I recently had to disable their Chrome extension because it made the browser grind to a halt (spammed mojo IPC messages to the main thread according to a profiler). I wasn't the only one affected, going by the recent extension reviews. I wonder if it's related.
Hasn't this been the case for a while? I vaguely remember using the optimal e8 packing to try to get denser product quantization for vector embeddings.
Out of curiosity, how many tokens are people using? I checked my openrouter activity - I used about 550 million tokens in the last month, 320M with Gemini and 240M with Opus. This cost me $600 in the past 30 days. $200 on Gemini, $400 on Opus.
My Claude Code usage stats after ~3 months of heavy use:
Favorite model: Opus 4.6 Total tokens: 42.6m
Sessions: 420 Longest session: 10d 2h 13m
Active days: 53/95 Longest streak: 16 days
Most active day: Feb 9 Current streak: 4 days
~158x more tokens than Moby-Dick
Monthly breakdown via claude-code-monitor (not sure how accurate this is):
Month Total Tokens Cost (USD)
2026-01 96,166,569 $112.66
2026-02 340,158,917 $393.44
2026-03 2,183,154,148 $3,794.51
2026-04 1,832,917,712 $3,412.72
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Total 4,452,397,346 $7,713.34
Have you tried the latest (3.1 pro) Gemini? In my experience, it's notably better for a similar type of problems than Opus 4.6. However, I don't really use OpenAI products to compare.
I actually haven't - I tried Gemini 3.0 Pro in Antigravity and was disappointed enough that I didn't pay much attention to the 3.1 release, it was notably worse than Opus and GPT at the time, and much more prone to "think" in circles or veer off into irrelevant tangents even with fairly precise instruction. I'll give 3.1 a try tomorrow, see what happens.
They remind me more of the Korean TV series Cashero. There, the main hero has strength superpowers, but the power comes by crunching through cash in his pocket.
But I use the per-token APIs for my usage, not a subscription, so I'm guessing I'm in the minority.
I also use them per-token (and strongly prefer that due to a lack of lock-in).
However, from a game theory perspective, when there's a subscription, the model makers are incentivized to maximize problem solving in the minimum amount of tokens. With per-token pricing, the incentive is to maximize problem solving while increasing token usage.
I don't think this is quite right because it's the same model underneath. This problem can manifest more through the tooling on top, but still largely hard to separate without people catching you.
I do agree that Big Ai has misaligned incentives with users, generally speaking. This is why I per-token with a custom agent stack.
I suspect the game theoretic aspects come into play more with the quantizing. I have not (anecdotally) experienced this in my API based, per-token usage. I.e. I'm getting what I pay for.
Some more love for the updates page. E.g. select a subset of updates to install, be more clear that the last update time could be different if you installed updates via CLI, that kind of thing.
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