These releases are lacking something. Yes, they optimised for benchmarks, but it’s just not all that impressive anymore.
It is time for a product, not for a marginally improved model.
It's more hedonic adaptation, people just aren't as impressed by incremental changes anymore over big leaps. It's the same as another thread yesterday where someone said the new MacBook with the latest processor doesn't excite them anymore, and it's because for most people, most models are good enough and now it's all about applications.
Not necessarily, Alibaba is still working on it and the CEO is directly co-leading the team. Translated with Qwen 3.5:
> To all colleagues in the Tongyi Lab:
> The company has approved Lin Junyang’s resignation and thanks him for his contributions during his tenure. Jingren will continue to lead the Tongyi Lab in advancing future work. At the same time, the company will establish a Foundation Model Support Group, jointly coordinated by myself, Jingren, and Fan Yu, to mobilize group resources in support of foundation model development.
> Technological progress demands constant advancement — stagnation means regression. Developing foundational large models is our key strategic direction toward the future. While continuing to uphold our open-source model strategy, we will further increase R&D investment in artificial intelligence, intensify efforts to attract top talent, and move forward together with renewed commitment.
I am actually super impressed with Codex-5.3 extra high reasoning. Its a drop in replacement (infact better than Claude Opus 4.6. lately claude being super verbose going in circles in getting things resolved). I stopped using claude mostly and having a blast with Codex 5.3. looking forward to 5.4 in codex.
Same, it also helps that it's way cheaper than Opus in VSCode Copilot, where OpenAI models are counted as 1x requests while Opus is 3x, for similar performance (no doubt Microsoft is subsidizing OpenAI models due to their partnership).
I've been using both Opus 4.6 and Codex 5.3 in VSCode's Copilot and while Opus is indeed 3x and Codex is 1x, that doesn't seem to matter as Opus is willing to go work in the background for like an hour for 3 credits, whereas Codex asks you whether to continue every few lines of code it changes, quickly eating way more credits than Opus. In fact Opus in Copilot is probably underpriced, as it can definitely work for an hour with just those 12 cents of cost. Which I'm not sure you get anywhere else at such a low price.
Update: I don't know why I can't reply to your reply, so I'll just update this. I have tried many times to give it a big todo list and told it to do it all. But I've never gotten it to actually work on it all and instead after the first task is complete it always asks if it should move onto the next task. In fact, I always tell it not to ask me and yet it still does. So unless I need to do very specific prompt engineering, that does not seem to work for me.
That shouldn't really make a difference because you can just prompt Codex to behave the same way, having it load a big list of todo items perhaps from a markdown file and asking it to iterate until it's finished without asking for confirmation, and that'll still cost 1x over Opus' 3x.
If you're benchmarking something, old & well-characterized / understood often beats new & un-characterized.
Sure, there may be shortcomings, but they're well understood. The closer you get to the cutting edge, the less characterization data you get to rely on. You need to be able to trust & understand your measurement tool for the results to be meaningful.
I don't use OpenAI nor even LLMs (despite having tried https://fabien.benetou.fr/Content/SelfHostingArtificialIntel... a lot of models) but I imagine if I did I would keep failed prompts (can just be a basic "last prompt failed" then export) then whenever a new model comes around I'd throw at 5 it random of MY fails (not benchmarks from others, those will come too anyway) and see if it's better, same, worst, for My use cases in minutes.
If it's "better" (whatever my criteria might be) I'd also throw back some of my useful prompts to avoid regression.
Really doesn't seem complicated nor taking much time to forge a realistic opinion.
GP said "It is time for a product, not for a marginally improved model."
ChatGPT is still just that: Chat.
Meanwhile, Anthropic offers a desktop app with plugins that easily extend the data Claude has access to. Connect it to Confluence, Jira, and Outlook, and it'll tell you what your top priorities are for the day, or write a Powerpoint. Add Github and it can reason about your code and create a design document on Confluence.
OpenAI doesn't have a product the way Anthropic does. ChatGPT might have a great model, but it's not nearly as useful.
The models are so good that incremental improvements are not super impressive. We literally would benefit more from maybe sending 50% of model spending into spending on implementation into the services and industrial economy. We literally are lagging in implementation, specialised tools, and hooks so we can connect everything to agents. I think.
Plasma physicist here, I haven't tried 5.4 yet, but in general I am very impressed with the recent upgrades that started arriving in the fall of 2025: for tasks like manipulating analytic systems of equations, quickly developing new features for simulation codes, and interpreting and designing experiments (with pictures) they have become much stronger. I've been asking questions and probing them for several years now out of curiosity, and they suddenly have developed deep understanding (Gemini 2.5 <<< Gemini 3.1) and become very useful. I totally get the current SV vibes, and am becoming a lot more ambitious in my future plans.
If we don't need plasma physicists anymore then we probably have fusion reactors or something, which seems like a fine trade. (In reality we're going to want humans in the loop for for the forseeable future)
underrated comment, this is going to be the main differentiator going forward, the more powerful and versatile harness the more the models will be able to achieve and better/more advanced products will come out of it.
They don't need to be impressive to be worthwhile. I like incremental improvements, they make a difference in the day to day work I do writing software with these.
The product is putting the skills / harness behind the api instead of the agent locally on your computer and iterating on that between model updates. Close off the garden.
Not that I want it, just where I imagine it going.
5.3 codex was a huge leap over 5.2 for agentic work in practice. have you been using both of those or paying attention more to benchmark news and chatgpt experience?
That's for you to build; they provide the brains. Do you really want one company to build everything? There wouldn't be a software industry to speak of if that happened.
When did they stop putting competitor models on the comparison table btw?
And yeh I mean the benchmark improvements are meh. Context Window and lack of real memory is still an issue.
