cases in their own software.

How does this work exactly? Is there like a "search online" tool that the harness is expected to provide? Or does the OpenAI infra do that as part of serving the response?

I've been working on building my own agent, just for fun, and I conceptually get using a command line, listing files, reading them, etc, but am sort of stumped how I'm supposed to do the web search piece of it.

Given that they're calling out that this model is great at online research - to what extent is that a property of the model itself? I would have thought that was a harness concern.

(same input price and 20% more output price than Opus 4.7)

Anyway - these benchmarks look really good; I’m hopeful on the qualitative stuff.

Yeah, this was the next step. Have RLVR make the model good. Next iteration start penalising long + correct and reward short + correct.

> CyberGym 81.8%

Mythos was self reported at 83.1% ... So not far. Also it seems they're going the same route with verification. We're entering the era where SotA will only be available after KYC, it seems.

However the language of ChatGPT is still the same slop as years ago, so many headings, so many emojis, so many "the important thing nobody mentions". 10 paragraphs of text for what should be a two paragraph response. Even with custom instructions (keep answers short and succinct) and using their settings (less list, less emoji, less fluff) it's still NOTICEABLY worse than Claude on base settings.

I've yet to test Codex, will get to that this weekend, but in terms of research or general Q&A I have no idea how anyone could prefer this to Claude. Unfortunately Claude has seemingly stopped giving a fuck about researching entirely.

https://deploymentsafety.openai.com/gpt-5-5

The large price bump might indicate the latter.

You can kind of use connectors like MCP, but having to use ngrok every time just to expose a local filesystem for file editing is more cumbersome than expected.

Particularly in areas outside straight coding tasks. So analysis, planning, etc. Better and more thorough output. Better use of formatting options(tables, diagrams, etc).

I'm hoping to see improvements in this area with 5.5.

This might be great if it translates to agentic engineering and not just benchmarks.

It seems some of the gains from Opus 4.6 to 4.7 required more tokens, not less.

Maybe more interesting is that they’ve used codex to improve model inference latency. iirc this is a new (expectedly larger) pretrain, so it’s presumably slower to serve.

I prescribe 20 hours of KSP to everyone involved, that'll set them right.

Once upon a time humans had to manually advance the spark ignition as their car's engine revved faster.

Once upon a time humans had to know the architecture of a CPU to code for it.

History is full of instances of humans meeting technology where it was, accommodating for its limitations. We are approaching a point where machines accommodate to our limitations -- it's not a point, really, but a spectrum that we've been on.

It's going to be a bumpy ride.

How much capability is lost, by hobbling models with a zillion protections against idiots?

Every prompt gets evaluated, to ensure you are not a hacker, you are not suicidal, you are not a racist, you are not...

Maybe just...leave that all off? I know, I know, individual responsibility no longer exists, but I can dream.

Anthropic is slightly better but where is 4.6 or 4.7 haiku or 4.7 sonnet etc.

I hope GPT 5.5 Pro is not cutting corners and neuter from the start, you got the compute for it not to be.

Since Feb when we got Gemini 3.1, Opus 4.6, and GPT-5.3-Codex we have seen GPT-5.4 and GPT-5.5 but only Opus 4.7 and no new Gemini model.

Both of these are pretty decent improvements.

The big question is: does it still just write slop, or not?

Fool me once, fool me twice, fool me for the 32nd time, it’s probably still just slop.

  https://www.nytimes.com/2026/04/23/technology/openai-new-model.html

However, this same-day article came out before people really looked at it. It seems largely intended to contrast OpenAI with Anthropic's caution, before there has been any evidence that the new model has cyber-security implications.

It's not at all clear that the broader discourse is helping, if even the NY Times is itself producing slop just to stoke questions.

Surely it doesn't still have the same ancient data cutoff as 5.4 did?

Because software and "information technology" generally didn't increase productivity over the past 30 years.

This has been long known as Solow's productivity paradox. There's lots of theories as to why this is observed, one of them being "mismeasurement" of productivity data.

But my favorite theory is that information technology is mostly entertainment, and rather than making you more productive, it distracts you and makes you more lazy.

AI's main application has been information space so far. If that continues, I doubt you will get more productivity from it.

If you give AI a body... well, maybe that changes.

Where's the demo link?

Are the tests getting harder and harder so the older AIs look worst and the new ones look like they are "almost there" ?

I don't want to be lazy.

Soo many unconvincing "I've had access for three weeks and omg it's amazing" takes, it actually primes me for it to be a "meh".

I prefer to see for myself, but the gradual rollout, combined with full-on marketing campaign, is annoying.

Anyways, still exciting to see more improvements.

...

> we’re deploying stricter classifiers for potential cyber risk which some users may find annoying initially

So we should be expecting to not be able to check our own code for vulnerabilities, because inherently the model cannot know whether I'm feeding my code or someone else's.

I'm not trying to make any kind of moral statement, but the company just feels toxic to me.

The LinkedIn/X influencers who hyped this as a Mythos-class model should be ashamed of themselves, but they’ll be too busy posting slop content about how “GPT-5.5 changes everything”.

https://arena.ai/leaderboard/code

The battle has just begun

I am still using Codex 5.3 and haven't switched to GPT 5.4 as I don't like the 'its automatic bro trust us', so wondering is Codex going to get these specific releases at all in the future.

Now, after all this time, this must shurely be the release that does all software developers out of a job ?

Or has Dirty Sam being caught lying, again ?

Cos I've still got a programming job, and GPT can't do it for shit.

The fact that GPT-5.5 is apparently even better at long-running tasks is very exciting. I don’t have access to it yet, but I’m really looking forward to trying it.

[1] https://news.ycombinator.com/item?id=47879330

Maybe this is a crazy theory, but I sometimes feel like they gimp their existing models before a big release to you'll notice more of a "step".

I have to imagine they'll go to Gemini 3.5 if only for marketing reasons.

Numbers look too good, wondering if it is benchmaxxed or not

Imagine spending 100m on some of these AI “geniuses” and this is the best they can do.

< 5 years until humans are buffered out of existence tbh

may the light of potentia spread forth beyond us