Comment by jstummbillig
6 hours ago
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.
6 hours ago
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 had exactly the same thoughts. I often have similar thoughts on other benchmark sites, where supposed performance is way off base from my experience.
I’m not sure what the methodology of these are, but they certainly don’t match what I experience. Maybe I need to look deeper for relevant benchmarks.
Why would you get emotional over a model? They got you that good?
There's an issue with GPT-5.6 Sol where it sometimes starts mixing thinking with output and stops working[1]. Once it starts doing that, the session is essentially cooked and you need to do a bit of gymnastics if you want to recover it.
This happens to me more commonly in large projects (>100k LOC) and in those projects it seems to happen every few sessions. I feel this specific benchmark would be impacted by this more than the smaller contrived benchmarks.
[1]: https://github.com/openai/codex/issues/37524
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I do think it’s the wizard not the wand at this point given a decent model. These benchmarks don’t have the wizard.
Otherwise I wouldn’t see others in the exact same codebase struggle and underutilize agents while others thrive using the exact same ones.
In other words, we're still in the era of centaur chess.
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.
SHA-256-hash-verified sealed package artifact with automatic reconciliation system p95<0.5ms
>> 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.
To me, having to give extremely specific instructions and provide tight guidelines and guardrails defeats the purpose of agentic coding agents almost completely. At that point I might as well do the task myself.
With Fable I can start with a general ask like "I'm trying to do X, can you investigate and tell me what the shape would look like" and have it poke around and think, ask me questions with single-choice or multiple-choice answers, then break the task into small chunks, each of which becomes a ticket.
With Astra, it's like pulling teeth. It often does not understand what I'm trying to do, takes things literally, does not go above and beyond (i.e. infer intent), and stops way too short of the actual goal. I have to constantly prod it and it's frankly exhausting.
I agree somewhat with the way the agents behave but feel the opposite reaction. With Fable, I get exhausted because it's always dumping out paragraphs of text that explain one approach but have some secret gotcha thrown out in the last two sentences. Then I have to pause and consider the caveat and if it matters and it happens every single time Fable responds and that constantly needing to make a decision that could radically change the approach gives me decision fatigue. I much prefer how much more decisive Astra can be.
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I found 5.6 Sol to be extremely underwhelming.