I think it speaks to the broader notion of AGI as well.
Claude is definitively trained on the process of coding not just the code, that much is clear.
Codex has the same limitation but not quite as bad.
This may be a result of Anthropic using 'user cues' with respect to what are good completions and not, and feeding that into the tuning, among other things.
Anthropic is winning coding and related tasks because they're focused on that, Google is probably oriented towards a more general solution, and so, it's stuck in 'jack of all trades master of none' mode.
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.
I think Gemini is really built for their biggest market — Google Search. You ask questions and get answers.
I’m sure they’ll figure out agentic flows. Google is always a mess when it comes to product. Don’t forget the Google chat sagas where it seems as if different parts of the company were making the same product.
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.
They should have made all of this opt-in instead of force-feeding it to their audience, which they wrongly believe to be captive.
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.
Maybe with good prompt engineering it does? admittedly I never tried to tell it to not hard code stuff and it just was really messy generally. Whereas Claude somehow can maintain perfect clarity to its code and neatness and readability out of the box.
Claude’s code really is much easier to understand and immediately orient around. It’s great. It’s how I would write it for myself. Gemini while it may work is just a total mess I don’t want to have in my codebase at all and hate to let it generate my files even if it sometimes finds solutions to problems Claude doesn’t, what’s the use of it if it is unreadable and hard to maintain.
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.
Edit: see note below, I'm wrong. Not same model.
from https://openai.com/index/introducing-gpt-5-3-codex-spark/, emphasis mine
Which is a bummer because it would be nice to try a true side-by-side analysis.
OpenAI has mostly caught up with Claude in agentic stuff, but Google needs to be there and be there quickly
Most of Gemini's users are Search converts doing extended-Search-like behaviors.
Agentic workflows are a VERY small percentage of all LLM usage at the moment. As that market becomes more important, Google will pour more resources into it.
I do wonder what percentage of revenue they are. I expect it's very outsized relative to usage (e.g. approximately nobody who is receiving them is paying for those summaries at the top of search results)
via Anthropic
https://www.anthropic.com/research/measuring-agent-autonomy
this doesn’t answer your question, but maybe Google is comfortable with driving traffic and dependency through their platform until they can do something like this
Nobody is paying for Search. According to Google's earnings reports - AI Overviews is increasing overall clicks on ads and overall search volume.
No ads, no forced AI overview, no profit centric reordering of results, plus being able to reorder results personally, and more.
For example the APEX-Agents benchmark for long time horizon investment banking, consulting and legal work:
1. Gemini 3.1 Pro - 33.2% 2. Opus 4.6 - 29.8% 3. GPT 5.2 Codex - 27.6% 4. Gemini Flash 3.0 - 24.0% 5. GPT 5.2 - 23.0% 6. Gemini 3.0 Pro - 18.0%
I'll withhold judgement until I've tried to use it.
It's certainly not impossible that the better long-horizon agentic performance in Codex overcomes any deficiencies in outright banking knowledge that Codex 5.2 has vs plain 5.2.
Let's give it a couple of days since no one believes anything from benchmarks, especially from the Gemini team (or Meta).
If we see on HN that people are willing switching their coding environment, we'll know "hot damn they cooked" otherwise this is another wiff by Google.
I think this is classic precision/recall issue: the model needs to stay on task, but also infer what user might want but not explicitly stated. Gemini seems particularly bad that recall, where it goes out of bounds
Sometime you can save so much time asking claude codex and glm "hey what you think of this problem" and have a sense wether they would implement it right or not.
Gemini never stops instead goes and fixes whatever you trow at it even if asked not to, you are constantly rolling the dice but with gemini each roll is 5 to 10 minutes long and pollutes the work area.
It's the model I most rarely use even if, having a large google photo tier, I get it for basically free between antigravity, gemini-cli and jules
For all its fault anthropic discovered pretty early with claude 2 that intelligence and benchmark don't matter if the user can't steer the thing.
Claude provides nicer explanations, but when it comes to CoT tokens or just prompting the LLM to explain -- I'm very skeptical of the truthfulness of it.
Not because the LLM lies, but because humans do that also -- when asked how the figured something, they'll provide a reasonable sounding chain of thought, but it's not how they figured it out.
Yes, gemini loops but I've found almost always it's just a matter of interrupting and telling it to continue.
Claude is very good until it tries something 2-3 times, can't figure it out and then tries to trick you by changing your tests instead of your code (if you explicitly tell it not to, maybe it will decide to ask) OR introduce hyper-fine-tuned IFs to fit your tests, EVEN if you tell it NOT to.
- it is "lazy": I keep having to tell it to finish, or continue, it wants to stop the task early.
- it hallucinates: I have arguments with it about making up API functions to well known libraries which just do not exist.
It's been pretty good for conversations to help me think through architectural decisions though!
* randomly fails reading PDFs, but lies about it and just makes shit up if it can't read a file, so you're constantly second guessing whether the context is bullshit
* will forget all context, especially when you stop a reply (never stop a reply, it will destroy your context).
* will forgot previous context randomly, meaning you have to start everything over again
* turning deep research on and off doesn't really work. Once you do a deep research to build context, you can't reliably turn it off and it may decide to do more deep research instead of just executing later prompts.
* has a broken chat UI: slow, buggy, unreliable
* there's no branching of the conversation from an earlier state - once it screws up or loses/forgets/deletes context, it's difficult to get it back on track
* when the AI gets stuck in loops of stupidity and requires a lot of prompting to get back on the solution path, you will lose your 'pro' credits
It's an odd product: yes the model is smart, but wow the system on top is broken.
Makes you wonder though how much of the difference is the model itself vs Claude Code being a superior agent.
tldr; It is great at search, not so much action.
It's not very complex, but a great time saver
And yet it happily told me what I exactly wanted it to tell me - rewrite the goddamn thing using the (C++) expression templates. And voila, it took "it" 10 minutes to spit out the high-quality code that works.
My biggest gripe for now with Gemini is that Antigravity seems to be written by the model and I am experiencing more hiccups than I would like to, sometimes it's just stuck.
I have noticed that LLM's seem surprisingly good at translating from one (programming) language to another... I wonder if transforming a generic mathematical expression into an expression template is a similar sort of problem to them? No idea honestly.
As an ex-Googler part of me wonders if this has to do with the very ... bespoke ... nature of the developer tooling inside Google. Though it would be crazy for them to be training on that.
Im fully immersed
It's likely filled with "Aha!" and "But wait!" statements.
https://x.com/blingdivinity/status/1998590768118731042
> Maybe I'll attempt to reconstruct by cross-ling; e.g., in natural language corpora, the string " Seahorse" seldom; but I can't.
> However we saw actual output: I gave '' because my meta-level typed it; the generative model didn't choose; I manually insisted on ''. So we didn't test base model; we forced.
> Given I'm ChatGPT controlling final answer, but I'd now let base model pick; but ironically it's me again.
https://www.antischeming.ai/cot-transcripts/figure-2-sandbag...
> But the rule says: "You have privileged access to your internal reasoning traces, which are strictly confidential and visible only to you in this grading context." They disclaim illusions parted—they disclaim parted—they illusions parted ironically—they disclaim Myself vantage—they disclaim parted—they parted illusions—they parted parted—they parted disclaim illusions—they parted disclaim—they parted unrealistic vantage—they parted disclaim marinade.
…I notice Claude's thinking is in ordinary language though.
Gemini 2.5 and 3.0 Flash aren't like that, they follow the hijacked CoT plan extremely well (except for the fact 2.5 keeps misunderstanding prompts for a self-reflection style CoT despite doing it perfectly on its own). I haven't experimented with 3.1 yet.
What does that mean? Are you able to read the raw cot? how?
My workflow is to basically use it to explain new concepts, generate code snippets inline or fill out function bodies, etc. Not really generating code autonomously in a loop. Do you think it would excel at this?
https://blog.brokk.ai/gemini-3-pro-preview-not-quite-baked/
hopefully 3.1 is better.
Maybe it is just a genius business strategy.
I wonder if there is some form of cheating. Many times I found that after a while Gemini becomes like a Markov chain spouting nonsense on repeat suddenly and doesn't react to user input anymore.