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Gemini 3.1 Pro

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Re: Gemini 3.1 Pro

#671

Post by bluegatty »

visarga wrote: post #664
bluegatty wrote: post #654

Earlier quoted context omitted.

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…

If you want that level of research I suggest you ask the model to draft a markdown plan with "[ ]" gates for todo items, and plan it in as many steps as needed. Then ask another LLM to review the plan, judge it. In the end use the plan as the execution state tracker, the model solves one by one the checkboxes. Using this method I could recreate "deep research" mode on a private collection of documents in a few minute…

OAI Pro is not a 'research' tool in that sense, and it's definitely different than the 'deep research' options avail on most platforms, as I indicated.

It's a different kind of solution altogether.

I suggest trying it.

Re: Gemini 3.1 Pro

#672

Post by spankalee »

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…

> Claude is definitively trained on the process of coding not just the code

This definitely feels like it.

It's hard to really judge, but Gemini feels like it might actually write better code, but the _process_ is so bad that it doesn't matter. At first I thought it was bad integration by the GitHub Copilot, but I see it elsewhere now.

Re: Gemini 3.1 Pro

#673

Post by bluegatty »

embedding-shape wrote: post #657
bluegatty wrote: post #652

Earlier quoted context omitted.

Your intuition is exactly correct - it's not just 'tone' it's 'deeper than that'. Codex is a 'poor communicator' - which matters surprisingly a lot in these things. It's overly verbose, it often misses the point - but - it is slightly stronger in some areas. Also - Codex now has 'Spark' which is on Cerebras, it's wildly fast - and this absolutely changes 'workflow' fundamentally. With 'wait-thinking' - you an have 3-…

Spark is fun and cool, but it isn't some revolution. It's a different workflow, but not suitable for everything that you're use GPT5.2 for with thinking set to high, for example, it's way more dumb and makes more mistakes, while 5.2 will carefully thread through a large codebase and spend 40 minutes just to validate the change actually didn't break anything, as long as you provide prompts for it. Spark on the other h…

Spark is the 'same model and harness' but on Cerebras.

Your intuition may be deceiving you, maybe assuming it's a speed/quality trade-off, it's not.

It's just faster hardware.

No IQ tradeoff.

If you toy around with Cerebras directly, you get a feel for it.

Edit: see note below, I'm wrong. Not same model.

Re: Gemini 3.1 Pro

#674

Post by KoolKat23 »

WarmWash wrote: post #626
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...

Gemini is the most paradoxical model because it benchmarks great even in private benchmarks done by regular people, Deep Mind is unquestionably full of capable engineers with incredible skill, and personally Gemini has been great for my day job and my coding for fun (not for profit) endeavors. Switching between it and 4.6 in antigravity and I don't see much of a difference, they both do what I ask. But man, people ar…

Outside of code, Gemini is really really good.

Re: Gemini 3.1 Pro

#675

Post by not_ai »

drivebyhooting wrote: post #613
HardCodedBias wrote: post #487

Earlier quoted context omitted.

LOL come on man. Let's give it a couple of days since no one believes anything from benchmarks, especially from the Gemini team (or Meta). If we see on HN that people are willing switching their coding environment, we'll know "hot damn they cooked" otherwise this is another wiff by Google.

You can’t put Gemini and Meta in the same sentence. Llama 4 was DOA, and Meta has given up on frontier models. Internally they’re using Claude.

After spending all that money and firing a bunch of people? Is the new group doing anything at this point?

Re: Gemini 3.1 Pro

#677

Post by lgl »

thallavajhula wrote: post #669

This is great. I am hopeful that Gemini 3.1 Pro would be great. So far, I'm almost always pulled away from Gemini models by Claude. Having used Claude Opus High for a while now, Claude Opus seems to be fantastic at coding. Even Gemini's comparison chart says so. OpenAI's 5.3-codex is by far the weakest (of the 3) for my coding purposes. Claude Opus really shines at explanations and generating code. Gemini is almost g…

> I keep switching among these subscriptions every month to not miss out on any of the offerings for too long; ChatGPT Plus Gemini Pro Claude.

I wonder why many people seem to be doing this instead of just going for a copilot subscription that has access to all those models? Anybody care to share pros and cons?

Re: Gemini 3.1 Pro

#678

Post by jacquesm »

rhubarbtree wrote: post #642
bluegatty wrote: post #589

Earlier quoted context omitted.

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…

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…

Google is scoring one own goal after another by making people working with their own data wonder how much of that data is sent off to be used to train their AI on. Without proof to the contrary I'm going to go with 'everything'.

They should have made all of this opt-in instead of force-feeding it to their audience, which they wrongly believe to be captive.

Re: Gemini 3.1 Pro

#680

Post by small_model »

mikestorrent wrote: post #646
TZubiri wrote: post #612

Earlier quoted context omitted.

You can map tokens to CPU instructions and train a model on that, that's what they do for input images I think. I think the main limitation on the current models is not that cpu instructions aren't cpu instructions (even though they can be with .asm), it's that they are causal, the cpu would need to generate a binary entirely from start to finish sequentially. If we learned something over the last 50 years of program…

Those CPU instructions still need to be making calls out to things, though. Hallucinated source code will reveal its flaws through linters, compiler errors, test suites. A hallucinated binary will not reveal its flaws until it segfaults.

Programs that pass linters, compile and test suites can still segfault. A good test harness that test the binary comprehensively can limit this. The model could be trained to have patterns of efficient assembly it uses rather than source code.
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