I've had no problems like the ones you've mentioned while using Opus 4.8. It does overthink stuff with higher effort levels but that's kind of expected.

> I feel like this benchmark reiterates my disbelief that anyone uses the latest Anthropic models for any productive work. They seem to be the best at burning tokens and spawning unnecessary subagents even for well-defined and tightly scoped tasks.

I keep Claude around for some specific tasks:

- Linked up to Figma MCP to implement front-end stuff

- Data analysis, in the "Connect AI to a data source and ask questions" way. I've tried both Opus 4.8 high and GPT 5.5 high for this and Opus is stronger because it gets the intent in the question better

I used to keep it around for planning too, but the 4.8 plans have had more holes than swiss cheese.

Now that enterprise customers are pay-as-you-go with tokens I suspect we'll see renewed interest in OpenAI and their focus on token efficiency. At least I hope so if the alternative is abandoning the tools entirely.

> I'm starting to wonder if this increased token usage is inadvertently bleeding into how Anthropic actually trains their model

Related: Sonnet 5’s new tokenizer increases token usage by 30%. (https://simonwillison.net/2026/Jun/30/claude-sonnet-5/)