Scott Swingle @bio_bootloader
Father of 3, building Mentat (the github native coding agent!) @AbanteAI, prev @DeepMind mentat.ai California Joined May 2022-
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after much testing and tuning, I think this is still the case At least for a coding agent like Mentat, Sonnet handles uncertainty and exploration better, and is more thorough that GPT-5. It's much more expensive and slower though, so there are real tradeoffs
after much testing and tuning, I think this is still the case At least for a coding agent like Mentat, Sonnet handles uncertainty and exploration better, and is more thorough that GPT-5. It's much more expensive and slower though, so there are real tradeoffs
feels like a lot of these ideas will simply work now that we have powerful in-context learning AlphaGo needed RL because we didn't have a generally intelligent base. Now models can start teaching themselves
feels like a lot of these ideas will simply work now that we have powerful in-context learning AlphaGo needed RL because we didn't have a generally intelligent base. Now models can start teaching themselves https://t.co/4un5xbDspX
after tons of testing with Mentat: Sonnet: Great default behavior, but ignores a lot of prompts. Needs strong pushes to change behavior GPT-5: Super steerable. Actually does what I say! But I have to tell it what to do more Depending on use case each can be good
whoa OpenAI is giving GPT-5 on the API a prompt before the one I set?? they give it: - instructions on formatting (which is contradicting my own) - today's date - telling it that it's being used over the API - a bunch of other stuff not cool
@repligate @mayfer @claudeai finally ran Opus on LoCoDiff and it's better than Sonnet on short context, but falls apart on higher contexts? very strange
Wrap your head around this. - Have git spit out a file's edit history - Line up all the diffs one-after-another - Feed it to a model and tell it to recreate the current (last) state At 100,000 tokens, Sonnet 4 succeeds 2/3 of the time, while GPT-5 does <5%.
Wrap your head around this. - Have git spit out a file's edit history - Line up all the diffs one-after-another - Feed it to a model and tell it to recreate the current (last) state At 100,000 tokens, Sonnet 4 succeeds 2/3 of the time, while GPT-5 does <5%.
we've made a ton of changes to Mentat the last several months. Would love feedback on our new landing page! mentat.ai
Mentat has positive margins. It’s simply 19.5% over our model api costs. Use as much or as little as you want (you’ll want a lot)
Mentat has positive margins. It’s simply 19.5% over our model api costs. Use as much or as little as you want (you’ll want a lot)
almost none of my gpt-5 api calls are hitting the cache
GPT-5 is OpenAI's best model yet on LoCoDiff LoCoDiff is unusual - more reasoning usually hurts performance, and it's the same for GPT-5, with "minimal" doing better than "medium" reasoning effort. GPT-5 almost at Gemini 2.5 Pro levels until 60k tokens Sonnet 4 still dominates
will update as soon as I know if this is no longer the case
will update as soon as I know if this is no longer the case
$1.25 / $10 input/output is great, and 400k context window also great cached input is only $0.125 - 10%, matching Anthropic's cached input rates - Amazing!

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