Gemini 4 Argon:
91.9% on Vibe Code Bench.
55% on FrontierSWE v2.
Different benchmarks, different tasks.
But they highlight something builders shouldn't ignore.
Getting AI to generate an impressive app is one challenge.
Getting it to maintain complex software is another.
@StevBuilds Yes, but be truthful to yourself, donβt let it get into your head.
Thereβs what we call βsee finishβ in pidgin it means people no longer regard you because they have seen what youβre made of.
Therefore, package yourself well, make person no see you finish.
Itβs no longer news that most products have now embedded AI functionality into their products workflow. However, I donβt see those AI features in most fintech companies in Nigeria. I mean features that are customer facing.
Gemini 4 Argon supports a 1 million-token context window.
Impressive.
But look at its GraphWalks results:
Up to 128K tokens: 99.7%
256Kβ1M tokens: 84.2%
A model's ability to accept information isn't the same as its ability to use it reliably.
Context size β comprehension.
@andreigec I had a conversation with one of my colleague and he was talking about how impressive AI is and how it has come to make things simpler, report that would take 3 days to product took 20 minutes.
@axhiii88547 How do you have a great product without any user? It starts with validation, if the problem is worth solving then you should already have a few users waiting to for launch.
Gemini 4 Argon is #1 on the Vals Index.
But on MedScribe, it ranks #15.
Same model. Different benchmarks.
This is why picking an AI model based on a general leaderboard can be a costly mistake.
The best model overall isn't necessarily the best model for your product.
@niqudrift Let me speak for my self, X monetization means i have at least 600 followers, its easy to post an ad onmy page and get people connect with my product.