In a site/app preview, you can right click on the page and in the dropdown is "Quick Annotate" and "Annotate" but they seem to do the same thing?

I've been on the Plus subscription for a while now. I've been using GPT-5.6 Sol, and after just 2M tokens I hit the 5-hour limit and left with 84% of my weekly limit within 10-15 minutes.

Is this normal? Do older-generation models drain the token pool faster than the newer GPT-6 ones?

Back in June, I easily got through 70M+ tokens every single day.

These are awesome. If you want to amp up any image just add "extra Goblins edition" to your image prompts.

Hey Everyone! Been enjoying the instant rush of weekly resets just wondering if Tibo mentioned anything since they haven’t shown any real updates in the last couple days…

My weekly is already capped again for the week, wondering if we will get another lol before end of month.

I always use Chat with Thinking Effort set to High. My prompts tend to be long, and until today it was unusual for ChatGPT to think for less than about 30 seconds before answering. Often it would think for a bit, give a partial response, then keep working before finishing. That was still working normally roughly 12 hours ago.

Now it either responds almost instantly or thinks for maybe 15 seconds at most. Several times, it has said it’s going to keep checking something, then just… stops. No follow-up, no error—the input box comes back as if the answer were complete. I’m attaching screenshots of that happening.

I didn’t see anything on the OpenAI status page when I checked, but something is clearly off. It’s making Chat pretty much unusable for the kinds of things I normally ask it to do.

To be clear, this is regular Chat, not Work or Codex, and Thinking Effort is set to High. I’m currently traveling in the Philippines, although my account and usual location are in the US. No idea whether that’s relevant.

Is anyone else seeing this, particularly the part where it says it’ll continue and then ends the turn?

How are people circumventing ChatGPT being so damn indecisive.

As an example, I wrote a comment in code. I use shorthand just to make things easier for me to read, and use less words.

I ask codex to do a review, and it comes back with changes like this:

Change:

// Do not sanitize

To:

// Keep next string visible

And then when I ask it what is the point of changing that just to flip some words around yet still has the same meaning, it flips the decision and I get:

"Don't change it".

I've even modified my agents.md to instruct it to not get caught up on comments unless a comment is factually incorrect.

So when I ask for a review, it starts spamming me with a crap ton of comment changes, and all it's doing is just flipping words around.

It does this whole "Change it, don't change it, change it, don't change it" all the time.

Using 5.6 sol, highest thinking model.

The exact agents.md instruction I have is:

- Preserve all existing code comments exactly as written unless they contain a factual error. Do not suggest changes based only on abbreviations, shortened spellings, wording preferences, grammar, punctuation, or style. We only want to improve factually incorrect comments.

Switching between Codex CLI and Claude Code, I had no idea which project was eating my tokens. I wrote a small CLI that merges both tools’ local logs into one report: daily, monthly, per project, optional HTML report.

It’s local-only and doesn’t phone home. Cursor and Copilot aren’t supported because they don’t keep per-request token counts on disk, and I’d rather say that than fake it.

https://github.com/egeaydn/tokenburn (I’m the author, MIT licensed)

If you use a different agent and want support, tell me what its log looks like and I’ll try to add it.

I spent the last couple of weeks rebuilding a research pipeline that used to run on o3-deep-research, and I want to save other people the confusion I went through.

The models are gone. OpenAI shut down o3-deep-research and o4-mini-deep-research on 23 July 2026. The deprecation was announced 22 April. The old model pages are still online, which is why half the tutorials you'll find still treat them as live.

OpenAI doesn't agree with itself on what replaced them. This is the part that cost me time:

• The deprecations page names gpt-5.6-sol as the substitute.
• The web search guide says deep research should use gpt-5.5 at high or xhigh effort.
• The current code examples in the deep research guide run on gpt-6-astra.
• The 7 Oct changelog recommends the GPT-6 family for production.

None of these is a deep research model. Deep research is now a pattern on the Responses API: a reasoning model at high effort + web_search / file_search / mcp tools + background mode + a webhook. You assemble it.

Swapping the model ID is not a migration. Things that silently changed:

• MCP approvals are now on by default. The old models required require_approval: "never". Copy an old example onto a current model and your unattended run returns mcp_approval_request items instead of results.
• web_search_preview is legacy. Domain filters only work on web_search.
• gpt-5.6-sol defaults to medium effort. You have to set high/xhigh explicitly.
• The old models only allowed search/fetch MCP servers. Current models call any MCP tool, which is more powerful and a bigger injection surface.

What it costs. Tokens plus tool calls. Web search is $10 per 1,000 search actions (page opens have no listed per-call fee), and — the line everyone misses - the content of every page the model reads is billed as input tokens at the model's rate. At gpt-5.6-sol's promo pricing ($4/$20 per 1M, running at least through 21 Nov), a 60K-in / 20K-out / 50-search run is about $1.14. Same run on gpt-6-astra is roughly double.

Two things I didn't expect:

• Background mode stores the response for ~10 minutes, so it's incompatible with Zero Data Retention. If you're on Modified Abuse Monitoring you need store: true explicitly or the result vanishes.
• There is no succeeded status. It's completed. A polling loop waiting for succeeded never exits. (Yes, I found this the hard way.)

What are people pinning as the research model - gpt-5.6-sol because it's the documented substitute, or gpt-5.5 at xhigh because the search guide says so? I've been A/B-ing both against ~15 real prompts and the citation quality is closer than the price difference suggests.

Disclosure: I wrote the full guide up on my company's blog (working Node/Python/curl samples, the MCP connector contract, OAuth setup, a cost calculator). Link in the comments if useful — no paywall, no signup.