But then they leave the door open for Anthropic on coding, enterprise and agentic workflows. Sensibly, that’s what they seem to be doing.
That said Gemini is noticeably worse than ChatGPT (it’s quite erratic) and Anthropic’s work on coding / reasoning seems to be filtering back to its chatbot.
So right now it feels like Anthropic is doing great, OpenAI is slowing but has significant mindshare, and Google are in there competing but their game plan seems a bit of a mess.
They should have made all of this opt-in instead of force-feeding it to their audience, which they wrongly believe to be captive.
You know what's also weird: Gem3 'Pro' is pretty dumb.
OAI has 'thinking levels' which work pretty well, it's nice to have the 'super duper' button - but also - they have the 'Pro' product which is another model altogether and thinks for 20 min. It's different than 'Research'.
OAI Pro (+ maybe Spark) is the only reason I have OAI sub. Neither Anthropic nor Google seem to want to try to compete.
I feel for the head of Google AI, they're probably pulled in major different directions all the time ...
Using this method I could recreate "deep research" mode on a private collection of documents in a few minutes. A markdown file can be like a script or playbook, just use checkboxes for progress. This works for models that have file storage and edit tools, which is most, starting with any coding agent.
It's a different kind of solution altogether.
I suggest trying it.
This definitely feels like it.
It's hard to really judge, but Gemini feels like it might actually write better code, but the _process_ is so bad that it doesn't matter. At first I thought it was bad integration by the GitHub Copilot, but I see it elsewhere now.
I have a pretty crude mental model for this stuff but Opus feels more like a guy to me, while Codex feels like a machine.
I think that's partly the personality and tone, but I think it goes deeper than that.
(Or maybe the language and tone shapes the behavior, because of how LLMs work? It sounds ridiculous but I told Claude to believe in itself and suddenly it was able to solve problems it wouldn't even attempt before...)
I use one to code and the other to review. Every few days I switch who does what. I like that they are different it makes me feel like I'm getting different perspectives.
Codex is a 'poor communicator' - which matters surprisingly a lot in these things. It's overly verbose, it often misses the point - but - it is slightly stronger in some areas.
Also - Codex now has 'Spark' which is on Cerebras, it's wildly fast - and this absolutely changes 'workflow' fundamentally.
With 'wait-thinking' - you an have 3-5 AIs going, because it takes time to process but with Cerebras-backed models ... maybe 1 or 2.
Basically - you're the 'slowpoke' doing the thinking now. The 'human is the limiting factor'. It's a weird feeling!
Codex has a more adept 'rollover' on it's context window it sort of magically does context - this is hard to compare to Claude because you don't see the rollover points as well. With Claude, it's problematic ... and helpful to 'reset' some things after a compact, but with Codex ... you just keep surfing and 'forget about the rollover'.
This is all very qualitative, you just have to try it. Spark is only on the Pro ($200/mo) version, but it's worth it for any professional use. Just try it.
In my workflow - Claude Code is my 'primary worker' - I keep Codex for secondary tasks, second opinions - it's excellent for 'absorbing a whole project fast and trying to resolve an issue'.
Finally - there is a 'secret' way to use Gemini. You can use gemeni cli, and then in 'models/' there is a way to pick custom models. In order to make Gem3 Pr avail, there is some other thing you have to switch (just ask the AI), and then you can get at Gem3 Pro.
You will very quickly find what the poster here is talking about: it's a great model, but it's a 'Wild Stallion' on the harness. It's worth trying though. Also note it's much faster than Claude as well.
Spark on the other hand is a bit faster at reaching a point when it says "Done!", even when there is lots more it could do. The context size is also very limiting, you need to really divide and conquer your tasks, otherwise it'll gather files and context, then start editing one file, trigger the automatic context compaction, then forget what it was doing and begin again, repeating tons of time and essentially making you wait 20 minutes for the change anyways.
Personally I keep codex GPT5.2 as the everyday model, because most of the stuff I do I only want to do once, and I want it to 100% follow my prompt to the letter. I've played around a bunch with spark this week, and been fun as it's way faster, but also completely different way of working, more hands-on, and still not as good as even the gpt-codex models. Personally I wouldn't get ChatGPT Pro only for Spark (but I would get it for the Pro mode in ChatGPT, doesn't seem to get better than that).
Your intuition may be deceiving you, maybe assuming it's a speed/quality trade-off, it's not.
It's just faster hardware.
No IQ tradeoff.
If you toy around with Cerebras directly, you get a feel for it.