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Comment by anonfunction | Hacker News Reader

I've been doing bias and misaligned behavior research, creating custom private eval suites to test and compare models. Claude Opus 4.7 is heavily biased and presents clear regulatory and reputational risk.

It seems the initial product footprint tries to sidestep this problem by not giving the agents control on who to lend to or which applications to approve. Even so I think it's quite an optimistic read on their end. Happy to share reports to anyone who's interested (montana@latentevals.com), especially if you work at a frontier model lab and are interested in plugging my evals into your RL systems!

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Slightly related, I used Opus 4.6 to help me make marketing copy and ideas for my app. It understood the vibe I was going for on my baby-naming app (elation at discovery, curiosity, shared experiences), while 4.7 instantly wanted to pit the couples against each other (really highlighting the he said/she said) and the marketing copy went from "find a name easier" to "Our new feature is great. You're welcome." I can't get it to drop the snarky sass no matter how much I change CLAUDE.md, brand voice, etc.

All I did was upgrade claude code and use the new model. It most definitely exhibits misaligned behavior (compared to 4.6)

I tried Opus 4.7 for two days before I started beginning every session with "claude --model claude-opus-4-6".

I assume that 4.6 will become unavailable at some point, but I hope not any time soon. 4.7 hit usage limits faster, didn't do anything obviously better, and had more annoying behaviors in other aspects. I don't know if this is strictly a model issue or if there are also problems with how it's harnessed through Claude Code. I'm not willing to spend more time digging into it until I'm forced to.

Join me in a petition for them to opensource 4.6 as their first model! It'll be like gemma4 but good enough for all the coding we do.

Nobody is using LLMs to make lending decisions. They are using LLMs to read, extract and audit the supporting documents that go into normal well-tested, compliant and rules-based underwriting systems. And firms A/B test against humans doing the same work. The outcomes your are looking for are metrics like delivering faster results back to customers, with fewer mistakes and less fraud, more compliant, than a comparable human-only process.

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