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Anonymous request-token comparisons from Opus 4.6 and Opus 4.7

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Re: Anonymous request-token comparisons from Opus 4.6 and Opus 4.7

#141

Post by hopfenspergerj »

jimkleiber wrote: post #138

I wonder if this is like when a restaurant introduces a new menu to increase prices. Is Opus 4.7 that significantly different in quality that it should use that much more in tokens? I like Claude and Anthropic a lot, and hope it's just some weird quirk in their tokenizer or whatnot, just seems like something changed in the last few weeks and may be going in a less-value-for-money direction, with not much being said a…

You can't accidentally retrain a model to use a different tokenizer. It changes the input vectors to the model.

Re: Anonymous request-token comparisons from Opus 4.6 and Opus 4.7

#142

Post by gsleblanc »

It's increasingly looking naive to assume scaling LLMs is all you need to get to full white-collar worker replacement. The attention mechanism / hopfield network is fundamentally modeling only a small subset of the full human brain, and all the increasing sustained hype around bolted-on solutions for "agentic memory" is, in my opinion, glaring evidence that these SOTA transformers alone aren't sufficient even when you just limit the space to text. Maybe I'm just parroting Yann LeCun.

Re: Anonymous request-token comparisons from Opus 4.6 and Opus 4.7

#143

Post by danw1979 »

jherdman wrote: post #102
blahblaher wrote: post #56

Earlier quoted context omitted.

qwen3.5/3.6 (30B) works well,locally, with opencode

Is this sort of setup tenable on a consumer MBP or similar?

Qwen’s 30B models run great on my MBP (M4, 48GB) but the issue I have is cooling - the fan exhaust is straight onto the screen, which I can’t help thinking will eventually degrade it, given the thermal cycling it would go through. A Mac Studio makes far more sense for local inference just for this reason alone.

Re: Anonymous request-token comparisons from Opus 4.6 and Opus 4.7

#144

Post by pwython »

pitched wrote: post #89
blahblaher wrote: post #56

Earlier quoted context omitted.

qwen3.5/3.6 (30B) works well,locally, with opencode

I want to bump this more than just a +1 by recommending everyone try out OpenCode. It can still run on a Codex subscription so you aren’t in fully unfamiliar territory but unlocks a lot of options.

pi-coding-agent (pi.dev) is also great. I've been using it with Gemma 4 and Qwen 3.6.

Re: Anonymous request-token comparisons from Opus 4.6 and Opus 4.7

#145

Post by [deleted] »

Shailendra_S wrote: post #40

45% is brutal if you're building on top of these models as a bootstrapped founder. The unit economics just don't work anymore at that price point for most indie products. What I've been doing is running a dual-model setup — use the cheaper/faster model for the heavy lifting where quality variance doesn't matter much, and only route to the expensive one when the output is customer-facing and quality is non-negotiable.…

[deleted]

Re: Anonymous request-token comparisons from Opus 4.6 and Opus 4.7

#146

Post by pitched »

bobjordan wrote: post #125

I've spent the past 4+ months building an internal multi-agent orchestrator for coding teams. Agents communicate through a coordination protocol we built, and all inter-agent messages plus runtime metrics are logged to a database. Our default topology is a two-agent pair: one implementer and one reviewer. In practice, that usually means Opus writing code and Codex reviewing it. I just finished a 10-hour run with 5 of…

I just switched fully into Codex today, off of Claude. The higher usage limits were one factor but I’m also working towards a custom harness that better integrates into the orchestrator. So the Claude TOS was also getting in the way.

Re: Anonymous request-token comparisons from Opus 4.6 and Opus 4.7

#147

Post by _blk »

bluefirebrand wrote: post #82
tossandthrow wrote: post #69

Earlier quoted context omitted.

You can argu that you will have skill atrophy by not using LLMs. We have gone multi cloud disaster recovery on our infrastructure. Something I would not have done yet, had we not had LLMs. I am learning at an incredible rate with LLMs.

> I am learning at an incredible rate with LLMs Could you do it again without the help of an LLM? If no, then can you really claim to have learned anything?

The challenge is not if you could do all of it without AI but any of it that you couldn't before.

Not everyone learns at the same pace and not everyone has the same fault tolerance threshold. In my experiencd some people are what I call "Japanese learners" perfecting by watching. They will learn with AI but would never do it themselves out of fear of getting something wrong while they understand most of it, others that I call "western learners" will start right away and "get their hands dirty" without much knowledge and also get it wrong right away. Both are valid learning strategies fitting different personalities.

Re: Anonymous request-token comparisons from Opus 4.6 and Opus 4.7

#148

Post by glerk »

I'd be ok with paying more if results were good, but it seems like Anthropic is going for the Tinder/casino intermittent reinforcement strategy: optimized to keep you spending tokens instead of achieving results.

And yes, Claude models are generally more fun to use than GPT/Codex. They have a personality. They have an intuition for design/aesthetics. Vibe-coding with them feels like playing a video game. But the result is almost always some version of cutting corners: tests removed to make the suite pass, duplicate code everywhere, wrong abstraction, type safety disabled, hard requirements ignored, etc.

These issues are not resolved in 4.7, no matter what the benchmarks say, and I don't think there is any interest in resolving them.

Re: Anonymous request-token comparisons from Opus 4.6 and Opus 4.7

#150

Post by dewarrn1 »

dakiol wrote: post #46

We dropped Claude. It's pretty clear this is a race to the bottom, and we don't want a hard dependency on another multi-billion dollar company just to write software We'll be keeping an eye on open models (of which we already make good use of). I think that's the way forward. Actually it would be great if everybody would put more focus on open models, perhaps we can come up with something like the "linux/postgres/git…

I'm hopeful that new efficiencies in training (Deepseek et al.), the impressive performance of smaller models enhanced through distillation, and a glut of past-their-prime-but-functioning GPUs all converge make good-enough open/libre models cheap, ubiquitous, and less resource-intensive to train and run.
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