Posted by AmazingTurtle 1 day ago
I was going to explore paying for Codex since OpenAI seems to be a bit more generous with rate limits, but I'm now not sure - for the stuff I do limited context size would be a dealbreaker - basically lots and lots of documentation and guideline references, code review loops, documentation references and web searches, multi-repo exploration, lots of tool calls and so on.
I might just upgrade to Anthropic's more expensive Max subscription or something to get more subsidized tokens. Even with plan files and the plan mode, it's like a slot machine after compacting the context, sometimes steps or other details just evaporate in thin air, the less of that I need to do the better. It's not that it can't work, it just doesn't work reliably enough not to be annoying.
Luckily DeepSeek V4 Pro, GLM 5.2 and Kimi K3 don't seem to have those limits either - though DS is around Sonnet, GLM 5.2 feels a bit above Sonnet and only K3 only really is in the proper Opus ballpark that's good enough for me to work undisturbed, even if it seems to be slower.
Will say that 5.6-Sol is a minor bump in my benchmarks in most areas vs 5.5 but a severe regression in a few specific task focused on rearranging trees, addressing merge conflicts, etc. where the model to accomplish the task does not properly adhere to prompts in a way GPT-5 originally managed, not retaining parts of history in the way prompted despite specific instructions not to as that made the final completion easier…
I am of the conservative and cautious opinion that no model should be able to run destructive tasks at all, I have seen every model do things that make me concerned enough to maintain that opinion and know my evals can’t catch everything. But for 5.6-Sol specifically, I’d caution everyone to reevaluate how you run the model, maybe take a few more precautions you tend to forgo.
It is extremely capable as a reviewer and for extensive tasks, though for the later, the safety net I feel is required to be comfortable limits the utility. The code 5.6-Sol provides also still is a bit harder to parse in reviews.
Release strategy wise, feel it’s have been smarter to release only Luna and Sol now, then Terra a few weeks of posttraining later, I simply cannot see a purpose for it in the current form given how well both Luna and Sol scale up and down respectively with reasoning. Two models from a lab at a time is also the limit I feel one can properly assess at a time.
There are attention mechanisms that help to mitigate this, but you can clearly see in the chart that Tibo posted that the attention mechanisms they are using are still scaling quadratically.
This becomes an optimization problem for token t:
min f[cost, quality loss] = compaction cost[t] + compaction quality loss[t] + token cost[t]
On the other hand, $10 for 1M tokens still seems really high? It's not too hard to blow through that in an hour or two.
I forget whether it's documented or not, and it is kind of annoying that that's how you find it, but it does tell you. Maybe useful for the future if nothing else.
Anecdotally I've been stuck in that situation of being at 400-500k tokens and "just one more prompt bro" will get the task done, and I appreciate not having to wait through a compaction. If anything, keeping the bloated context helps with accuracy at the expense of speed in these cases.
