Rendered at 19:30:19 GMT+0000 (Coordinated Universal Time) with Cloudflare Workers.
GodelNumbering 32 minutes ago [-]
Per million input/output tokens:
Gemini 2.5 flash: $0.30/$2.50
Gemini 3.0 flash preview: $0.50/$3.00
Gemini 3.5 flash: $1.50/$9.00
Interesting pricing direction. I don't think we have ever seen a 3x price increase for in the immediate next same-sized model (and lol @ 3 only ever getting a preview).
3.5 flash costs similar to Gemini 2.5 pro which was $1.25/$10
LetsGetTechnicl 5 minutes ago [-]
Gen AI is unprofitable, especially at the insanely cheap rates they've been offering to get people in the door. So expect more increases in the future.
doginasuit 13 minutes ago [-]
They probably never intended to keep serving cheap models. This is a natural way to introduce the squeeze, now that they have people who built services on their API. It makes a lot of sense to have an abstraction layer where the provider doesn't matter. If you are working in Kotlin, Koog is excellent.
dr_dshiv 18 minutes ago [-]
3.1 flash lite — $0.25/$1.50 — plus insanely fast.
3.1 flash lite isn’t quite as good as 3 flash preview (which is the most incredible cheap model… I really love it) — but 3.1 is half the price and the insane speed opens up different use cases.
For comparison, Opus models are $5/$25
fnordsensei 20 minutes ago [-]
3.5 flash is listed as stable rather than preview, or am I misreading?
Yeah, it is a massive jump in price, hardly a "Flash" model anymore... I wonder if they'll release flash lite or something with a bit more affordable price point.
rudedogg 23 minutes ago [-]
If Google is actually getting cheaper inference than everyone else with their TPUs, this smells like trouble to me. Maybe serving LLMs at a profit is proving difficult.
Or maybe they think because their benchmarks are good they can ramp up the prices. Seems like they don’t have the market share to justify a move like that yet to me.
IncreasePosts 19 minutes ago [-]
Maybe the margins are just very large for Google because they predict so much demand for 3.5?
GodelNumbering 15 minutes ago [-]
This combined with locally runnable models getting pretty good recently (e.g. Qwen 3.6) tells me that it's time to seriously consider local dev setup again
dbbk 23 minutes ago [-]
I don't think they're really comparable. Seems they created the Flash-Lite tier to take the spot of the old Flash models.
GodelNumbering 18 minutes ago [-]
No, 2.5 had both flash and flash lite.
SXX 1 hours ago [-]
> Create animated SVG of a frog on a boat rowing through jungle river. Single page self contained HTML page with SVG
Well, honestly this is quite impressive compared to 3.1 Flash Lite and 2.5 Pro. Considering that 2.5 Pro is actually quite good at generating massive amounts of code one shot.
captn3m0 1 hours ago [-]
All three links animate for me.
NitpickLawyer 56 minutes ago [-]
I think they mean the boat is moving. In the flash ones the paddles are animated but the boat is stationary for me.
codazoda 48 minutes ago [-]
The boat moves in all three for me
Fishkins 42 minutes ago [-]
The boat itself rocks, but do you see the background changing to indicate the boat is progressing through the environment? I only see that in the 3.1 Pro example. I believe that's what the OP meant.
Manuel_D 35 minutes ago [-]
I think this illustrates the problem with OP's prompt. If the goal is specifically to implement a scrolling background, this should have been in the prompt.
SXX 13 minutes ago [-]
Yup. My bad. It was just first idea that come to my mind since I enjoy visually compare each new release with unique prompts.
wslh 53 minutes ago [-]
Can you try with a more complex story such as "three little pigs"? I tried but it created a storybook instead of the SVG animation. I am looking to partially imitate Godogen [1][2] which is really great, even for animations.
It might indicate core model training and pre training is really slowing down?
OsrsNeedsf2P 51 minutes ago [-]
Beats 3.1 Pro for price per token, but artificial analysis is showing it's dumber per token and costs more overall
golfer 26 minutes ago [-]
Arena.ai is saying "Gemini 3.5 Flash’s pricing shifts the Pareto frontier in Text. 8 models from GoogleDeepMind dominate the Text Arena Pareto curve where only 4 labs are represented for top performance in their price tiers."
It’s not possible to uptrain on preview releases and it did not get that much love for a while.
golfer 25 minutes ago [-]
Arena.ai:
> Gemini 3.5 Flash’s pricing shifts the Pareto frontier in Text. 8 models from
GoogleDeepMind dominate the Text Arena Pareto curve where only 4 labs are represented for top performance in their price tiers.
"Flash-Lite" is a different product from "Flash", which is more expensive. They couldn't be more confusing with their naming though, especially since they have 3.1 Pro and not 3.1 Flash non-lite.
WarmWash 50 minutes ago [-]
I haven't used 3.5 at all yet, but previous Gemini (and Gemma models) are by far the most token light per task than any other model.
Cost per task is a more productive measure, but obviously a more difficult one to benchmark.
iwhalen 1 hours ago [-]
I wonder why they didn't discuss price in the post?
I don't think input/output pricing matters, 90% of the cost is cache. $0.15 is pretty good, but still very expensive.
simonw 6 minutes ago [-]
Gemini caching is confusing though:
$0.15 / million tokens
$1.00 / 1,000,000 tokens per hour (storage price)
I much prefer the OpenAI/DeepSeek way of pricing caching where you don't have to think about storage price at all - you pay for cached tokens if you reuse the same prefix within a (loosely defined) time period.
wolttam 1 hours ago [-]
It depends on the use-case. yes, 90% of cost is cache in agentic coding scenarios (actually 95% in my experience). But not when the model reasons for 200k+ tokens before answering a complex problem.
himata4113 58 minutes ago [-]
gemini models solve a problem in 80% less tokens so that's something to think about.
johaugum 8 minutes ago [-]
Source?
__jl__ 1 hours ago [-]
In our experience, caching is not very reliable with google. We always get random cache misses that don't happen with other providers. We find OpenAI, Anthropic and Fireworks (which we use a lot) all have higher cache hit rates. So it's not only about the costs of cached token but also what kind of cached hit rate you get.
svachalek 16 minutes ago [-]
In my experience Google is the most flaky in general, which is surprising considering the rock solid history of their search and other products. Just more likely not to respond at all, to give a response out of left field, to handle the same error in 12 different ways randomly (a rainbow of HTTP status codes and error messages), etc etc.
minimaxir 1 hours ago [-]
10% of input pricing is standard especially compared to competition.
himata4113 1 hours ago [-]
yah, which means that the input cost is the only value that should be paid attention to at the end + the cache discount (x10). If google would start offering x20 discount it would make it twice as cheap while input and output stayed the same.
John7878781 1 hours ago [-]
[deleted]
stri8ed 1 hours ago [-]
Output cost is 3x from Gemini 3 flash.
s3p 47 minutes ago [-]
Yikes. I think the concept of a 'flash' model is changing, no? Google used to market this as its lower-intelligence, faster, cheaper option. I appreciate that they are delivering on both of those, but personally I would appreciate if they could create an incremental knowledge improvement while holding price steady. Fortune 500 companies have to make their money I guess.
2001zhaozhao 9 minutes ago [-]
I think flash just means "fast" now
himata4113 1 hours ago [-]
Engineers at google have publically stated that the models are too big and are far from their potencial. Glad they're being proven right with every release.
They continue to focus on smaller models while openai and anthropic are increasing compute requirements for their SOTA models.
stri8ed 1 hours ago [-]
Given the cost increase associated with this model, and previous model releases, I think the size is trending upwards, not down.
himata4113 1 hours ago [-]
The speed says otherwise. I think they're increasing costs since they want to start seeing ROI.
JanSt 50 minutes ago [-]
Those are (mostly) new, faster TPU
himata4113 46 minutes ago [-]
latest TPU's appear to reach 800tok/s rather than the advertised 300tok/s.
Dinux 11 minutes ago [-]
Source please cause i dont believe that for once second
Jabbles 27 minutes ago [-]
> Engineers at google have publically stated that the models are too big and are far from their potencial
Can you link to a source?
howdareme 1 hours ago [-]
Google’s pro models are almost certainly bigger than Openai’s lol
fikama 11 minutes ago [-]
Why would that be? I am curious why do you think that.
maipen 1 hours ago [-]
Don’t let that fool yourself.
Google will have SOTA models as big as or even bigger than their competitors.
They are just refining their current models while they finish training the next generation.
They will all come out at about the same time. Anthropic, OpenAi, Google, xAI
ACCount37 1 hours ago [-]
Anthropic has been sitting on Mythos for a while now. I guess they don't feel pressured to fuck it ship it until anyone else gets a 10T to work.
That claim keeps contradicted hard by other parties, who say Mythos beats 5.5 resoundingly on both autonomous search and discovery and creation of complex exploit chains.
There might be a harness difference, but also, this CTF-type benchmark might not capture the capability difference fully.
Sevii 1 hours ago [-]
It's doubtful they have the compute to make mythos publicly available even after the SpaceX datacenter deal. And why sell it publicly if people are still willing to pay for Opus 4.7?
outside1234 47 minutes ago [-]
I suspect that Mythos doesn't have a business model that works
npn 33 minutes ago [-]
The price is crazy.
And I guess Gemini 3.5 pro will have the pricing increment, too. 12 x 5 = 60?
It seems like google does want us to use Chinese models.
aliljet 1 hours ago [-]
Is there a good benchmark tracking hallucinations? The models are all incredibly good now, even the open ones, and my hope is that the rate of hallucinations is something that's falling off in concert with larger and larger context lengths.
WarmWash 52 minutes ago [-]
People complain about them incessantly, but I can almost never get people to actually post receipts. Every provider allows sharing chats, and anyone can share a prompt that reliably produces hallucinations.
More often than not, people are using images in responses that go awry. Which is fair, the models are sold as multi-modal, but image analyses is still at gpt-4.0 text-analyses levels.
hibikir 39 seconds ago [-]
I see constant hallucination in claude code when using specific tooling: It thinks it knows aws cli, for instance, but there's some flags that don't exist, it attempts to use all the time in 4.6 and 4.7. When asked about it, it says that yes , the flag doesn't exist in that command, but it exists in a different command (which it does), and yet, it attempts to use it without extra info.
Claude also believes it knows how AWS' KMS works, quite confidently, while getting things wrong. I have a separate "this is how KMS replication actually works" file just to deal with its misconceptions.
For gemini, I typically use it to query information from large corpuses, but it often web searches and hallucinates instead of reading the actual corpus. On a book series, it will hallucinate chapters and events which, while reasonable and plausible, do not exist. "Go look at the files and see if your reference is correct" shows that it's not correct, and it's a mandatory step. But that doesn't prevent hallucination, but makes sure you catch it after the fact, just like a method in a class that doesn't exist gets found out by the compiler. The LLM still hallucinated it.
rjh29 15 minutes ago [-]
"People complain about them incessantly, but I can almost never get people to actually post receipts."
...my chats are all pretty long and involve personal conversations, or I've deleted them. It's a lot to ask for someone to post receipts. The number of complaints is enough data.
No matter how big the model is there will be edge cases where it has no data or is out of date. In these cases it just makes stuff up. You can detect it yourself by looking for words like usually or often when it states facts, e.g. "the mall often has a Starbucks." I asked it about a Genshin Impact character released in June 2025 and it consistently interpreted the name (Aino) as my player character because Aino wasn't in its data.
Honestly I'm surprised your haven't encountered it if you're using it more than casually. Pro is much better but not perfect.
saberience 43 minutes ago [-]
I see hallucinations ALL the time. It's only obvious when you're prompting about a subject you know well.
And when I say all the time, I mean it, and this is for Opus 4.7 Adaptive.
I often have to say, please do searches and cite sources, as if it doesn't it will confidently give me wrong or outdated information.
If you're often asking questions about a topic that's not in your specialist knowledge you won't notice them.
droidjj 13 minutes ago [-]
Hallucination is also much better controlled in the context of agentic coding because outputs can be validated by running the code (or linters/LSP). I almost never notice hallucinations when I’m coding with AI, but when using AI for legal work (my real job) it hallucinates constantly and perniciously because the hallucinations are subtle—e.g., making up a crucial fact about a real case.
It's a gibberish input detection benchmark, and does not measure output hallucinations.
Sevii 1 hours ago [-]
I haven't been bothered by hallucinations in premier models since early last year. Still see it in smaller local models though.
aliljet 1 hours ago [-]
I'm really running into this deep at the edges of content creation. Take, for example, a need to general some kind of legal work. The cost of painstakingly checking and rechecking each case cited is reducing the value of these frontier models immensely.
Coding, however, is solved like magic. Easier to add tests, to be fair.
FergusArgyll 39 minutes ago [-]
As long as the model uses web search, they almost never hallucinate anymore. The fast models (haiku, gpt-instant, flash) still sometimes have the problem where they don't search before answering so they can hallucinate
goldenarm 22 minutes ago [-]
I've seen chatGPT and Gemini hallucinate even from web search, it's better is not sufficient
if last year's models were the ones people got familiar with in late 2022, hallucinations would be an underrepresented rumor, there would be no articles about it because its so rare. overconfident lawyers wouldn't have messed up dockets in court with fake case law, in other domains that move faster, sources would be only partially outdated with agentic search and mcp servers filling in the gaps
AI psychosis would be the problem people talk about more, not just outright agreement but subtle ways of making you feel confident in your ideas. "yes, buy that domain name buy these other ones for defensibility"
(the domain name is dumb and completely unmarketable)
jampekka 40 minutes ago [-]
The models still hallucinate bad when called via APIs, especially if web search is not enabled. Gemini hallucinates quite frequently even with the app and search enabled. More recent (e.g. ChatGPT 5.x and Deepseek v4) prompts/harnesses search very aggressively, which does greatly mitigate hallucinations.
The Artificial Analysis benchmark results are pretty underwhelming. Roughly the same "intelligence" as MiMo-V2.5-Pro for over 3x the cost. We'll have to see how that translates to actual usage but it's not a great sign.
hydra-f 4 minutes ago [-]
That really depends on whether they have similar parameter counts, doesn't it? Unless you know that, the comparison is just strange
mixtureoftakes 1 hours ago [-]
benchmarks look REALLY good, the price hike is big but it also beats sonnet 4.6 in every discipline?
1 hours ago [-]
swe_dima 1 hours ago [-]
Flash family but costs like a Pro. $9 vs $12 for output.
1 hours ago [-]
hubraumhugo 36 minutes ago [-]
Just updated my HN Wrapped project with it and it does well on my totally unscientific LLM humor benchmark: https://hn-wrapped.kadoa.com
eis 1 hours ago [-]
3.5 Flash was more expensive than 3.1 Pro to run the Artifical Analysis test suite. $1551 for 3.5 Flash [0] vs $892 for 3.1 Pro [1]. That's 74% more cost while ranking lower. It's 2.5x as fast but I don't think the bang for the buck is there anymore like it was with 3.0 Flash. I'm a bit bummed out to be honest.
I did not expect such a huge (3x) price increase from 3.0 Flash and I bet many people will not just blindly upgrade as the value proposition is widely different.
One interesting point to note is that Google marked the model as Stable in contrast to nearly everything else being perpetually set as Preview.
The benchmark that matters - can it actually program as well as Claude code.
If not then I’m not using it.
Cancelled my account 3 months ago, only Claude code level capability would bring me back.
1 hours ago [-]
alexdns 2 hours ago [-]
Its Gemini 3.5 Flash
nerdalytics 1 hours ago [-]
Yeah, Google chose a misleading title for the blog post.
jader201 4 minutes ago [-]
> Today, we’re introducing Gemini 3.5, our latest family of models combining frontier intelligence with action. This represents a major leap forward in building more capable, intelligent agents. We’re kicking off the series by releasing 3.5 Flash.
simianwords 36 minutes ago [-]
No one talking about how this flash Beats Pro? Imagine what 3.5 pro looks like?
Also concerned about Gemini models being benchmaxxed generally
NitpickLawyer 18 minutes ago [-]
> concerned about Gemini models being benchmaxxed generally
I would say they are the least benchmaxxed out of all the top labs, for coding. They've always been behind opus/gpt-xhigh for agentic stuff (mostly because of poor tool use), but in raw coding tasks and ability to take a paper/blog/idea and implement it, they've been punching above their benchmarks ever since 2.5. I would still take 2.5 over all the "chinese model beats opus" if I could run that locally, tbh.
f311a 2 hours ago [-]
$9/1M output
explosion-s 2 hours ago [-]
I wonder if this is because it's a larger model or maybe just because they can? Although with the latest Deepseek it's really tough to compete pricing wise. Inference speed and integration (e.g. Antigravity) might be their only hope here
nightski 1 hours ago [-]
AI being a product is not the future. It's more like an operating system that deserves to be open and free (aka Linux). Unless that happens we are in for a very dystopian future. I wish I had the intelligence, resources and/or connections to try and make that happen.
HardCodedBias 57 minutes ago [-]
Oh boy.
GDM is making (or has been backed into a corner into making) the bet that high throughput, low latency, low capability models are the path forward.
That probably works for vibe coded apps by non-practitioners.
I suspect that practitioners/professionals will wait longer for better results.
brokencode 46 minutes ago [-]
Where do you see that it’s low capability?
And Google is trying to make something affordable enough for a mass market, ad-supported audience.
They aren’t hyper focused on enterprise like Anthropic is. And that’s okay. There’s room for different players in different markets.
bakugo 1 hours ago [-]
Triple the price of the last Flash model ($3 -> $9 per 1M output). Quickly approaching Sonnet prices.
Feels like the AI pricing noose is tightening sooner rather than later.
2 hours ago [-]
llmslave 26 minutes ago [-]
Conspiracy theory:
This model isnt an advancement, its a previous model that runs more compute, which is why it costs more
npn 22 minutes ago [-]
Nah, it costs what you are willing to pay.
cesarvarela 1 hours ago [-]
Add Flash to the title, please.
meetpateltech 53 minutes ago [-]
edited it.
mugivarra69 1 hours ago [-]
[dead]
warthog 38 minutes ago [-]
GPT-5.5 on the benchmarks still seem to perform better than this
Plus the vibe of the gemini models are so weird particularly when it comes to tool calling
At this point I kinda need them to shock me to make the switch
benbencodes 1 hours ago [-]
Pricing is now live on ai.google.dev/pricing:
Gemini 3.5 Flash: $0.75 input / $4.50 output per 1M tokens, 1M context window. The output price explicitly "includes thinking tokens" — which is why it's higher than a typical flash-class model.
For comparison within the Gemini lineup:
- Gemini 2.5 Flash: $0.30 / $2.50
- Gemini 3.1 Flash-Lite: $0.25 / $1.50
- Gemini 3.1 Pro Preview: $2.00 / $12.00
So 3.5 Flash is ~2.5x more expensive input vs 2.5 Flash. The pricing and "including thinking tokens" framing position it as a reasoning-capable flash model rather than just a pure speed optimization.
lyjackal 1 hours ago [-]
You’re quoting the batch pricing. On demand is 1.5 per input and 9 per M output. This is effectively comparable cost to Gemini 2.5 Pro in a flash tier model
ls_stats 58 minutes ago [-]
You are seeing batch inference, standard inference is $1.5/$9.
I was excited until I saw that price.
conorh 1 hours ago [-]
I think you have your pricing wrong there, Gemini 3.5 flash is $1.50 input and $9 output.
mchusma 57 minutes ago [-]
Okay, it's kind of somewhere between haiku and sonnet level pricing, at somewhere between sonnet and opus level performance. Its a great option. I was hoping to see opus class intelligence at haiku level pricing out of google, and this is close to that!
mchusma 47 minutes ago [-]
Never mind, after looking at more benchmarks, seems closer to sonnet level intelligence at slightly lower cost. Speed is great for latency sensitive applications, but if this was 1/2 the cost it would have been priced to win.
If this is the big model release out of google, its a disappointent.
Tiberium 44 minutes ago [-]
Please delete/edit your AI-written and factually wrong post.
Gemini 2.5 flash: $0.30/$2.50
Gemini 3.0 flash preview: $0.50/$3.00
Gemini 3.5 flash: $1.50/$9.00
Interesting pricing direction. I don't think we have ever seen a 3x price increase for in the immediate next same-sized model (and lol @ 3 only ever getting a preview).
3.5 flash costs similar to Gemini 2.5 pro which was $1.25/$10
3.1 flash lite isn’t quite as good as 3 flash preview (which is the most incredible cheap model… I really love it) — but 3.1 is half the price and the insane speed opens up different use cases.
For comparison, Opus models are $5/$25
https://ai.google.dev/gemini-api/docs/models/gemini-3.5-flas...
Or maybe they think because their benchmarks are good they can ramp up the prices. Seems like they don’t have the market share to justify a move like that yet to me.
https://gistpreview.github.io/?5c9858fd2057e678b55d563d9bff0...
3.5 Flash: Thinking High - 7280 tokens
https://gistpreview.github.io/?1cab3d70064349d08cf5952cdc165...
3.1 Pro - 28,258 tokens
https://gistpreview.github.io/?6bf3da2f80487608b9525bce53018...
Though 3.1 took 3 minutes of thinking to generate, but it only one that got animated movement.
https://gistpreview.github.io/?3496285c5dac5ba10ebbc0b201a1a...
Gemini 2.5 Pro - 5,325 tokens:
https://gistpreview.github.io/?cc5e0fefeaaffecd228c16c95e736...
Gemini 2.5 Flash - 7,556 tokens:
https://gistpreview.github.io/?263d6058fe526a62b8f270f0620ec...
https://gistpreview.github.io/?da742884e5e830ce71ee4db877519...
OFC this is just for fun, but nevertheless gave me working code on first try.
https://claude.ai/public/artifacts/128ebe5a-add7-406a-9bce-6...
8112 tokens @ 52.97 TPS, 0.85s TTFT
https://gistpreview.github.io/?7bdefff99aca89d1bc12405323bd4...
Full session: https://gist.github.com/abtinf/7bdefff99aca89d1bc12405323bd4...
Generated with LM Studio on a Macbook Pro M2 Max
https://huggingface.co/hesamation/Qwen3.6-35B-A3B-Claude-4.6...
[1] https://github.com/htdt/godogen
[2] https://drive.google.com/file/d/1ozZmWcSwieZQG0muYjbj7Xjhhlz...
Latest update: May 2026
I have a very bad feeling about this lag.
https://x.com/arena/status/2056793180998361233
It’s not possible to uptrain on preview releases and it did not get that much love for a while.
> Gemini 3.5 Flash’s pricing shifts the Pareto frontier in Text. 8 models from GoogleDeepMind dominate the Text Arena Pareto curve where only 4 labs are represented for top performance in their price tiers.
https://x.com/arena/status/2056793180998361233
6x the price of 3.1 flash lite
Cost per task is a more productive measure, but obviously a more difficult one to benchmark.
Compare to the GPT-5.5 announcement: https://openai.com/index/introducing-gpt-5-5/
They continue to focus on smaller models while openai and anthropic are increasing compute requirements for their SOTA models.
Can you link to a source?
They are just refining their current models while they finish training the next generation.
They will all come out at about the same time. Anthropic, OpenAi, Google, xAI
Hold on, I think this claim needs some hard data. Here you go gentlemen:
https://www.aisi.gov.uk/blog/our-evaluation-of-openais-gpt-5...
There might be a harness difference, but also, this CTF-type benchmark might not capture the capability difference fully.
And I guess Gemini 3.5 pro will have the pricing increment, too. 12 x 5 = 60?
It seems like google does want us to use Chinese models.
More often than not, people are using images in responses that go awry. Which is fair, the models are sold as multi-modal, but image analyses is still at gpt-4.0 text-analyses levels.
Claude also believes it knows how AWS' KMS works, quite confidently, while getting things wrong. I have a separate "this is how KMS replication actually works" file just to deal with its misconceptions.
For gemini, I typically use it to query information from large corpuses, but it often web searches and hallucinates instead of reading the actual corpus. On a book series, it will hallucinate chapters and events which, while reasonable and plausible, do not exist. "Go look at the files and see if your reference is correct" shows that it's not correct, and it's a mandatory step. But that doesn't prevent hallucination, but makes sure you catch it after the fact, just like a method in a class that doesn't exist gets found out by the compiler. The LLM still hallucinated it.
...my chats are all pretty long and involve personal conversations, or I've deleted them. It's a lot to ask for someone to post receipts. The number of complaints is enough data.
No matter how big the model is there will be edge cases where it has no data or is out of date. In these cases it just makes stuff up. You can detect it yourself by looking for words like usually or often when it states facts, e.g. "the mall often has a Starbucks." I asked it about a Genshin Impact character released in June 2025 and it consistently interpreted the name (Aino) as my player character because Aino wasn't in its data.
Honestly I'm surprised your haven't encountered it if you're using it more than casually. Pro is much better but not perfect.
And when I say all the time, I mean it, and this is for Opus 4.7 Adaptive.
I often have to say, please do searches and cite sources, as if it doesn't it will confidently give me wrong or outdated information.
If you're often asking questions about a topic that's not in your specialist knowledge you won't notice them.
Coding, however, is solved like magic. Easier to add tests, to be fair.
AI psychosis would be the problem people talk about more, not just outright agreement but subtle ways of making you feel confident in your ideas. "yes, buy that domain name buy these other ones for defensibility"
(the domain name is dumb and completely unmarketable)
https://storage.googleapis.com/gweb-uniblog-publish-prod/ori...
I did not expect such a huge (3x) price increase from 3.0 Flash and I bet many people will not just blindly upgrade as the value proposition is widely different.
One interesting point to note is that Google marked the model as Stable in contrast to nearly everything else being perpetually set as Preview.
[0] https://artificialanalysis.ai/models/gemini-3-5-flash [1] https://artificialanalysis.ai/models/gemini-3-1-pro-preview
That's everything I needed to know.
Does that mean this model is production ready?
[0] https://news.ycombinator.com/item?id=47076484
If not then I’m not using it.
Cancelled my account 3 months ago, only Claude code level capability would bring me back.
Also concerned about Gemini models being benchmaxxed generally
I would say they are the least benchmaxxed out of all the top labs, for coding. They've always been behind opus/gpt-xhigh for agentic stuff (mostly because of poor tool use), but in raw coding tasks and ability to take a paper/blog/idea and implement it, they've been punching above their benchmarks ever since 2.5. I would still take 2.5 over all the "chinese model beats opus" if I could run that locally, tbh.
GDM is making (or has been backed into a corner into making) the bet that high throughput, low latency, low capability models are the path forward.
That probably works for vibe coded apps by non-practitioners.
I suspect that practitioners/professionals will wait longer for better results.
And Google is trying to make something affordable enough for a mass market, ad-supported audience.
They aren’t hyper focused on enterprise like Anthropic is. And that’s okay. There’s room for different players in different markets.
Feels like the AI pricing noose is tightening sooner rather than later.
This model isnt an advancement, its a previous model that runs more compute, which is why it costs more
Plus the vibe of the gemini models are so weird particularly when it comes to tool calling
At this point I kinda need them to shock me to make the switch
Gemini 3.5 Flash: $0.75 input / $4.50 output per 1M tokens, 1M context window. The output price explicitly "includes thinking tokens" — which is why it's higher than a typical flash-class model.
For comparison within the Gemini lineup: - Gemini 2.5 Flash: $0.30 / $2.50 - Gemini 3.1 Flash-Lite: $0.25 / $1.50 - Gemini 3.1 Pro Preview: $2.00 / $12.00
So 3.5 Flash is ~2.5x more expensive input vs 2.5 Flash. The pricing and "including thinking tokens" framing position it as a reasoning-capable flash model rather than just a pure speed optimization.
If this is the big model release out of google, its a disappointent.
(I suspect you're viewing the "flex" pricing).