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Comment by seviu | Hacker News Reader

In those rare occasion when I want to use Gemini I just type gemini on my terminal.

Gemini was on life support on my side. I barely get to use it due to its subpar performance in coding, which is to be honest the only use I have of it.

And now I read that they spent 4 to 5 months testing 3.5 internally. Let that sink in. By the time they release the world has moved on. I don’t know who makes decisions at Google regarding AI but it saddens me to see this happening. Google should be up there leading but they are lagging against everybody.

How can I justify dropping 100$ per month, for a coding agent that is half a year behind, knowing that Codex or Kimi is going to do much better?

Stock might be ripping but that’s about it.

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On the other hand I quietly cheer every time they fumble even slightly, in their seemingly inexorable march to becoming our ultimate, terrifying, corporate overlords.

Yes. Every possible pain they have makes me happy when their other hand is slowly destroying Android. Let them suffer.

I get what you're saying about Gemini for coding and it's useful that you mention it.

I wonder though if Google isn't so worried about the viability of their coding AIs and have a longer term view than simply providing coding aids. This might also be indicated by their recent $40B investment in Anthropic, https://www.cnbc.com/2026/04/24/google-to-invest-up-to-40-bi...

...Only time will tell!

I think google has another 'problem': Gemini needs to do a lot more than claude.

They use Gemini for personal assistent to all of their Gmail and co users/customers. They have Google Docs, they have GCP were gemini should support you too.

They also have a lot more languages to support too.

They optimize Gemini for A LOT more than 'just' coding. So its probably a balance act for them. And because they are that rich and have no issues on compute and brain, they can play the long game easily.

If they push their tpu further and continue their build out, they will be able to start training high quality topic optimized models in parallel while everyone else needs the same amount to just train one main model.

What is preventing them from making a coding dedicated model if that's the issue?

Strategic vision.

The common sentiment is that you didn't really want to do that. You expect higher returns from only having a single base model.

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