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Gemini 3.1 Pro

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Re: Gemini 3.1 Pro

#771

Post by exabrial »

arcfour wrote: post #744
exabrial wrote: post #734

You know what would slay right now? A native app. Not another piece of Electron bloatware, a regular, efficient, fast, snappy, native, app. One that connects to my MCP severs and has local filesystem tools. Anthropic might fall behind Google/OpenAI eventually, but their Desktop App + MCP/Connectors is unbelievably useful to get real work done.

I haven't used Anthropic's desktop app in months since I don't have access to a Mac anymore, but when I did...it was just an electron app? Did something change?

No, sadly. I wish it were native. Its _terrible_.

Re: Gemini 3.1 Pro

#772

Post by jswny »

bluegatty wrote: post #654
rhubarbtree wrote: post #642

Earlier quoted context omitted.

Google are stuck because they have to compete with OpenAI. If they don’t, they face an existential threat to their advertising business. But then they leave the door open for Anthropic on coding, enterprise and agentic workflows. Sensibly, that’s what they seem to be doing. That said Gemini is noticeably worse than ChatGPT (it’s quite erratic) and Anthropic’s work on coding / reasoning seems to be filtering back to i…

Yup, you got it. It's a weird situation for sure. You know what's also weird: Gem3 'Pro' is pretty dumb. OAI has 'thinking levels' which work pretty well, it's nice to have the 'super duper' button - but also - they have the 'Pro' product which is another model altogether and thinks for 20 min. It's different than 'Research'. OAI Pro (+ maybe Spark) is the only reason I have OAI sub. Neither Anthropic nor Google seem…

Can you explain what’s so different about pro?

I’ve used everything frontier model and had Pro a while ago but it seemed to just be the same models served faster at the time.

Re: Gemini 3.1 Pro

#774

Post by spyckie2 »

ethbr1 wrote: post #758
spyckie2 wrote: post #740

Earlier quoted context omitted.

I mean their ads business just broke $80b per quarter, not sure where this idea is coming from...

Google hasn't seen its legacy ad revenue start to dent until products with built-in agents start to see mass adoption. Writing is on the wall that orders of magnitude fewer people will be going to google.com or using an interactive Google search in the next 5 years though.

> Writing is on the wall that orders of magnitude fewer people will be going to [product] or using [product] in the next 5 years though.

counterpoint: which service or product is immune to this statement?

Re: Gemini 3.1 Pro

#775

Post by [deleted] »

lukebechtel wrote: post #745
sdeiley wrote: post #548

People underrate Google's cost effectiveness so much. Half price of Opus. HALF. Think about ANY other product and what you'd expect from the competition thats half the price. Yet people here act like Gemini is dead weight ____ Update: 3.1 was 40% of the cost to run AA index vs Opus Thinking AND SONNET, beat Opus, and still 30% faster for output speed. https://artificialanalysis.ai/?speed=intelligence-vs-speed&m...

sonnet 4.6 is a third, and equivalent to opus 4.5, which is enough for me usually :) EDIT: Gemini does have 1m context for "free" though so that's great.

[deleted]

Re: Gemini 3.1 Pro

#776

Post by kovek »

1024core wrote: post #350
kovek wrote: post #344

Earlier quoted context omitted.

I think that semantically this question is too similar to the car wash one. Changing subjects from car to elephant and car wash to creek does not change the fact that they are subjects. The embeddings will be similar in that dimension.

I understand. But isn't it a sign of "smarts" that one can generalize from analoguous tasks?

Every word and every hierarchy of words in natural language is understand by LLMs as embeddings (vectors).

Each vector has many many dimensions, and when we train the LLMs, their internal understanding of those vectors sees all sorts of dimensions. A simple way to visualize this is a word's vector being which would all mean a certain value at that dimension. In this example say the dimensions are . In this case, our example LLM could have learned that the example I gave is . The vector's undergo some transformation so every dimension is not that discretely clear cut.

In this case, elephant and car both semantically look very similar to vehicles. They basically would have most vectors very similar.

See this article. It shows that once you train an LLM, and you assign an embedding vector for each token, then you can see how the LLM can distinguish the difference between king and queen: man and woman.

https://informatics.ed.ac.uk/news-events/news/news-archive/k...

Re: Gemini 3.1 Pro

#777

Post by datahack »

bluegatty wrote: post #589
spankalee wrote: post #199

I hope this works better than 3.0 Pro I'm a former Googler and know some people near the team, so I mildly root for them to at least do well, but Gemini is consistently the most frustrating model I've used for development. It's stunningly good at reasoning, design, and generating the raw code, but it just falls over a lot when actually trying to get things done, especially compared to Claude Opus. Within VS Code Copi…

Yes, this is very true and it speaks strongly to this wayward notion of 'models' - it depends so much on the tuning, the harness, the tools. I think it speaks to the broader notion of AGI as well. Claude is definitively trained on the process of coding not just the code, that much is clear. Codex has the same limitation but not quite as bad. This may be a result of Anthropic using 'user cues' with respect to what are…

I know this is only a partial answer, but I feel like Google is once again trying to build a product based on internal priorities, existing business protectionism, and internal business goals, rather than building a product that is listening actively to real use feedback as the primary priority.

It is the company’s constant kryptonite.

They seem to be, from my third part perspective, repeating the same ol’, same ol’ pattern. It is the “wave lesson” all over again.

Anthropic meanwhile is giving people what they want. They are really listening. And it’s working.

Re: Gemini 3.1 Pro

#778

Post by adonese »

minimaxir wrote: post #23

Price is unchanged from Gemini 3 Pro: $2/M input, $12/M output. https://ai.google.dev/gemini-api/docs/pricing Knowledge cutoff is unchanged at Jan 2025. Gemini 3.1 Pro supports "medium" thinking where Gemini 3 did not: https://ai.google.dev/gemini-api/docs/gemini-3 Compare to Opus 4.6's $5/M input, $25/M output. If Gemini 3.1 Pro does indeed have similar performance, the price difference is notable.

The only thing i don't like about gemini models (gemini cli) is that there's no transparency on which model I'm using. I can start with pro and it can be downgraded sometimes even to gemini 2.5 flash lite.

Re: Gemini 3.1 Pro

#779

Post by raincole »

sdeiley wrote: post #548

People underrate Google's cost effectiveness so much. Half price of Opus. HALF. Think about ANY other product and what you'd expect from the competition thats half the price. Yet people here act like Gemini is dead weight ____ Update: 3.1 was 40% of the cost to run AA index vs Opus Thinking AND SONNET, beat Opus, and still 30% faster for output speed. https://artificialanalysis.ai/?speed=intelligence-vs-speed&m...

^ This is a weird Gemini shilling account (check their comment history) but I still want to point how ridiculous this statement is:

> Think about ANY other product and what you'd expect from the competition thats half the price.

Car, fashion, jewelry, earphone, furniture, keyboard, mouse, restaurant, house,...

Re: Gemini 3.1 Pro

#780

Post by ttul »

AIorNot wrote: post #710
ttul wrote: post #709

What I’m noticing, overall: I’ve never cut so much code in my life. I’ve become a coding monster with one of those dark green GitHub profiles ever since 5.3-Codex gave me the confidence to load in a ridiculous number of tasks every day and let it rip. I have about three coding tasks going at once and in another window, Claude Cowork is ripping through PowerPoints and getting back to lawyers. This tech is not going to…

Yeah see this article I think it was spot on https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies...

“Some described sending a “quick last prompt” right before leaving their desk so that the AI could work while they stepped away.”

This, I can relate to. Also: I feel like I need a second monitor.

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