At this point, the deciding factors for me between Claude Code 20x and Codex Pro 20x are:
1/ Usage limits: downstream of input/output cost, but resets and obscure windows and odd 20x plan / 5x plan != 4x usage math throw a wrench into it. Winner right now is Codex by a mile, especially when you factor in the fact that ChatGPT usage (even 6 Astra Pro) is essentially unmetered on the 20x plan. Always a bummer when asking if I should see a doctor about a rash means I can't code as much. It's also is a godsend if you use an MCP like oracle to automate the process of calling the Pro model on particularly tough problems, giving better planning results or deeper code analysis without burning usage.
2/ Context window in the harness. Claude Code wins on this. There used to be a toml file workaround for Codex to extend the GPT context window to 1m, but this stopped working on the plans and only on per-token billing (ETA: noname120 pointed out this is no longer the case and it can be enabled again [1]). 252k is just not enough. Codex's compaction is very good, fwiw, but it happens so frequently that even a model as powerful as Astra sometimes loses the plot on long-running tasks.
3/ Ability to use the plan outside of the official harness. Codex wins. Anthropic does shit like bills requests as extra usage if it sees a hermes.md in a commit.
I've subscription hopped a bunch, and at times I've had both, but I keep coming back to Codex because it wins on 2/3.
ETA: apparently I haven't been Keeping Up With the Altmans and new 20x signups have been disabled for a few weeks. I am grandfathered in, which makes the comparison above pretty much moot.
> "Codex's compaction is very good, fwiw, but it happens so frequently that..."
I appreciate and follow Matt Pocock's advice: avoid autocompaction. Compaction is lossy, which is ok when you're managing it at phase boundaries, but autocompact is lossy at the most inopportune times, firing mid-task and leading to agents going off the rails.
Bad advice, compaction is why Codex is so fantastic.
My conversations compact hundreds of times. By the time it has done a dozen or so compactions, it fully understands the work I want it to do (and how). It's almost like having a fine-tuned Astra model.
I'm not sure I follow; how is autocompaction (lossy summarization), applied at random times (vs strategically, between workflow phases), helpful to ensuring clarity of intent? Maybe you're saying that just plowing ahead and living with the signal loss along the way works well enough for your purposes. In which case, ok, YMMV, different strokes.... but paying attention to context quality and being deliberate about when to compact vs handoff vs delegate to subagents is most definitely not "bad advice".
I agree with @erichocean on this. In theory, compaction is bad. But in practice I found the model is smart enough to write critical details down somewhere, and post-compaction the model doesn't make assumptions. A small amount of time is lost reading materials, but the benefit is that you can operate unbounded vs doing small controlled chunks, which is what I used to do with Opus back in the day. Now I just give it as big a task as I can think of.
This approach got good with Sol. With 5.5 I'd break tasks up, record planning docs, etc.
Now with Sol I rarely bother. It's really good at remembering the salient details. Its also great at continuing a pattern I setup, like commit after finishing each feature block, etc.
jeffnash · · focus · HN ↗
1/ Usage limits: downstream of input/output cost, but resets and obscure windows and odd 20x plan / 5x plan != 4x usage math throw a wrench into it. Winner right now is Codex by a mile, especially when you factor in the fact that ChatGPT usage (even 6 Astra Pro) is essentially unmetered on the 20x plan. Always a bummer when asking if I should see a doctor about a rash means I can't code as much. It's also is a godsend if you use an MCP like oracle to automate the process of calling the Pro model on particularly tough problems, giving better planning results or deeper code analysis without burning usage.
2/ Context window in the harness. Claude Code wins on this. There used to be a toml file workaround for Codex to extend the GPT context window to 1m, but this stopped working on the plans and only on per-token billing (ETA: noname120 pointed out this is no longer the case and it can be enabled again [1]). 252k is just not enough. Codex's compaction is very good, fwiw, but it happens so frequently that even a model as powerful as Astra sometimes loses the plot on long-running tasks.
3/ Ability to use the plan outside of the official harness. Codex wins. Anthropic does shit like bills requests as extra usage if it sees a hermes.md in a commit.
I've subscription hopped a bunch, and at times I've had both, but I keep coming back to Codex because it wins on 2/3.
ETA: apparently I haven't been Keeping Up With the Altmans and new 20x signups have been disabled for a few weeks. I am grandfathered in, which makes the comparison above pretty much moot.
[1]<a href="https://news.ycombinator.com/item?id=49806060">https://news.ycombinator.com/item?id=49806060
chrisweekly · · focus · HN ↗
I appreciate and follow Matt Pocock's advice: avoid autocompaction. Compaction is lossy, which is ok when you're managing it at phase boundaries, but autocompact is lossy at the most inopportune times, firing mid-task and leading to agents going off the rails.
erichocean · · focus · HN ↗
My conversations compact hundreds of times. By the time it has done a dozen or so compactions, it fully understands the work I want it to do (and how). It's almost like having a fine-tuned Astra model.
10/10, would recommend.
chrisweekly · · focus · HN ↗
edg5000 · · focus · HN ↗
elcritch · · focus · HN ↗
Now with Sol I rarely bother. It's really good at remembering the salient details. Its also great at continuing a pattern I setup, like commit after finishing each feature block, etc.