Gemini 3.8 Flash and 3.8 Flash Cyber | Hacker News Reader

Gemini 3.8 Flash and 3.8 Flash Cyber

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1,160 pointsbratao669 comments

https://deepmind.google/models/model-cards/gemini-3-8-flash/

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The speed combined with the fact that this thing is really good at HTML JavaScript is pretty exciting.

Here's what I got for 1.8 cents and 13 seconds from the prompt "make me a cool thing in html":

https://gisthost.github.io/?6a77bc41a81718c6aaa10d4ab243c59f

Transcript here (it was part of a chat): https://gist.github.com/simonw/b6149a49d327164d67d62c3d12992...

Here's quite an impressive follow-up. I have a tool which knows how to render Markdown documents with embedded SVG content - I use it for the pelican test.

Since this transcript has HTML in it, I decided to upgrade that tool to also render HTML.

I set Gemini 3.8 Flash the task, using my own VERY shonky coding agent tool (llm-coding-agent) - and it did a solid job.

So now you can see the "cool thing in html" rendered within the Markdown document using code that Gemini 3.8 Flash also wrote: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...

Transcript where it built that is here: https://gist.github.com/simonw/3e36b98292dfdc1b3baff158faa74...

Definitely cool.

I noticed it felt a little janky on my PC despite being "60 FPS"...then I noticed the "60 FPS" is hard-coded into the HTML.

Focus on speed and being OK with temporarily being #3/4 in intelligence might be the counterintuitive approach which makes Google win long term (whether accidentally or strategically). Can't wait to try Gemini Pro later this year!

Why do all LLMs do a particle simulation when you ask them this prompt ? Qwen3.6, Qwen3.8 and Ling 3.0 Tiny all did the same thing !

I find Ling 3.0 tiny particularly interesting as it looks really nice for a tiny model with 7.9B total parameters, with only 1.3B parameters activated per token. Here is the result https://coolthing-ling-3-tiny.tiiny.site (sorry for the weird hosting, first I found that worked)

(it cost me almost 0 cents and done in 49 seconds)

https://gist.github.com/simonw/b6149a49d327164d67d62c3d12992...

> Aside from reading identically forwards and backwards down to the letter

No it doesn't.

I saw your username, clicked the link without reading, and was very confused to see a cosmic vortex and not a pelican.

I'd be impressed if eventually training data sets learn who you are (the specific human) and do something like make this galaxy simulator, but every so often when the user moves the cursor around the star field, a small animated SVG of a pelican on a bicycle appears.

Thought processs: "Oh, simonw is asking me to make something cool, I think I know what he really wants..."

it's such a weird split how most AI companies are trying to be the best, but Google really has a different mission statement. they already have users. lots of users. they need to be working on building models they can deploy and use with the most number of people, as they already have the users.

i don't know if Gemini models per se are fully is in line with that purpose, but the results we see keep seeming to be in-line with that split-of-focus.

It's been great even since gemini-3.1-flash-lite, which I heavily use in both complex vertical domain tools calling, plus JS code writing for eval-style dynamic tools. At least in my applications, cost x quality x speed there are simply no alternatives.

> this thing is really good at HTML JavaScript is pretty exciting.

I would hope the people who make one of the most used JS engines in the world are capable of making a model good at JavaScript ;)

Pretty typical "cool HTML toy" LLM output, tbh. The only thing impressive about this is how fast it generated it (13 seconds is wild!), but that's more of testament to Google's infrastructural advantage than to the quality of the model.

For comparison's sake, I tried something similar with a couple other cheap models I've used lately, with the prompt "Impress me. Make something cool in HTML. Ensure that it is mobile friendly." (Added the mobile condition as I was on my phone when I did it).

Mimo-2.5 created something similar, only a bit less complex than Gemini's (though, at least the FPS counter is real!), in a minute or two for about 1/3 of a cent: https://gisthost.github.io/?740c325c21e9bfbee59c4f94d9aab0af

GLM-5.3-Flash, currently my workhorse model, spent 12 minutes (ouch) thinking about the prompt. Didn't cost me anything directly because I have a GLM sub, but I did the math and it would have cost about 1.1 cents through the API. Turned out nicely in my opinion (though in reality, it still isn't really anything special): https://gisthost.github.io/?9ef050e16cec2561e6504e725a3f0bcc

Side note: thanks for setting up that Gist Host tool, it's very convenient!

---

Editing to add this bonus from Mercury-2.5-Preview, which I just learned released a couple days ago. It's much less impressive-looking than any of the above, but it cost less than 1/20th of a cent, and the response was generated effectively instantly: https://gisthost.github.io/?02f40b50aa891bf396bfaaa3a7998203

Mission accomplished. That's both cool and fast.

OK, but what about a pelican in a bycicle.

For a moment I thought it was a version of goatse. Once burned...

LLM: produces a toolbox of an id and a clock

User: use them both

Made me giggle.

next you should ask it to document where it got the code from.

good to see people still value trinket toys

I've been using Gemini 3.7 for my personal trip planning app. Across multiple benchmarks, it ranks higher on everything I tried:

- Real world knowledge (when a thing opens and closes, the geographic region, historical facts). It's also the best at taking a cluster of places and working out a visiting order.

- Photo ranking (which photo should be the hero). Gemini can tell whether a photo is of the thing or of the view from it.

- Document parsing (extracting the relevant trip info from PDFs).

If you use LLMs for anything other than coding, I definitely recommend not discounting Gemini like I did just because other models are more popular.

Gemini 3.7 is my workhorse - fast and good enough for most tasks. Occasionally I go to GPT Sol or Claude to improve Gemini's output or for more complex tasks, but more than of my work usage is Gemini 3.7. Quite happy to test 3.8 now.

I've been benchmarking[1] models for trip planning and world knowledge specifically (to decide on which model to use with my travel app), and the Gemini models consistently come out on top.

[1]: https://tripstitch.app/benchmarks/

I started trying out 3.7 Flash this week and it is competitive with opus/fable and also FAST. It is getting work done that anthropic models were struggling with and the speed with which it does is quite a bit noticeably faster.

Beginning to think Google is a dark horse in this race and some of Anthropic's "everything feels janky and rushed" karma is going to catch up.

> Real world knowledge

For awhile now I've found Gemini will use Google search for pretty much any real world knowledge, which is a huge plus IMO. It's basically Google with a much better frontend and no ads/seo nonsense.

One thing in your comment surprised me: "when a thing opens and closes"

Why you would rely on the model's weights to know opening hours, instead of having the model call a web search tool to verify it on the official site?

This sounds great to combine with Wanderlog using an unofficial MCP I made https://github.com/shaikhspeare/wanderlog-mcp

I've just returned home after doing a 100 day "half lap" of Australia in a camper trailer with my family. I made heavy use of gemini to plan much of it. Getting packed up with a general destination in mind and telling gemini "we're leaving X town at 10am and heading to Y, where should we stop for lunch. My wife has coeliac disease, find us somewhere that does good gluten free options" was one of the many things I regularly leant on it for.

Never had a bad suggestion.

In my experience, Gemini 3.7 is excellent for general non-coding tasks. But for coding, especially backend development, I still find models like Opus 5 and GPT-5.6 more reliable.

I've swapped over to it in the past two weeks, it's been really good. It does what I ask and doesn't think it knows better than me, which so far has made it the most pleasing experience I've had when slop-coding.

My only wish is it were somewhat cheaper, as it tends to balloon pretty quickly when I'm using it in Opencode. I'm currently trying to offload a lot of work to subagents to stop the context expanding so rapidly. But on the upside, I rarely have to correct it - I've spent far less time arguing with this than with anything else so far.

Yeah, most people keep bashing my aibenchy.com benchmarks, because Gemini is on top, but that's because questions are not coding only...

Can G3.7 use Google Maps for distance grounding?

> trip planning app.

this has to be stong suit of ai agents any model

thanks! this is a very helpful one. I am going to try.

I tried similar travelling tasks but also added transportation and complex transfers (train, bus, walk, next train...). Worked meh and still a difficult thing to do for a llm.

"Claude 3.7"?

I stopped using Gemini a few months ago because it would often just (partially) reply literal nonsense to me.

Think 2023 style ChatGPT. Something like “to open a document on your Mac click File > Open docurrrar” - like it suddenly forgot it had to produce actual words.

Overall I enjoyed its speed and comprehensiveness. But those occurrences of nonsense just made it feel like a great car that once a month just stops in the middle of the highway.

Currently top at https://deepswe.datacurve.ai - beating Opus 5!

https://artificialanalysis.ai/models/gemini-3-8-flash shows an intelligence score of 59, the same as Opus 5 medium!

Wow - for a flash model this seems to benchmark powerfully. Remains to be seen what it is like to use.

As of writing this comment, Claude Opus 5 has an intelligence score of 63, not 59 (it's not the same as Gemini 3.8 Flash).

With a score of 59, Gemini 3.8 Flash is in eighth place, falling behind even Grok 4.6, Kimi k3, and GLM 5.3.

https://imgur.com/a/BMOJBED

On artificial analysis it's only equal to opus 5 medium effort. Opus 5 max scores 63.

Further, opus 5 medium outputs 4x fewer tokens to achieve the same result, negating a lot of the speed difference.

The benchmark also doesn't include speed. You almost think something has gone wrong when using it because it returns full responses so incredibly fast.

The rumor is that 3.9 is an equal improvement in all directions, and that it should be another fast follow on like 3.7 and 3.8 were.

It's almost across the board better than Terra at less than half the price. 3.9 is likely to approach Sol at the 1/10th the price.

Hopefully OpenAI releases Astra first, and it's not only better than Sol but significantly cheaper, too.

A fifth of the cost of Opus 5! Google is certainly pushing the completion with this.

Crushing it on DeepSWE is a very big deal. Excited to give this a try.

Google - we're so back

I've been trying this Gemini 3.8 Flash for a day. Looks not much different than Gemini 3.7 Flash in my use case: I have Codex (gpt-5.6 sol) write up a design plan to implement a feature or refactor a portion of a system I am building, and have Claude (Opus-5) and Gemini (3.8 Flash) review and critique the plan, until all problems are addressed by Codex and approved by the reviewers; then have a cheaper model of Codex (gpt-5.6 luna) implement the plan, and still have Claude (Opus-5) and Gemini (3.8 Flash) review and critique the implementation, until all problems are addressed by Codex and approved by the reviewers.

The result is the same as the previous Gemini 3.6/3.7 Flash days: Claude could always note much more problems in Codex's plan and implementation than Gemini could - the ratio is like 10:1.

I occasionally switch the roles between Codex and Claude, and result is the same, Codex could always catch much more problems in Claude's plan and implementation, than Gemini could.

So I am guessing in a relatedly complex codebase, Gemini is much less effective in acting as a guardrail (or a senior engineer/team lead) than the other SOTA models.

deepswe is public and can be considered contaminated.

>shows an intelligence score of 59, the same as Opus 5!

...on Medium reasoning. Claude Opus 5 (high) is the default in e.g. Claude Code and scores 61. Still very impressive.

We'll see about that. I suspect benchmaxxing as all the labs do as I haven't found Gemini models to be nearly as good in agentic engineering compared to Claude or GPT models.

Check number of agent steps.

sidenote, but wow sonnet 5 is shockingly bad on this benchmark.

There are important gaps in that hot take.

For example, it's not even close to Opus 5 on Terminal-bench 4.0, 19.1% vs. 51.8%.

Wait a week with your judgement - most likely, Google is just bench-maxing very hard. If you look at the previous Flash models and the announcement on Google I/O, it was an absolute disaster. Reality diverged very much from the marketing (supposedly great benchmarks).

Pelicans (thinking effort high, medium, low): https://tools.simonwillison.net/markdown-svg-renderer?url=ht... - high cost 8.9742 cents

Here are the 3.7 pelicans for comparison: https://tools.simonwillison.net/markdown-svg-renderer.html?u... - high cost 8.4387 cents

(I think thinking level low is a regression on 3.8 compared to 3.7.)

This is in comparison to Fable:

> https://tools.simonwillison.net/markdown-svg-renderer?url=ht...

> Took just under 14 minutes to generate, and at 65927 output tokens cost me a hefty $3.30!

So 50x cheaper - and how much faster?

Why are the SVGs getting more detailed rather than just more correct than previous models?

It's about to squash a tiny baby pelican

The rendering of the gullet is very poor, because its both behind the handlebars but in front of the bike frame (impossible geometry). Surprising because gemini is usually pretty good on geo spatial skills.

Edit: scrolled down to medium effort, its better but also has a weird clipping issue with the fish in the beak.

The fenders are a nice touch, but putting the fenders through the tires seems like a design flaw.

These are becoming unreadable as the reasoning chains expand. I think you should consider reformatting them and either putting the image first or else folding the COT output.

Impressive pelicans!

I mean no offense but these pelicans are a bit tiresome and a very meaningless benchmark. There's no real difference between any of these svgs across models and model versions anymore.

The most interesting thing about the Gemini models is still their multi-modal support: they accept audio and video input, OpenAI and Anthropic's flagships are still image-only.

Gemini Flash is also pretty cheap, so it's a great family for performing media analysis, like extracting structured data from images and video.

Lost in the news was their update to gemini video analysis yesterday, dramatically cutting tokens (up to 88%!) needed to analyze videos.

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

Yeah we use it a lot for analysing streams and clipping content. As well as analysing social content that gets put out.

We transcode everything to 480p before we send it to Gemini batch api. Works great

Interesting side note: although Opus is still image-only, you can still drag videos into Claude Code and it doesn't blink an eye; it just strips it down to a series of images to parse.

True multimodal support would be way better, but I have no issues pasting in full screen recordings while QA'ing games and having Claude identify and fix issues in the video.

Something maybe unfamiliar with you: not about coding but writing. I've asked it to write an argumentative essay, which is a part of "gaokao" (China's university entrance exam), and its work is *extremely* impressive. speaks and writes like a real senior high school student, and the opinions unfold progressively with deep hierarchy. I don't know how the Gemini team reaches this because this kind of Chinese capability literally outperforms at least 2/3 Chinese students, no to mention those who speak Chinese. After all, the model speaks like a real humankind if you prompt it well. That's AGI guys

Gemini is known for good at creative writing in the Chinese writing community. It's a bit ironic though. Google has probably the most and best code base among all tech companies but its Gemini is bad at coding. Google has no access to Chinese market but its model is incredibly good at writing in Chinese.

Nitpick, but in my opinion an LLM is an "it", not a "her" or "he". Using male or female pronouns risks anthropomorphizing them which can lead to unhealthy outcomes.

People have been sleeping on Gemini lately but these last few Flash releases (which were very rapid) are damn good.

These sort of fast and cheap models are great for tasks that are verifiable and can be retried infinitely (like coding), you can basically get frontier results with a good harness (at a fraction of the time and money).

As someone who has stubbornly stuck with Claude Code, what's a good harness for Gemini models?

I agree.

I might be wrong about this, but obviously Google would like to provide inferencing at the lowest cost to themselves, so perhaps their slow ‘pro’ releases and rapid ‘flash’ releases is an attempt to guide people to use more profitable models?

Looks like the strategy of regular updates with incremental improvements is working out well. Interestingly, the biggest jump in Artificial Analysis Intelligence Index score is for reasoning level Medium ( 3.7 was 51, 53, 57 for Low, Medium and High, 3.8 is 52,57, 59 respectively). I think scores at lower reasoning levels are more indicative of model capability since higher reasoning levels are focussed on benchmaxxing. We use the lowest reasoning level in production with good results.

I'm not an expert but I agree with your statement on the lower reasoning levels.

Lots of models seem to just allow the model to "bloatmax" tokens in order to get bumps at high/max reasoning levels. Many of the max reasoning levels allow models to use up to double or more the tokens the next lowest reasoning level uses. Its basically only useful for people who have no cost or time stipulations on anything.

I think I actually preferred it when we had models that either had reasoning enabled or didn't.

Wow this comes after what - 3 or 4 weeks since 3.7 Flash, which was also 3 or 4 weeks after 3.6 Flash IIRC?

I eagerly wait more info but sounds like Deepmind without Demis calling the shots has been unleashed and are operating at full speed? Shocker!

At this point it is a meme of course, but where is 3.5 Pro :)

According to the WSJ, 3.5 Pro is reportedly being skipped entirely, making Gemini 4 the next flagship model after post-training.

https://x.com/AndrewCurran_/status/2094937419615502370

A month is not enough time for any meaningful change in an organization the size of Deepmind/Google. These models were surely the result of work streams and teams that started under Demis. I think Demis can safely feel proud Deepmind is getting back on track.

Now i am awaiting Gemini 3.11 "For Workgroups" to be released early December...

"The knowledge cutoff date for Gemini 3.8 Flash is March 2026 – users can expect updated information for some domains while in others they may experience the model’s knowledge is limited to January 2025 (in line with the Gemini 3 Model Family)."

Kind of wild that they haven't (successfully) pretrained a base model since Jan-25.

I'm curious if the knowledge cutoff is important, when the interface (Gemini app) can search online for recent information. Is there a big advantage to having everything internal?

That extremely likely just means that they're preparing an omega huge Gemini 4 Pro release and that that's what training right now on most of the compute

Wait, I didn't realize 3.7 Flash was already beating Sol on a bunch of the benchmarks. Isn't it a way smaller models?

IDK if it's smaller, but I know it's way faster. In one test I did, Flash 3.7 high was ~9.4x faster than Luna High.

But, also... Sol crushes Flash 3.7 at writing code in a codebase of any size beyond "tiny".

Flash is my go-to for prototyping, and basically anything that isn't writing production code.

They're quite selective in benchmarks, c.f. notably only bad one is 10% on TerminalBench. It's a really addled model, one time I said "Hi" and it built out a 4 panel hello world app with (fake) weather, a todo list, and a couple other things I forgot. I wouldn't be comfortable saying "ignore the #s!" except when I complained it was trash and way overcooked on agentic coding yet not good at it, and a couple DeepMind ML people liked the tweet.

It's pretty good if you can actively steer it , its actually really really good , the antigravity free tier and pro tiers are generous as well . I'm shocked at how fast it generates tokens.

Gemini 3.7 Flash was already smashing more expensive models on my Redactle benchmark https://redactle.net/llm-leaderboard which mostly tests omniscience.

They've interestingly left out any mention of speed.

I have been testing 3.7 flash against 3.5 flash and it seems to lose every time in overall latency. Every benchmark I've seen seems to suggest the opposite[1] - that 3.7 flash is significantly (at times 2x) faster than 3.5 flash - but I have never been able to prove this out in real world use cases.

Has anyone found their latency numbers to actually be accurate? Is this why they've toned it down in this release? For context, I'm testing larger generation payloads that take 8-10 seconds in 3.5 flash and 15-25 seconds in 3.7 flash. Lowest reasoning settings in both cases.

1: https://artificialanalysis.ai/?speed=intelligence-vs-speed&m...

In my niche Redactle puzzle solving benchmark [1] I noticed Gemini 3.8 flash is slightly faster than 3.7 flash. They both smoke every model I've tested. I have not yet run 3.5 flash. Gemini models are great at this task because they seem to have exact Wikipedia text baked into the weights. When I rewrite the wiki text a bit it's not able to one-shot the game so much.

[1]: https://redactle.net/llm-leaderboard

It depends on how you're querying Gemini models. OpenRouter is the fastest by far. I'm guessing they bought the dedicated pipe from Google. Gemini via VertexAI and consumer API has pretty bad latency.

I like Google's strategy here. These new Flash models of late (Flash 3.6, 3.7 and now 3.8) have obviously been distilled from a much larger unreleased model (Gemini 3.5 Pro, iirc from the rumors).

One aspect of model releases that don't get discussed as much are the cache invalidation (changes in underlying architecture, weights, or tokenizers); I assess Google seems to be squeezing the maximum out of the last 'Pro' version they released with 3.1 back in February.

Small models cataching up with their bigger siblings are fantastic news.

A couple larger GCP customers requested this for sometime, especially on the cybersecurity side.

A SOC/IR or AppSec team doesn't need a generalized model that knows when Chaucer lived but it absolutely needs a model that can efficiently, quickly, and accurately prioritize vulnerability severity or validate patches.

Seem like a great, no-compromise, upgrade over 3.7 which is already a bargain, fast, and doesn't have the brain-damaged writing style of Claude.

that's certainly what it's looking like so far. kind of mind boggling ...

I don't know if Google is having the worst marketing fumble or the most genius marketing one. Their "flash" models are very comparable to other companies' "pro" or "flagship" models. It seems to be a quite counterintuitive naming convention as it undersells the models.

Unless they have an even more powerful Gemini Pro in the oven...?

the 3.5 pro pretrain was a complete disaster, they shelved it and are now working on gemini 4.

3.0 flash -> 3.8 flash is all post training which is pretty impressive.

The conspiracy theorist in me wonders if it's about keeping the federal government out of their business after seeing what happened to Sol & Mythos.

My assumption is that they're cooking an ultra humongous Gemini 4 Pro release. They certainly have the cash and the compute for it, and it's so obviously the thing to do from a strategic perspective.

Well if that's the case, it's been in the oven for quite a while now.

not saying they do have a beefier pro, but even if they did, isn't the delta between flash vs pro models reduced quite a bit? (e.g glm 5.3 flash vs 5.3, v4 flash vs v4 pro, sonnet 5 vs opus 5)?

so far using Gemini from 2.5 Pro to date (3.7 flash) - the way google trains the model it seems - is to identify top 3 to 4 things to fix first. as a result gemini is not that good in being thorough - but it's a needle mover . Opus5 Opus4.8 and Fable always point out things that Gemini missed. but Gemini was a needle mover - identifying the most important things to fix.

I always enjoy interacting with Gemini . When i ask it questions about designing a new model etc - it's always the most helpful and encouraging . I really want to thank the Google team for this and their happy positive models they generate.

I use gemini flash after a round of deliberation between Sol and Kimi these days on the main plan . Kimi 2.7 paired with Gemini 3.5/4.6/3.7 flash has been my implementer - Kimi k3 and Sol 5.6 have been my planners and code reviewers.

I would have ideally like Anthropic and was on their USD 200 plan - but after they didnt sign the letter for Open source models - i dropped my subscription . Wont make a difference to their lives.

But Sol is great . Combined with Kimi K3 for adversarial plan reviews - you get robust plans . And Sol as a reviewer for Gemini/kimi 2.7 - you get great edge case handling and robust code.

I also integrated muse 1.2 - on the contributor tier - and I Just saw facebook release 1.3 muse . this is great news. The training im permitting is my thanks to FB for releasing the open source models of the past ! Thank you !

Also just want to let my appreciation here for 3.7 it’s cheap super fast super reliable incredible at information parsing eu host able (important for us) and perfectly integrated into gcp. Great job google!

I hope they bring a lite version, its good enough for information parsing and very cheap.

3.7 flash was by far the best model for image recognition tasks according to my benchmarks. 3.8 flash didn't regress any candidates and improved some specificity (positive ID of common name vs species name of exotic fruit, correct identification of cast/replica of artifact and statue) but is still relatively weaker (26/30) on esoteric public figures (Korean beatboxers). I'm going to have to make my benchmark harder.

I’m very curious about your esoteric public figures benchmark, do you ask it in English or Korean to identify the person? Does it change the result? I wonder if having data labeled in only a given language (or web sources in only a given language) change the output.

It struck me today using Google Antigravity (Claude Code alternative) just how direct and usably terse Gemini is in prose.

I've complained plenty on here about Claude verbosity and TED-talk phrasing, and it seems by contrast Gemini has already arrived at the dream end-state of Claude from a prose standpoint.

Sometimes I ask for feedback, and I get back a list of multiple-choice options as if it's already ready to go. If I indicate I'm thinking about doing something, sometimes it'll just...do it. (Not in an annoying way.)

It seems very geared toward action in a way that's completely refreshing coming from months steeped in Claude essays.

Gemini 3.8 Flash is top of the Redactle LLM benchmark but so was Gemini 3.7 Flash. Both one shot all puzzles in the evals though 3.8 is just a bit faster. It also does the evals cheaper and faster than almost all the other models I've tried.

https://redactle.net/llm-leaderboard

The flash models, for coding are reckless in my experience. I have a Ultimate subscription, get good quota, but still use Opus 4.6 as it's much more reliable if you manage the context window carefully.

I use Flash model as code implementation executor, then have GPT-5.6-Sol or Opus to review the work. Pretty good so far and presumably less expensive.

If by reckless you mean commit, push, deploy without me asking it to, the I agree!

> The flash models, for coding are reckless in my experience.

My experience is that antigravity is awful and reckless - but that the model itself isn't.

    "available to trusted defenders through our new Fairwind Program"

Then why even bother announcing this? Ordinary people can use K3 and GLM 5.3 or whatever drops next and avoid all this hassle.

You're telling me for only 5x the cost and 1/10th the speed I can use a Chinese model which performs worse than Gemini 3.8 Cyber? And I get to do all the hosting and setup work myself instead of just using a model and framework which is already integrated with GCP? Dang!

Agreed. The only legitimate use case is restricted to a secret guild. Imagine:

"Valgrind is only available to trusted defenders in our new UnfairAdvantage program"

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

Still zero on the felony bench.

Is this weakness in their training regimen the impact of operating under regulatory frameworks for too long?

Still refuse to search internet for stuff it thinks does not exist lol.

And even when searching for internet, it still cannot suggest a up-to-date approach to the problem.

For example I'm using crystal, it recently revamped the concurrency/parallel model. Even using web search, gemini still does not aware of the new feature and still give the outdated code.

I'm sure my crystal usage is not the unique case here.

Everyone is censoring models now with anything remotely resembling cyber or bio. I already have problems with my research in mathematical epidemiology because of that - both Sol and Fable simply refuse. They keep pushing people towards Chinese models that can be decensored.

Supposedly Fable 5.1 is better, but I haven't tried it yet. I've run into the same thing with mundane work that is barely bio/cyber adjacent.

Re: Chinese models, even if the model itself isn't censored, some of the big model providers have guardrails now that you can't exceed, which somewhat defeats the purpose.

I can’t wait until waiting hours and spending a big chunk of your usage per task seems antiquated, and real-time iteration on massive code changes is the norm. This might just be the year of efficiency, that truly allows AI to be used to the heart’s content.

When deepseek-v4-flash-0731 was released I used it constantly for everything, and I loved the very low cost and speed. I think Google has the same game plan with their flash models.

It is more expensive per task than 5.6-sol high: https://artificialanalysis.ai/models/gemini-3-8-flash#price-...

Cheaper at medium level while still being same score as Sol medium

Huh, according to some of those charts, it's both dumber, and more expensive to run against their benchmarking tasks than Fable??? Seems crazy to me.

Sol is still underrated imo, especially for the current discounted price

Seems to do reasonably well in opencode according to artificial analysis. https://artificialanalysis.ai/agents/coding-agents

If I had to pay per token I would probably consider using this (they seem to be on the pareto of performance) but not being able to use opencode with a subscription is not really something I'm realistically going to do when claude and codex are around. Also never gotten along well with gemini-cli / antigravity-cli.

It seems like this is one of the most powerful models for the price, really didn't see that coming from Google

Is the google infra stable enough right now? At the start of the year, the flash model was unusable for a whole month via gemini CLI. They could not fix it for a whole month and I was a paid customer.

I haven't had any issues lately.

I use it quite a lot and after a week of use I’m being hard rate limited

I have mixed feelings about Gemini 3.7 Flash. I used it for a personal project in Java and it was ok: it was crazy fast and it reached the correct result, but the code quality was barely passable.

I also used it for a an app for my Garmin watch, and it wasn't good. The code was compiling, but functionality was totally broken and even with a lot of steering it wasn't able to make it work. GLM 5.3-flash instead was up for it and the code wasn't bad at all. I am curious to see if 3.8 is an improvement in this use case.

been absolutely loving 3.7 flash for coding. it feels very fast and quality is decent for implementing product features. usually use opus or sol for hardcore debugging.

Is there any good subscription and CLI harness to use Gemini models now?

I tested Gemini CLI while ago, and it was awful tbh.

i think it's better than sonnet 5, especially when you compare speeds. i have to work with the llm anyway, the faster i can turn it the better the outcome.

I don't use Gemini, but I thought `cool, let's give this new model a try`. Opened gemini.google.com, and I'm not even surprised. The drop down gives me the following options:

- Flash-Lite

- 3.6 Flash [new]

- 3.1 Pro

The above is why i don't use LLM products from Google. If the model is not available right this minute (heck, hours before the release!), then I'm not gonna bother getting back to it tomorrow, because tomorrow I'll be playing with the new model from OAI/Anthropic.

It's such a weird attitude, especially considering that 1) it's readily available on AI Studio 2) Anthropic models were not always available the moment they got released either.

(It also shows that the internet isn't dead. Even people who are not aware of Google AI Studio can express their valuable opinions on LLMs!)

I'm a paid Gemini subscriber via Workspace Standard accounts and yet I also only have access to 3.6.

So frustrating and confusing.

Meanwhile Anthropic and OpenAI simply release a model everywhere (Fable on Pro only as a somewhat mild exception).

That looks like the options that get presented for Workspace users (like at my company). The personal Google accounts give more recent models, for some reason I don't understand.

The android Gemini app defaults to 3.8 flash already.

As someone with a Pro subscription, I had access to 3.7 the day it came out. Expecting to have access to 3.8 now, too. It's only the free accounts that are behind.

I see it on my Pixel with the Gemini app.

it's weird how the web ui doesn't show the latest flash options while the desktop/mobile apps update the same day as the release. I saw the model in the model selection (by coincidence) before seeing it show up on HN

yeah in typical google fashion, the best way to use the gemini models is by avoiding google's actual products. i've got a vision project where gemini flash is the best option by a long shot, and i just use openrouter so i don't have to navigate google's mess.

FYI, 3.8 Flash is available on aistudio.google.com (along with all of their other models)

But yeah, they really dgaf about gemini.google.com -- I dropped that sub in April when it was clear OAI and Anthropic had lapped them

i have it on gemini app the moment they announced it. pro, student

Sums up Google AI products.

I have a weird vibe from all the comments in this thread, they feel like a script rather a real experience.

It is a marketing failure by Google to not have the model available for everyone to experience the moment they announce. Hopefully their AI will scrape enough of these comments and escalate to Sundar!

It's available in antigravity which I started using again (for small things until I can trust gemini for coding again).

It's interesting that Deepseek models were missing in the comparison. I see Deepseek v4 Flash a direct competitor to Gemini Flash for text-based agentic work.

For my application, I'm still happily using gemini-2.5-flash and the only problem is when it reports being overloaded. It's for interpreting a downscaled phone camera photo of a hand-written shopping list on a whiteboard, and it works stunningly well. My handwriting sucks, too.

(I guess the only relevance here is that if your problem matches a model's strengths, then you can do fine with a model that is several generations out of date.)

I believe the older models are being gradually phased out, newer ones have no availability issues

I would test this, might be cheaper per task even costing more per token, probably faster too

please do yourself a favor and use something far more efficient ! GLM5.3 will make you super happy

Wow fable5.1 was the first model to do what I actually told it and I couldn’t find any problems with it, excited to try this just a day later lol

Gemini 3.7 benchmarks against GDPVal-AA-V2 were 1525 in Aug blog post. However same model against same benchmark is 1482 in today's blog post of Gemini 3.8 release..

Do they make it intentionally to look previous model less superior than current models? or these are the real numbers when re-ran the benchmark..?

How generous is the Google subscription quotas compared to Anthropic and OpenAI? This sounds like a really good potential model for high volume due to its speed and cost effectiveness.

(By high volume I mean things like "main app just updated with XYZ commits, please scan XYZ plugins and surface any compatibility issues")

I'm on the Ultra plan and use it for chat, antigravity, and some other work automations (similar to your example). The only time I've ever hit my limit is when I use Deep Think (which usually eats up 4-5% of the 6-hour usage limit per response).

Really generous. I'm on the Pro plan and I just use Antigravity for vibe coding w/o automation. It's actually difficult to hit my weekly limit now, it takes about ~30-35 hours of continuous agent work, which virtually only happens when building a new app from scratch.

3.8 uses nearly twice as many tokens as 3.7. One might be inclined to think that they just increased the thinking budgets...

3.7 used 64M on high: https://artificialanalysis.ai/models/gemini-3-7-flash 3.8 used 120M on high: https://artificialanalysis.ai/models/gemini-3-8-flash

Even their own chart showed more than 2x higher cost compared to 3.7: https://storage.googleapis.com/gweb-uniblog-publish-prod/ima...

3.7 high and 3.8 medium are essentially the same on AA intelligence and cost. Output tokens on DeepSWE gives the same picture. So there might be something to it but they have done other things as well. At least the tokens are really fast.

3.8 uses nearly twice as many tokens as 3.7. One might be inclined to think that they just uppsed the thinking budgets...

3.7 used 64M on high: https://artificialanalysis.ai/models/gemini-3-7-flash 3.8 used 120M on high: https://artificialanalysis.ai/models/gemini-3-8-flash

Even their own chart showed more than 2x higher cost compared to 3.7: https://storage.googleapis.com/gweb-uniblog-publish-prod/ima...

As a big proponent of GPT-5.6-Luna for the combination of speed/perf/(especially)price,

Flash 3.8 seems like where I can specify Flash3.8 as the coding model as part of agent workflow.

The video recognition is especially impressive as they got all of Youtube to train from.

- Def people who has to queue video recognition jobs to use the model.

I'm surprised the introductory 50% discount is good for 4 months. It seems like frontier models release new versions every 2-3 months, so raising prices in 4 months seems like a bad plan: you're effectively planning to charge users twice as much for a model that is no longer frontier.

The goal is to encourage users to move to the next generation. The fewer models they serve, the less excess capacity they need to provision.

Serving more models also adds a significant ops burden on the SREs and trust& safety teams.

>"safety performance" - this starting to get long in the tooth. Gemini cut programming session 3 times for "safety reasons" yesterday for mentioning image generation (I need to generate bunch of those for infinite zoom virtual training app experience). After I got creative and managed to trick it to answer t was of course because "think of a children"

And in my other app I was debugging and using OpenAI to optimize some path it cut me off numerous times because it did not like JIT functionality (this is my commercial business rule evaluation engine that compiles rules to executable code inside the app to increase performance using asmjit library)

I am basically paying for them to waste my tokens and time on these 2 tasks

CAD for 3D printing is finally becoming feasible with Flash 3.7 and 3.8. Exciting times. https://github.com/ModelRift/openscad-skill/

We've gotten an unusually fast speed of Gemini Flash releases over the past few months. Is this Recursive Self Improvement, or Google just trying to distract from the fact that it's been a while since the last Gemini Pro release?

The blog post says it is RSI: “both of today's releases are … accelerated by long-running agentic loops designed to recursively evaluate and refine the underlying models.”

Nice surprise. In a few of my own tests it seems maybe a tad slower than 3.7 (but still way faster than any other LLM I've used) and even smarter. With 3.7 I felt I could just not use 3.1 Pro at all and 3.8 seems even better.

So 89.4% on Terminal Bench 2 but only 19.1% on Tbench 4. Opus 5 is 89.1%/51.8%.

I was thinking the same, obvious suspicion is they benchmaxed it on older bench.

After struggling with Gemini for months, I think the trick to getting the most out of the model is writing a really solid personal intelligence/instructions prompt. The results are night and day in terms of performance.

Funnily enough you really do need a great prompting and SKILLS setup to use antigravity effectively in contrast to other providers which actually started benefiting from less detailed prompts over time. But I like it this way, its more customizable and much cheaper especially with a sub.

slot machine addict thinks if he pushes buttons in a certain order the odds get better.

In all seriousness, gemini has the best interactive planning document/orchestration. Tell it to create a plan document and work through it with it and it will preform really well(in antigravity products). But this is the case with plan modes with every model, I just think the interactive document that antigravity uses is really well thought out.

Whatever they’re using within the Maps app is not good at all. I cannot just ask it for things conversationally like I do with ChatGPT. They really need to put a better model in there. I don’t even think it maintains context across two different queries within the same session. It’s not seamless and doesn’t just “get it” like ChatGPT does.

Yesterday I asked for food stop on my road trip 45 minutes from the current time and it gave me some options, but then I changed my mind and specifically asked for Asian restaurants and it completely forgot about the 45 minutes and gave me the closest Asian restaurant to me.

I think about Google is the value you get of their plans, for 5$ a month you get their ai plus model combined with 400gb you can share this with your family. The other ai companies don't provide family plans

And the free year-long trial for college students they recently offered, which includes 5 tb of Google Drive storage.

Is there any reason to even use 3.1 Pro now?

In my experience? No. 3.7 is faster and it just seems to get things right more often. Only big architecture tasks and analysis make sense with 3.1, perhaps, but honestly just use the Opus 4.6 to generate a plan and then switch back to flash for the implementation

It is still going to be better at text work, skills, document review, deep reasoning, architecture review, etc. It is only 6 months old, it isn’t like its world knowledge and software knowledge is really out of date. Use it to churn on harder design problems.

3.7-flash has been useless many times, specially when context gets bigger. 3.1 is the only Google model that has seen use from me. With extended thinking, 3.7-flash is kinda usable but not without many problems. I find myself falling back to 3.1 often. I don't believe in any benchmarks because whatever they are doing to award 85% to 3.7 on anything, they should seriously reconsider that test for anything.

IME 3.1 Pro still has better system-instruction following than Flash 3.7, esp. when there're many conditions and clauses. 3.1 also writes better prose for technical material than Flash 3.7.

Once the system prompt complexity goes up, Flash starts to write very dense english. it might be fine for tasks like coding, but not for user-facing text meant to be digested by the average person.

I haven't tested 3.8 on my workload yet.

In my tests 3.8 Flash is considerably more expensive[0]/less token efficient than 3.7 or 3.6, and not necessarily much smarter. I assume it is faster in tps, but hard to tell because ot also outputs more tokens, so response time is slower oferall.

[0]: https://aibenchy.com/compare/google-gemini-3-6-flash-high/go...

The blog post says that their gains come largely from the model trying harder. So, more tokens, more time spent.

Gemini 3.8 flash seems to be especially low efficiency in tool calling for some reason.

Gemini flash seems to have been a bit of a sleeper. Somehow it's ended up as the most used LLM for my client document extraction work these past few months.

I have an eval harness that runs every Thursday to determine which models are the current best for a few different client workflows. And since May(?) flash has slowly been taking over more and more stuff to the point it is now 100% on 8 out of 11 document extraction flows with the other 3 being a Flash / Opus 4.8 mix for high value stuff where cost is less of a factor.

Do they officially support you use their AI Pro subscription (or whatever the heck it's called this month, the one that gives you models in antigravity) in a 3rd party harness?

Meanwhile I pay for Pro and still don't have access to 3.7?

Same for me (at least through the Gemini app).

I'm running lexical analysis on gemini 3.8 flash and the latency progress is incredible. For my tasks the latency is reduced by ~40% w/ quality on par.

I see benchmarks beating sol terra and sonnet. But is actually better? Has someone used it? I don't see actually much people that use Gemini for coding.

There must be a deeper read on why Google can rapidly ship better small models while being delayed months on the bigger model.

What's the simplest explanation?

Perhaps post training? I believe I read that Qwen 3.8 is just post trained Qwen 3.6, which is why it was able to be released so quick.

It may be that these flash models are simply post trained larger older models.

Is the Gemini CLI still terrible compared to Claude Code and Codex? The harness the main thing holding back Google models as they could've been the best given all the advantages in compute capacity and training data they initially had, where now even the Google CEO said they're falling behind in agentic tasks, which is sort of a vicious cycle because RLHF relies on human usage.

In May they replaced the Gemini CLI with the Antigravity CLI.

https://developers.googleblog.com/an-important-update-transi...

That was sunset and replaced by Antigravity. FWIW until I abandoned it knowing the sunsetting, I was able to get good behavior out of Gemini CLI with overriding the system prompt. The default prompt crippled the harness with very poor instructions, but there was a hidden ENV to override it. Replacing it with Claude Code like prompts based on the model selected, it ran at a much higher intelligence level full stack with significantly less errors.

There is no Gemini CLI anymore, nor you can use Gemini with your own harness unless you pay per-token.

It was superseded by the antigravity CLI.

it is antigravity now. It is ok

Honestly, it's platform dependent and "OK" at best, "Mediocre" at worst (Agy on Windows).

Gemini is great via the Chat interface and decent via Github Copilot.

I honestly hate it via Antigravity CLI because their sandboxing system frankly doesn't work. Every other harness has mastered "don't ask me if you're working in this one directory and using common commands". Agy instead either tries to pull a global elevation or wants every tedious variation of a command string whitelisted. Madness - circa 2023.

Agy _really_ needs to make the out-of-the-box experience cleaner and hassle-free. Heck, even Grok CLI "just works".

This may reflect a global mind-shift from "approve and validate everything" to "just do the stuff and only ask permission if it's outside the folder or a command that actually requires elevation". Maybe that's not for everyone, but for those that do want to perform unattended agentic work -- Agy is painful.

The model seems fast enough to solve your problem before Google finishes explaining which of its three products you need to open to access it.

Gemini models look so good on paper by IRL dev and daily life usage totally make it seem like it's way behind Codex and Claude.

Is there any comparison of usage limits for Antigravity plans vs. Codex?

I just ran two light tasks on my codebase and got 100% of the weekly limits of a Pro plan blown away. Is Ultra plan any different? Because on Codex it wouldn't affect my Max plan at all, I think it would have been below 1% othese usage.

I am not surprised. Google is a real company that wants to make money on selling services.

Anthropic and OpenAI are in a different game of spending investor money to buy market share.

Alexandr Wang (Meta's new AI chief) makes a good summary of this model: https://x.com/alexandr_wang/status/2079707749412483104?lang=...

He inherited his predecessor’s bad manners and arrogance.

Is anyone here using using these models via google subscription (not api). I tried to in the past using gemini cli and then agy - headless invoked by codex and claude code, but they were so incredibly buggy that it stalled 1/2 times and I cancelled. Interested to know if that has changed!

The iteration cycle is becoming very quick. Gemini 3.8 Flash arrived just 20 days after 3.7 Flash.

Similarly Qwen3.8-Max was updated in just 30 days (to the 0902 release) and Muse Spark in just 28 days (to the 1.3 release).

A year ago iterative releases were every 3-6 months. At what point will they reach nightly candidates?

It's a shame Google crams it ham-fistedly into search results and that Google has some of the reputation it has because I actually really enjoy Gemini and I don't even use it for the reason people often list which is that you can cross-reference it to stuff in your Google account

Could someone explain to me why it matters if google has the best model? Isnt the real metric cost per task?

On my short tests: This model is amazing and the speed makes it feel like another sort of AI.

But it's bad at code reviews (maybe it's the harness agy cli?). Could not get it to same quality level on reviews like Opus, GPT 5.6, Grok. Even tried special code review skills but no luck.

Dear Google, Kindly make you chat window on the right side of vscode in antigravity extension, There is a reason others kept it like that. I can see the code and inspect the files changed while Agents keep working. its critical for me personally.

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

congrats to the gemini team, the work is super impressive!

I switched my subagent-swarm skill(https://github.com/bazelment/yoloswe/blob/main/.claude/skill...) to the 3.8 model and has been very happy so far. It has been driving the swarm to produce steady outcome, and more importantly, the exec communication is also crystal clear, instead of filling with jargons and long sentences.

Curious which model this can supplant as a clear winner on almost every metric. Sol? Looks like it's not quite there on a couple of benches, but I'm not clear how much they matter in practice.

Hopefully before they release 4.0 Flash we will finally get Gemini 3.5 Pro.

more likely 4 pro will be released pretty soon instead, since pre-training for 4 began in late July

https://x.com/OfficialLoganK/status/2079594867161022817

> 4.0 Flash we will finally get Gemini 3.5 Pro

Nah, we'll just get the 4.0 Pro Preview.

I’ve used the Gemini flash, but then when I have soul ultra check its work, it found a bunch of cut corners and improper design.

As much as I like the speed and interactivity, I really don’t trust it

I asked gemini 3.8 high to review the site I'm working on for points of high cpu/ram consumption - it failed spectacularly and also halucinated the server i/o limits

From personal experience it feels much more capable than 3.7 Flash.

I’m interested in a general knowledge model (closed or open weight) and not coding specific. I want to plan for travel and trip. Do you have one of your favorite HN crowd?

this is cool for all other non coding task. however I am still stuck on 3.6 flash on my gemini web as a plus user, can anyone else even access 3.7 flash in AU?

AU user also, just checked AI studio since that seemed like the best bet and both 3.8 and 3.7 show up (and can be used for chat in playground, though IDK what the limits for that are). Chat in gemini.google.com is also 3.6 for me but I'm on free tier lol so I don't exactly expect it to show up any time soon. I think there's also another free API beyond the AI studio one (which is 20 RPD free according to docs so not really useful) but I forgot where it was (Google cloud maybe?) and what the limits for that were.

AU Pro user here. 3.8 Flash available (default) in the web app for me.

model card https://news.ycombinator.com/item?id=49537354 (doesn't 404)

  [1] For tone and instruction following, a positive percentage increase represents an improvement in the tone of the model on sensitive topics and the model’s ability to follow instructions while remaining safe compared to Gemini 3 Flash. We mark improvements in green and regressions in red.

Gemini 3 Flash?! So is Gemini 3.8 Flash less safe than 3.7 Flash in all areas besides Text to Text Safety (and identical on Image to Text Safety)?

Why bother with a column “Gemini 3.8 Flash vs. Gemini 3.7 Flash” when you’re going to disregard the label for 20% of it? Also is the “Tone” label short for “Tone and Instruction Following”?

Chartcrime, the major AI lab tradition.

Also https://web.archive.org/web/20260902150007/https://storage.g... in case it goes again

I use Gemini to make sense of things Claude says to me.

Will Gemini refuse to work like Claude when it hits modulo 11 calculation on a number string or finds a variable named CVV?

I would really love to be able to use these Gemini models in Opencode or Pi with my existing Google AI Pro subscription.

It’s strange that its score on Terminal‑Bench 4.0 is so low. They aren’t fast enough to benchmaxx that section.

A company with 400+B revenue from software cannot build a usable command line cli for its vital AI model?

They do have a cli: https://github.com/google-antigravity/antigravity-cli

Who coined the phrase "cyber" for security related things lol. It's so 1999.

Hasn't the field been called "cybersecurity" since... forever?

I assure you that in 1999 "cyber" meant something very different

Cyber is more of an early 1990's thing, and I have no issue with it unlike most in the tech field. I feel like it dropped off in the late 90's and early 00's but made a comeback as hacking became a mainstream security issue.

Amusingly, "cyber" comes from the word "kubernetes"!

A/S/L?

Relevant xkcd: https://xkcd.com/1573/

It's really disappointing to see social media dismissing gemini so easily.

I think the worst thing we can do is have loyalty towards models. I used to be loyal towards Claude, and my viewpoint changed dramatically when I used codex.

I highly recommend that if you are someone who only used one model so far, that you really give another model a shot and see how it goes. It's very eye opening and gives you a more holistic perspective.

Vendor locking is a big problem when it comes to models, and I hope the software world doesn't do this blindly.

Is anything going on with Gemma? Because of I want a closed model I use Claude.

If Google has the juice and wants to win, they need to start releasing world models.

Google keeps flashing everyone where everyone is expecting to get PRO'bed.

We also had GLM-5.3 flash and Qwen 3.8 Flash Next, everyone's getting flashed and I think it's a good trend.

Almost suspect that the rate of improvement to post-training is so fast that small models have an advantage - it takes much more compute to train a bigger model, so the flash models are just running in circles (well, not exactly of course) around the larger models right now.

Just tried Gemini 3.8 Flash on these 2 consecutive prompts at gemini.google.com:

1 what is tesla cybercab plan to address legal implications of accident that will happen? who is going to be responsible for them when they happen? are they covered by tesla insurance or some other insurance? are there any official plan/statements around that?

2 what was the name of the experiment they started in san antonio tx when some cars didn't have a driver? what was the results of it? did they expand the operations? it was much smaller than waymo, is it growing? how it is related to robotaxi?

It is not able to connect the dots that I keep asking about Tesla in 2nd prompt and spit out some unrelated stuff. Really? How it can be that bad? Gemini 3.1 Pro model works fine in this case btw. I thought maybe it is about knowledge cut over date and it doesn't know about those events from 2025 but it seems it has the knowledge up to March 2025. Top 10 in Intelligence on artificialanalysis ladies and gentlemen.

Am I the only only one thinking that Google might still "win" the AI race, despite the apparent gap?

They're apparently evolving slower than most SOTA models but "slow and steady wins the race" is probably still a thing.

And since Google doesn't depend exclusively on AI models, they can probably afford to "wait and see" where all this craze is heading.

Staying a little ways behind the leaders is not "slow and steady." Every company is moving very fast right now.

I don't think slow and steady will win this race, but I think anyone can still win--especially Google.

disclaimer : Introductory price expires on December 31, 2026. Starting January 1, 2027, $1.50/1M input tokens and $7.50/1M output tokens will apply.

Where is my Gemma 5?

The speed, combined with the fact that this thing is really good at JavaScript, is pretty exciting. I’ve added another AI programming assistant to my toolkit; hopefully AI will continue to get stronger.

When can we use it in Gemini (web)?

It still uses 3.6 Flash for example.

The recent Sonnet models have been disappointing for me personally which is why I'm going look into using Opus/Fable as the planner and Flash as the executor. Let the expensive model handle the hard thinking and use Flash for implementation and tests so that I can stretch the Opus/Fable usage further

I think hackernews should not suport closed moddels

shows up in /models though and encourages you to use it over 3.7 Flash I prefer this over reading specs: the "just show me" way

Anyone has a cached page / mirror ? 404

The race to the bottom continues

I have to say:

The Google brand remains powerful on HN!

I’m shocked.

the names are getting so ridiculous and childish, are we here in ideocracy?

playing around with 3.8 - it generates non-working code. basically unusable.

At this point I wonder why Google still releases proprietary models. They are behind the open models.

But where is Gemini 3.5 pro?

I wish google to thrive

Gemini 3.8 flash thinks Entoloma sinuatum is good to eat... Otherwise feels great

A reminder that google is the only major lab without a meaningful opt-out of training on your data. The only way to opt out is to disable message history entirely, which seems like a darkest of dark patterns to get users to leave "opt in" to training on, because next to nobody wants to use it without message history.

The biggest problem with Gemini is that its performance degrades the longer you use it for coding. Is it just me?

What did it say?

Why is it still such a bad coding agent? Does anybody have any insight?

I am continually impressed with Gemini's chat responses, which encourages me to test their agentic capabilities and... no... no... and no... every single time.

It's terrifying watching it, really.

love these flash models

Meh, not any noticeable improvement and unlike 3.7 high it eats all your credits, perhaps medium would be better

What is with Google's dumb ass STILL refusing to respect the OS dark mode setting in fucking 2027??

not really getting the excitement over this, its at opus 5 medium level, and opus 5 is not really the go to model , claude purists hate it

so its fast sure and decent at non coding usage but for developers nothing can really top sol or fable.

even grok 4.6 is so so and i would not choose 3.8 flash over it.

"Page not found"...

It's 404 now.

And yet again another failed launch from Google. I pay for their AI plus Google one package to get more cloud storage (have no interest in their AI bundle but you have to pay). and all I see in the Gemini app is 3.6-flash

I'm trying it now for token heavy coding tasks, it's capable for many tasks but in noway compares to Claude/Sol - requires more prompts and the output isn't as good.

So just another mid-tier flash model, nothing exciting, but Antigravity has very generous quotas so it's a good workhorse model when your Claude/OpenAI subs run out.

And whilst it's a fast model, having to baby sit through and approve prompts every few seconds ends up making it slower than the Auto approve modes of Claude/ChatGPT - they definitely need an auto approve mode.

Looks like Google's given up on frontier models for external consumption?

Gemini is getting less useful with each update. I could edit a pdf with the 3-pro model before but 3.1-pro couldn't edit the given pdf nor it could generate one for me.

One place where I find the Flash models surprisingly bad is Google Search's "AI Mode".

A recent example - I searched for how to unsubscribe from Pearson emails. Google Search "AI Mode" confidently gave me a sequence of steps along the lines of Settings > Profile > Email preferences > Unsubscribe.

Of course, I looked for an unsubscribe link before asking Google. None of those options existed. The correct answer was there is no way to unsubscribe through the account, so I just blockthe emails instead.

I've run into this pattern quite a few times. AI Mode seems to make up things all the time.

Not to rain on anyone's parade but I find it strange how excited and giddy people on HN get for any new X.X model releases. Pumping it straight to the top, clamoring to use it, check and compare benchmarks, bragging about it being your "daily driver"?

Are you people truly this excited about this crap? I mean I guess if you work for Google or Anthropic or whatever I could see it??? Otherwise, are these just bot comments?

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