> "You're holding it wrong." Is hardly a retort from a real paying customer having problems with their paid services.

It's very appropriate in the cases when you're holding it wrong. The fact that you're paying doesn't mean that you can't make mistakes or waste resources.

Sounds like that's the problem then, 275k is a tiny context window. I regularly have sessions that go to 450k or even up to 700k for an unattended overnight Claude Opus session.

Apparently OpenAI makes you manually setup their 1 Million context window, and it seems to be only documented on X:

https://x.com/thsottiaux/status/2089082893804896524

There's at least a forum thread about it here:

https://community.openai.com/t/why-does-codex-report-a-258-4...

Same for me, I started wondering if maybe workflows using compaction instead of clear + markdown memory would be more efficient. Writing a plan or tasks to a file often has the next session repeat part of the exploration, compaction seems to keep most relevant context.

Gemini 3.8 Flash is actually pretty good.

cheerleader hallucinating agent. That's Gemini.

My side by side evaluation this week was to build a tool for mounting my app’s UI components in a headless chrome and feeding mock data into them, for the purpose of taking screenshots for help docs. Not super complicated, but a real task I needed done.

I gave the task to codex first, sol 6 xhigh. it took a couple back and forth prompts to define the project and then it worked for a bit and to took a couple more prompts before I decided it was good enough - not perfect, but close. It re-implemented some wrapper components in a simplified way that lost some of the UI, but it would work.

Opus 5.5 high took the same prompt with no back and forth, it just went off and one-shotted a tool that takes pixel-perfect screenshots of exactly what my app looks like.

You HAVE to have a set of personal evals for each class of task you want to use models against at scale so you can test plausible candidates and compare output on your work against your evals.

There is way too much subtlety in what does and doesn't work for a given problem, context/prompt, tool set and eval. I can tell you Fable is generally better than Haiku, but comparing similar tiers really does depend on your exact context.

> OpenAI models used to be the prototype for robotic text, but lately I've been finding them much more natural.

This was the biggest thing I noticed in the 6 models; their conversational prose is dramatically less grating.

For me Anthropic models from 4.7 to 5 including where bad and ate tokens like crazy. Task delivery was worse than GPT 5.6 and token usage was 2-3x higher.

Looks like 5.5 is the new 4.6

I think it’s one of the reasons why you often see people decrying the lessening capabilities of the models a few weeks later, despite there being 0 proof of any changes, and evidence of the models staying the same from sites that track it.

They form these super strong opinions after a few prompts, then face reality over time.

People have been talking about how good whatever model is at “complex” tasks since the beginning, never mind that all of those models are now outperformed by Luna which many people consider unusable for complex work.

Try it, it's that good compared to openai current offering.

I get better results and usage our of my $20 claude sub than my $100 openai sub... it's that ridiculous

While I have no experience comparing this brand-new model, OpenAI themselves call it "near-Astra" intelligence. I set Astra and Opus 5.5 independently working on the same large research/coding task in an experimental project (doing NURBS surface modeling stuff). They had the same starting repo state, same task packet, same test suite to try to meet. I have the $100 plan in both.

Astra used 215% of a week's budget (I burned 2 free resets) and took 13 hours. Opus used 20% of a week's budget and took 20 hours. Both were asked to use lesser sub-agents for implementation grunt work at their discretion (Luna, Sonnet) as long as they manage and review the output.

The timing comparison is not that interesting because the wall-clock speed mostly reflects how often they ran the (large, slow) test suite, not their coding speed. Although in the past my gut feeling is that OpenAI models do generally respond faster.

The quality of their implementation was more interesting. There turned out to be a bug in one of the unit tests the agents were trying to pass. Opus interpreted the natural-language requirements from the task packet, found the test bug, and fixed it. Astra tried hard to solve the problem without altering the test suite. In practical terms Opus got much, much farther into a useful implementation. Astra was still stubbing out and faking critical parts of the implementation (B-splines) and since it ultimately couldn't pass the full test suite, finally gave up on its implementation. Astra wrote some useful tooling in the process of its efforts which I ended up integrating into Opus's version of the code, but otherwise its approach was behind.

Now, this is just one comparison in one domain, and arguably Astra's strict adherence to the tests as-given is a good thing. But Opus wasn't merely loosening the rules / moving the goalposts to pass, it spotted an actual bug, and was more successful at doing what I actually wanted. And the cost difference was Astra-nomical.

Out of curiosity for an interpretation free from my personal bias, I gave Astra a hint from Opus and permission to change the test in question, which it did, and got a bit farther, but still ultimately didn't produce a working implementation (to be fair, Opus's was not completely working either, but was closer). I then fired up fresh agents to review the two repos. Predictably, an Opus agent thought the Opus-written repo was the better basis to build on, and an Astra agent thought the Astra-written repo was the one to keep. They were not explicitly told which was which nor did the commit trailers say, but I assume they can tell. However, after doing this twice each, I saved the 4 review reports into another folder and did yet another meta-review of the 4 reports, so each would see the arguments and critiques both directions. In this meta-review both Astra and Opus converged on preferring the Opus implementation.

Yeah totally agree, people keep jumping in w/ strong views hours after release, eg: https://news.ycombinator.com/item?id=49045430

They’re comparing against the previous model, not the newly released one (6.1). Why do that on a thread about the new model, I don’t know.

benchmarks.

Only takes 5-10 minutes to test your favorite one shot comparison prompt.

Well, it is genuine.

Opus 5.5 fails the "understanding" tasks which Opus 5 passes. I feed it a script which takes two numbers and prints the max of the two numbers. Opus 5.5 thinks it prints 1/0 instead of the max numbers. Opus 5 gets it right.

Here are the outputs from both: https://gist.github.com/dom96/b5bce82b6e6c1ebd5271ed70ad941b....

Looking at that Opus 5.5 fails to deduce that the "hack statement" is actually an if statement in disguise, but Opus 5 gets this right. I feel like this is a pretty good test and shows Opus 5's greater intelligence.

> Spin up a bunch of loops or fire a semi-autonomous factory at a project and it's pretty easy to blow through a 20x account in a few hours if you can afford the sandboxes, CI and other infra required.

This is a tiny minority of people, not “most people”.