> If I ask them a question about which library is best for a given task, they'll download 4 different candidates, read the source, and write test programs. Meanwhile Opus will look at a couple of web pages and give me glib bullshit answer. Opus is often like a brilliant coworker who can't be bothered to do their job.
I’ve been using Qwen3.8FN and DeepSeek. I would not trust them to evaluate 4 different libraries from scratch unless I gave them extremely specific benchmarks to run. They’re very good at consuming tokens for long stretches and then giving you what looks like thoroughly researched output, but their intelligence just isn’t at a level I trust.
But really, the difference you’re describing is easily shaped by prompting. If you want Opus to download and compare the libraries, just tell it to. It will. All of the models benefit from being clear in your intentions. If you try to find models that will read your mind where spending 20 extra seconds to type out your intentions would accomplish the goal, you’re ignoring the very important intelligence and efficiency differences between models.
> Look, all I actually need out of a model is things like "tell me where this distributed algorithm might break", "get this ancient C code to build on modern Debian with modern dependencies", or "write a plan to migrate this database from MySQL to PostgreSQL and to test that the app still works." The modern open models can do all that, no problem.
I actually read the code output for projects that matter. There is a huge difference between a model that can eventually accomplish a shallow goal like “fix this bug” and a model that does it cleanly. Even with Opus or Astra I will spot cleanup issues frequently.
When using models down to Qwen3.8-FN or DS-Flash size, the code can feel like the kind of spaghetti mess that you get from an overly caffeinated and confident junior developer who accomplished the goal via brute force and staying up all night. Yeah it compiles and does the thing you asked, but all of that spaghetti code compounds with each change until a couple months later your harness is spending half a day and countless tokens doing simple tasks because it has to navigate the mess of a codebase.
That problem is much less of an issue at the Opus level and I can trust Opus to clean up better when directed to do so. It’s those differences that compound.
But if I’m doing a throwaway task in a sandbox that just needs to work for a day? I’ll throw it to the open models and see what they can do. They are interesting to play with.
I’ve been using Qwen3.8FN and DeepSeek. I would not trust them to evaluate 4 different libraries from scratch unless I gave them extremely specific benchmarks to run. They’re very good at consuming tokens for long stretches and then giving you what looks like thoroughly researched output, but their intelligence just isn’t at a level I trust.
But really, the difference you’re describing is easily shaped by prompting. If you want Opus to download and compare the libraries, just tell it to. It will. All of the models benefit from being clear in your intentions. If you try to find models that will read your mind where spending 20 extra seconds to type out your intentions would accomplish the goal, you’re ignoring the very important intelligence and efficiency differences between models.
> Look, all I actually need out of a model is things like "tell me where this distributed algorithm might break", "get this ancient C code to build on modern Debian with modern dependencies", or "write a plan to migrate this database from MySQL to PostgreSQL and to test that the app still works." The modern open models can do all that, no problem.
I actually read the code output for projects that matter. There is a huge difference between a model that can eventually accomplish a shallow goal like “fix this bug” and a model that does it cleanly. Even with Opus or Astra I will spot cleanup issues frequently.
When using models down to Qwen3.8-FN or DS-Flash size, the code can feel like the kind of spaghetti mess that you get from an overly caffeinated and confident junior developer who accomplished the goal via brute force and staying up all night. Yeah it compiles and does the thing you asked, but all of that spaghetti code compounds with each change until a couple months later your harness is spending half a day and countless tokens doing simple tasks because it has to navigate the mess of a codebase.
That problem is much less of an issue at the Opus level and I can trust Opus to clean up better when directed to do so. It’s those differences that compound.
But if I’m doing a throwaway task in a sandbox that just needs to work for a day? I’ll throw it to the open models and see what they can do. They are interesting to play with.