121–130 of 147 posts
I am trying to estimate if my reaction to seeing GPT-5.6 Sol last on that list is reasonable or or mostly emotional and find that I have no way of telling.
Any bench that puts GLM 5.3 ahead of 5.6 Sol is highly sus. They've been my two daily drivers since release, and I like GLM 5.3, but it's definitely not better than Sol, it's more ~Terra, while being significantly slower.
I am trying to estimate if my reaction to seeing GPT-5.6 Sol last on that list is reasonable or or mostly emotional and find that I have no way of telling.
Sol failing mostly on “unverified assumptions” and rarely hitting “integration errors” seems about right to me. I think Sol is second only to Astra (and miles ahead of even Fable) in architecting & engineering the right implementation — but only if you are extremely specific and provide tight guidelines and guardrails. If you give it a one-liner… you’re going to have a bad (SHA-256-hash-verified) time.
Some things in this seem reasonable, but others just don’t make sense and there’s crucial details missing (like reasoning levels and what harness was used). For example, I found Kimi K3 to use more tokens than some other models, which caused it to cost twice as much purely because of the token volume. This experience lines up with ArtificialAnalysis’s benchmarks, but not these. There’s a number of other comparisons h…
I'd love to see these: - DeepSeek V4.1 Flash - Kimi K3 - GLM 5.3 (and flash) - hy4-preview - Grok 4.6 All of these can be acessed using a $10/mo OpenCode Go subscription.
Dreadful color-coding on the output tokens table
what if the production code base was made by mostly by Anthropic models?
So TL;DR benchmarking in a completely non-reproducible manner ? "Model X performed great, but we can't possibly tell you anything about the code it was looking at apart from it was a large code base from an unknown company". So basically pinky-promise benchmarking ? I'm not sure I follow the value here ?
The fact that gemini 3.8 flash is so high up there just tells you this is an awful benchmark. Try and use gemini 3.8 yourself for any real world work and you'll see it's terrible. It'll just go in circles reading the same file 20 times for no reason making hundreds of tool calls for a simple change. EDIT: I was using gemini cli... it's not a harness issue lol
I built exactly this using my own codebases. The setup isn’t that complex; split the git history to just before the change, sandbox the agent with everything they will need at that commit and lightly modify rules so they don’t go searching outside the box. Then they get the same prompt (usually the ticket that began the work) and are graded against the accepted PR. The thing that takes the most time is finding the ex…
hey this seems really interesting - what prompted you to test multiple agents on your codebase?
I rebuilt my entire workflow around agents so the unease that the frontiers would change something (access, pricing, availability) and lock me out of that were high. Also why I spent way too much on hardware (at least that can be deducted). Now the whole stack could run in my house and I feel much better about the situation.
Once I got the testing going though it is worth it for its own pursuit. Building processes around the dev process and trying to get the best outcomes is at least as fun to me as actually delivering client code. For the first time in my tech career I feel like I’m in a place with no maps. No one has done my experiments yet. I have a custom quant of K3 at Q5 that lets me get 10 tok/s on a CPU inference box (admittedly you need a 72GB Blackwell also). As far as I can tell no one else has done this. It’s such an exciting time!
> Each task comes from a private production codebase that we licensed from a real-world company How does that work?