I don't know why anyone would use Copilot subscriptions now. They bill by API cost, and GPT-5.5 especially is very expensive on API. Just to give you an example, from my recent usage I calculated that on the $200 OpenAI sub I get about $2600 weekly GPT-5.5 API equivalent. Monthly = $10400. That's 52x cheaper than doing API costs directly, which is what Copilot is doing. And you can use Codex sub outside of Codex. FWIW this is because the $200 plan is still on 20x. Plus is 1x (so, about $130 weekly I guess), $100 Pro is 5x now, so about $750.
It's honestly bizarre how Microsoft has direct access to OpenAI models in Azure, owns GitHub, but still has to provide the OpenAI models at raw API cost instead of doing at least a 2x discount.
Just as an example, Gemini docs recommend NOT passing any sampling parameters at all for Gemini 3.x
https://t.co/bycycc54Y7
Opus 4.7 and 4.8 do not support any sampling parameters outright:
https://t.co/Paz9ayer2H
OpenAI reasoning models since ~o1 don't allow changing temperature.
@thesquashSH I remember how o3 back in the day would say stuff like "Tested on my MacBook M1 and the function runs in 0.5s" for competitive programming tasks (just pure hallucinations)
@antigravity Come on, r/osdev gets new vibe-coded OSes posted literally almost daily, any frontier Claude/GPT model is capable of that, and far more than just shell + DOOM.
@Someody42 (reposting) If you read the paper, they used Opus 4.5 for ACTUAL WORK. Like, what the fuck? Are they not aware that OpenAI has been ahead in math, for like, two years now? Even if the paper came out at March 31, 2026, they could've used GPT 5.1, 5.2, whatever was available.
@joseph_h_garvin If you read the paper, they used Opus 4.5 for ACTUAL WORK. Like, what the fuck? Are they not aware that OpenAI has been ahead in math, for like, two years now? Even if the paper came out at March 31, 2026, they could've used GPT 5.1, 5.2, whatever was available.
@romashka_tea@madebygoogle Make a public gist or commit to a public repo, it should get autorevoked. The same works for OpenAI and Anthropic keys, AWS keys get auto quarantined.
Big AI labs like OpenAI/Anthropic have similar tools in a broader sense. For example, OpenAI lets you define custom CFGs in Lark, and provide those to the API.
https://t.co/7CbQE4DFXt
This means that the LLM *will* have to follow the grammar, even if the line rangers/lines might not match, but the structure will be the exact same.
apply_patch's lark definition, from recent-ish Codex (the actual full apply_patch definition has the description too):
Patch := Begin { FileOp } End
Begin := "*** Begin Patch" NEWLINE
End := "*** End Patch" NEWLINE
FileOp := AddFile | DeleteFile | UpdateFile
AddFile := "*** Add File: " path NEWLINE { "+" line NEWLINE }
DeleteFile := "*** Delete File: " path NEWLINE
UpdateFile := "*** Update File: " path NEWLINE [ MoveTo ] { Hunk }
MoveTo := "*** Move to: " newPath NEWLINE
Hunk := "@@" [ header ] NEWLINE { HunkLine } [ "*** End of File" NEWLINE ]
HunkLine := (" " | "-" | "+") text NEWLINE