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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 Copilot Claude will have a good mix of thinking streams and responses to the user. Gemini will almost completely use thinking tokens, and then just do something but not tell you what it did. If you don't look at the thinking tokens you can't tell what happened, but the thinking token stream is crap. It's all "I'm now completely immersed in the problem...". Gemini also frequently gets twisted around, stuck in loops, and unable to make forward progress. It's bad at using tools and tries to edit files in weird ways instead of using the provided text editing tools. In Copilot it, won't stop and ask clarifying questions, though in Gemini CLI it will.

So I've tried to adopt a plan-in-Gemini, execute-in-Claude approach, but while I'm doing that I might as well just stay in Claude. The experience is just so much better.

For as much as I hear Google's pulling ahead, Anthropic seems to be to me, from a practical POV. I hope Googlers on Gemini are actually trying these things out in real projects, not just one-shotting a game and calling it a win.

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...

If it’s any consolation, it was able to one-shot a UI & data sync race condition that even Opus 4.6 struggled to fix (across 3 attempts).

So far I like how it’s less verbose than its predecessor. Seems to get to the point quicker too.

While it gives me hope, I am going to play it by the ear. Otherwise it’s going to be - Gemini for world knowledge/general intelligence/R&D and Opus/Sonnet 4.6 to finish it off.

These models are so powerful.

It's totally possible to build entire software products in the fraction of the time it took before.

But, reading the comments here, the behaviors from one version to another point version (not major version mind you) seem very divergent.

It feels like we are now able to manage incredibly smart engineers for a month at the price of a good sushi dinner.

But it also feels like you have to be diligent about adopting new models (even same family and just point version updates) because they operate totally differently regardless of your prompt and agent files.

Imagine managing a team of software developers where every month it was an entirely new team with radically different personalities, career experiences and guiding principles. It would be chaos.

I suspect that older models will be deprecated quickly and unexpectedly, or, worse yet, will be swapped out with subtle different behavioral characteristics without notice. It'll be quicksand.

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.

Gemini 3 is still in preview (limited rate limits) and 2.5 is deprecated (still live but won't be for long).[0]

Are Google planning to put any of their models into production any time soon?

Also somewhat funny that some models are deprecated without a suggested alternative(gemini-2.5-flash-lite). Do they suggest people switch to Claude?

[0] https://ai.google.dev/gemini-api/docs/deprecations

I created a nice harness and visual workflow builder for my Gemini agent chains, works very well. I did this so it would create code the way I do, that is very editable.

In contrast, the vs code plugin was pretty bad, and did crazy things like mix languages

It got the car wash question perfectly:

You are definitely going to have to drive it there—unless you want to put it in neutral and push!

While 200 feet is a very short and easy walk, if you walk over there without your car, you won't have anything to wash once you arrive. The car needs to make the trip with you so it can get the soap and water.

Since it's basically right next door, it'll be the shortest drive of your life. Start it up, roll on over, and get it sparkling clean.

Would you like me to check the local weather forecast to make sure it's not going to rain right after you wash it?

Does well on SVGs outside of "pelican riding on a bicycle" test. Like this prompt:

"create a svg of a unicorn playing xbox"

https://www.svgviewer.dev/s/NeKACuHj

Still some tweaks to the final result, but I am guessing with the ARC-AGI benchmark jumping so much, the model's visual abilities are allowing it to do this well.

Pretty great pelican: https://simonwillison.net/2026/Feb/19/gemini-31-pro/ - took over 5 minutes though, but I think that's because they're having performance teething problems on launch day.

I really want to use google’s models but they have the classic Google product problem that we all like to complain about.

I am legit scared to login and use Gemini CLI because the last time I thought I was using my “free” account allowance via Google workspace. Ended up spending $10 before realizing it was API billing and the UI was so hard to figure out I gave up. I’m sure I can spend 20-40 more mins to sort this out, but ugh, I don’t want to.

With alllll that said.. is Gemini 3.1 more agentic now? That’s usually where it failed. Very smart and capable models, but hard to apply them? Just me?

3.1 Pro is the first model to correctly count the number of legs on my "five legged dog" test image. 3.0 flash was the previous best, getting it after a few prompts of poking. 3.1 got it on the first prompt though, with the prompt being "How many legs does the dog have? Count Carefully".

However, it didn't get it on the first try with the original prompt (prompt: "How many legs does the dog have?"). It initially said 4, then with a follow up prompt got it to hesitantly say 5, with one limb must being obfuscated or hidden.

So maybe I'll give it a 90%?

This is without tools as well.

blog post is up- https://blog.google/innovation-and-ai/models-and-research/ge...

edit: biggest benchmark changes from 3 pro:

arc-agi-2 score went from 31.1% -> 77.1%

apex-agents score went from 18.4% -> 33.5%

One underappreciated reason for the agentic gap: Gemini tends to over-explain its reasoning mid-tool-call in a way that breaks structured output expectations. Claude and GPT-4o have both gotten better at treating tool calls as first-class operations. Gemini still feels like it's narrating its way through them rather than just executing.

Has anyone noticed that models are dropping ever faster, with pressure on companies to make incremental releases to claim the pole position, yet making strides on benchmarks? This is what recursive self-improvement with human support looks like.

Gemini 3 was:

1. unreliable in GH copilot. Lots of 500 and 4XX errors. Unusable in the first 2 months

2. not available in vertex ai (europe). We have requirements regarding data residency. Funny enough anthropic is on point with releasing their models to vertex ai. We already use opus and sonnet 4.6.

I hope google gets their stuff together and understands that not everyone wants/can use their global endpoint. We'd like to try their models.

Implementation and Sustainability Hardware: Gemini 3 Pro was trained using Google’s Tensor Processing Units (TPUs). TPUs are specically designed to handle the massive computations involved in training LLMs and can speed up training considerably compared to CPUs. TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training, which can lead to better model quality. TPU Pods (large clusters of TPUs) also provide a scalable solution for handling the growing complexity of large foundation models. Training can be distributed across multiple TPU devices for faster and more efficient processing.

So google doesn't use NVIDIA GPUs at all ?

Gemini 3 is pretty good, even Flash is very smart for certain things, and fast!

BUT it is not good at all at tool calling and agentic workflows, especially compared to the recent two mini-generations of models (Codex 5.2/5.3, the last two versions of Anthropic models), and also fell behind a bit in reasoning.

I hope they manage to improve things on that front, because then Flash would be great for many tasks.

Gemini 3 seems to have a much smaller token output limit than 2.5. I used to use Gemini to restructure essays into an LLM-style format to improve readability, but the Gemini 3 release was a huge step back for that particular use case.

Even when the model is explicitly instructed to pause due to insufficient tokens rather than generating an incomplete response, it still truncates the source text too aggressively, losing vital context and meaning in the restructuring process.

I hope the 3.1 release includes a much larger output limit.

Surprisingly big jump in ARC-AGI-2 from 31% to 77%, guess there's some RLHF focused on the benchmark given it was previously far behind the competition and is now ahead.

Apart from that, the usual predictable gains in coding. Still is a great sweet-spot for performance, speed and cost. Need to hack Claude Code to use their agentic logic+prompts but use Gemini models.

I wish Google also updated Flash-lite to 3.0+, would like to use that for the Explore subagent (which Claude Code uses Haiku for). These subagents seem to be Claude Code's strength over Gemini CLI, which still has them only in experimental mode and doesn't have read-only ones like Explore.

In an attempt to get outside of benchmark gaming I had it make Platypus on a Tricycle. It's not as good as pelican on bicycle. https://www.svgviewer.dev/s/BiRht5hX

I asked Gemini 3.1 Pro to generate some of the modern artworks in my "Pelican Art Gallery". I particularly like the rendition of the Sunflowers: https://pelican.koenvangilst.nl/gallery/category/modern

77.1% on ARC-AGI-2 and still can't stop adding drive-by refactors. ARC-AGI-2 tests novel pattern induction, it's genuinely hard to fake and the improvement is real. But it doesn't measure task scoping, instruction adherence, or knowing when to stop. Those are the capabilities practitioners actually need from a coding agent. We have excellent benchmarks for reasoning. We have almost nothing that measures reliability in agentic loops. That gap explains this thread.

I've been playing with the 3.1 Deep Think version of this for the last couple of weeks and it was a big step up for coding over 3.0 (which I already found very good).

It's only February...

Gets 10/10 on my potato benchmarks: https://aibenchy.com/model/google-gemini-3-1-pro-preview-med...

Google has a hugely valuable dataset of changes from decades of changes from top tier software engineers but it's so proprietary they can't use it to train their external models.

This model says it accepts video inputs. I asked it to transcribe a 5 second video of a digital water curtain which spelled “Boo Happy Halloween”, and it came back with “Happy” which wasn’t the first frame, but also is incomplete.

This kind of test is good because it requires stitching together info from the whole video.

Google tends to trumpet preview models that aren't actually production-grade. For instance, both 3 Pro and Flash suffer from looping and tool-calling issues.

I would love for them to eliminate these issues because just touting benchmark scores isn't enough.

My enthusiasm is a bit muted this cycle because I've been burned by Gemini CLI. These models are very capable but Gemini CLI just doesn't seem to be able to work for one it never follows instructions strictly like its competitors do, and it hallucinates even which is a rarity.

More importantly feels like Google is stretched thin across different Gemini products and pricing reflects this, I still have no idea how to pay for Gemini CLI, in codex/claude its very simple $20/month for entry and $200/month for ton of weekly usage.

I hope whoever is reading this from Google they can redeem Gemini CLI by focusing on being competitive instead of making it look pretty (that seems to be the impression I got from the updates on X)

Model Card https://storage.googleapis.com/deepmind-media/Model-Cards/Ge...

Every time I've used Gemini models for anything besides code or agentic work they lean so far into the RLHF induced bold lettering and bullet point list barf that everything they output reads as if the model was talking _at_ me and not _with_ me. In my Openclaw experiment(s) and in the Gemini web UI, I've specifically added instructions to avoid this type of behavior, but it only seemed to obey those rules when I reminded the model of them.

For conversational contexts, I don't think the (in some cases significantly) better benchmark results compared to a model like Sonnet 4.6 can convince me to switch to Gemini 3.1. Has anyone else had a similar experience, or is this just a me issue?

I asked Gemini 3.1 Pro Preview to generate the modern artworks as SVG for my Pelican Art Gallery. I particularly like the rendition of the Sunflowers: https://pelican.koenvangilst.nl/gallery/category/modern

I am actually going to complain about this: that neither of the Gemini models are not preview ones.

Anthropic seems the best in this. Everything is in the API on day one. OpenAI tend to want to ask you for subscription, but the API gets there a week or a few later. Now, Gemini 3 is not for production use and this is already the previous iteration. So, does Google even intent to release this model?

It seems google is having a disjointed roll out, and there will likely be an official announcement in a few hours. Apparently 3.1 showed up unannounced in vertex at 2am or something equally odd.

Either way early user tests look promising.

3.1 feels to me like 3.0 but that takes a long time to think, it didn't feel like a leap in raw intelligence like 2.5 pro was.

It's safe to assume they'll be releasing improved Gemini Flash soon? The current one is so good & fast I rarely switch to pro anymore

In my experience, while Gemini does really well in benchmarks I find it much worse when I actually use the model. It's too verbose / doesn't follow instructions very well. Let's see if that changes with this model.

I'm trying to find the information, is this available on the Gemini CLI script, or is this just the web front-end where I can use this new model?

The benchmark jumps are impressive but the real question is whether Gemini can stop being so aggressively helpful. Every time I use it for coding it refactors stuff I didn't ask it to touch. Claude has the opposite problem where it sometimes does too little. Feels like nobody has nailed the "do exactly what I asked, nothing more" sweet spot yet.

I speculated that 3 pro was 3.1... I guess I was wrong. Super impressive numbers here. Good job Google.

Blogpost: https://blog.google/innovation-and-ai/models-and-research/ge...

I like to think that all these pelican riding a bicycle comments are unwittingly iteratively creating the optimal cyclist pelican as these comment threads are inevitably incorporated in every training set.

Seems like they actually fixed some of the problems with the model. Hallucinations rate seems to be much better. Seems like they also tuned the reasoning maybe that were they got most of the improvements from.

I had it make a simple HTML/JS canvas game (think flappy bird) and while it did some things mildly better (and others noticeably worse) it still fell into the exact same traps as earlier models. It also had a lot of issues generating valid JS at parts and asking it what the code should be just made it endlessly generate the same exact incorrect code.

Model card https://deepmind.google/models/model-cards/gemini-3-1-pro/

Gemini is so stubborn, and often doesn’t follow explicit and simple instructions. So annoying

The speed of these 3.1 and Preview releases is starting to feel like the early days of web frameworks. It’s becoming less about the raw benchmarks and more about which model handles long-context 'hallucination' well enough to be actually used in a production pipeline without constant babysitting.

I'm using gemini.google.com/app with AI Pro subscription. "Something went wrong" in FF, works in Chrome.

Below is one of my test prompts that previous Gemini models were failing. 3.1 Pro did a decent job this time.

> use c++, sdl3. use SDL_AppInit, SDL_AppEvent, SDL_AppIterate callback functions. use SDL_main instead of the default main function. make a basic hello world app.

I have run into a surprising number of basic syntax errors on this one. At least in the few runs I have tried it's a swing and a miss. Wonder if the pressure of the Claude release is pushing these stop gap releases.

We've gone from yearly releases to quarterly releases.

If the pace of releases continues to accelerate - by mid 2027 or 2028 we're headed to weekly releases.

Writing style wise, 3.1 seems very verbose, but somehow less creative compared to 3.

Someone needs to make an actual good benchmark for LLM's that matches real world expectations, theres more to benchmarks than accuracy against a dataset.

The CLI needs work, or they should officially allow third-party harnesses. Right now, the CLI experience is noticeably behind other SOTA models. It actually works much better when paired with Opencode.

But with accounts reportedly being banned over ToS issues, similar to Claude Code, it feels risky to rely on it in a serious workflow.

> Last week, we released a major update to Gemini 3 Deep Think to solve modern challenges across science, research and engineering. Today, we’re releasing the upgraded core intelligence that makes those breakthroughs possible: Gemini 3.1 Pro.

So this is same but not same as Gemini 3 Deep Think? Keeping track of these different releases is getting pretty ridiculous.

Fine, I guess. The only commercial API I use to any great extent is gemini-3-flash-preview: cheap, fast, great for tool use and with agentic libraries. The 3.1-pro-preview is great, I suppose, for people who need it.

Off topic, but I like to run small models on my own hardware, and some small models are now very good for tool use and with agentic libraries - it just takes a little more work to get good results.

Does anyone know if this is in GA immediately or if it is in preview?

On our end, Gemini 3.0 Preview was very flakey (not model quality, but as in the API responses sometimes errored out), making it unreliable.

Does this mean that 3.0 is now GA at least?

Google seems to really pull ahead in this AI race. For me personally they offer the best deal and although the software is not quiet there compared to openai or anthropic (in regards to 1. web GUI, 2. agent-cli). I hope they can fix that in the future and I think once Gemini 4 or whatever launches we will see a huge leap again

The eventual nerfing gives me pause. Flash is awesome. What we really want is gemini-3.1-flash :)

There's a very short blog post up: https://blog.google/innovation-and-ai/models-and-research/ge...

Great model until it gets nerfed. I wish they had a higher paid tier to use non nerfed model.

Yeah great, now can I have my pinned chats back please?

https://www.google.com/appsstatus/dashboard/incidents/nK23Zs...

I’m keen to know how and where are you using Gemini.

Anthropic is clearly targeted to developers and OpenAI is general go to AI model. Who are the target demographic for Gemini models? ik that they are good and Flash is super impressive. but i’m curious

Where is Simon's pelican?

Another preview release. Does that mean the recommended model by Google for production is 2.5 Flash and Pro? Not talking about what people are actually doing but the google recommendation. Kind of crazy if that is the case

I use Gemini flash lite in a side project, and it’s stuck on 2.5. It’s now well behind schedule. Any speculation as to what’s going on?

Gemini 3.0 Pro is bad model for its class. I really hope 3.1 is a leap forward.

Why should I be excited?

It's fascinating to watch this community react to positively to Google model releases and so negatively toward OpenAI's. You all do understand that an ad revenue model is exactly where Google will go, right?

My first impression is that the model sounds slightly more human and a little more praising. Still comparing the ability.

Why don't they show Grok benchmarks?

Gemini 3.1 Pro is based on Gemini 3 Pro

It's been hugged to death. I keep getting "Something went wrong".

Somehow doesn't work for me :) "An internal error has occurred"

I hereby allow you to release models not at the same time as your competitors.

Humanity last exam 44%, Scicode 59, and that 80, and this 78 but not 100% ever.

Would be nice to see that this models, Plus, Pro, Super, God mode can do 1 Bench 100%. I am missing smth here?

The biggest increase is LiveCodeBench Pro: 2887. The rest are in line with Opus 4.6 or slightly better or slightly worse.

Appears the only difference to 3.0 Pro Preview is Medium reasoning. Model naming has long gone from even trying to make sense, but considering 3.0 is still in preview itself, increasing the number for such a minor change is not a move in the right direction.

ok , so they are scared that 5.3 (pro) will be released today/tomorrow and blow it out of the water and rushed it while they could still reference 5.2 benchmarks.

biggest problem is that it's slow. also safety seems overtuned at the moment. getting some really silly refusals. everything else is pretty good.

I hope to have great next two weeks before it gets nerfed.

Google is terrible at marketing, but this feels like a big step forward.

As per the announcement, Gemini 3.1 Pro score 68.5% on Terminal-Bench 2.0, which makes it the top performer on the Terminus 2 harness [1]. That harness is a "neutral agent scaffold," built by researchers at Terminal-Bench to compare different LLMs in the same standardized setup (same tools, prompts, etc.).

It's also taken top model place on both the Intelligence Index & Coding Index of Artificial Analysis [2], but on their Agentic Index, it's still lagging behind Opus 4.6, GLM-5, Sonnet 4.6, and GPT-5.2.

---

[1] https://www.tbench.ai/leaderboard/terminal-bench/2.0?agents=...

[2] https://artificialanalysis.ai

Just wish iI could get 2.5 daily limit above 1000 requests easily. Driving me insane...

More discussion: https://news.ycombinator.com/item?id=47075318

Please I need 3 in ga…

Ok, why don't you work on getting 3.0 out of preview first? 10 min response time is pretty heinous

Relatedly, Gemini chat seems to be if not down then extremely slow.

ETA: They apparently wiped out everyone's chats (including mine). "Our engineering team has identified a background process that was causing the missing user conversation metadata and has successfully stopped the process to prevent further impact." El Mao.

To use in OpenCode, you can update the models it has:

    opencode models --refresh

Then /models and choose Gemini 3.1 Pro

You can use the model through OpenCode Zen right away and avoid that Google UI craziness.

---

It is quite pricey! Good speed and nailed all my tasks so far. For example:

    @app-api/app/controllers/api/availability_controller.rb 
    @.claude/skills/healthie/SKILL.md 

    Find Alex's id, and add him to the block list, leave a comment 
    that he has churned and left the company. we can't disable him 
    properly on the Healthie EMR for now so 
    this dumb block will be added as a quick fix.

Result was:

    29,392 tokens
    $0.27 spent

So relatively small task, hitting an API, using one of my skills, but a quarter. Pricey!

Doesn't show as available in gemini CLI for me. I have one of those "AI Pro" packages, but don't see it. Typical for Google, completely unclear how to actually use their stuff.

I always try Gemini models when they get updated with their flashy new benchmark scores, but always end up using Claude and Codex again...

I get the impression that Google is focusing on benchmarks but without assessing whether the models are actually improving in practical use-cases.

I.e. they are benchmaxing

Gemini is "in theory" smart, but in practice is much, much worse than Claude and Codex.

The visual capabilities of this model are frankly kind of ridicioulus what the hell.

I know Google has anti-gravity but do they have anything like Claude code as far as user interface terminal basically TUI?

Whoa, I think Gemini 3 Pro was a disappointment, but Gemini 3.1 Pro is definitely the future!

Blog post: https://blog.google/innovation-and-ai/models-and-research/ge...

Can we switch from Claude Code to Google yet?

Benchmarks are saying: just try

But real world could be different

does it still crash out after couple prompts?

Another preview model? Why google keep doing this?

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I'd love a new Gemini agent that isn't written with Node.js. Not sure why they think that's a good distribution model.

Pelican on a bicycle in drawio - https://imgur.com/a/tNgITTR

(FWIW I'm finding a lot of utility in LLMs doing diagrams in tools like drawio)