Posted by logickkk1 6 hours ago
Going on baseless speculation, the lack of accompanying pro models with these flash releases either means: 1) the model is too big to be economical, 2) google doesn't have the compute to serve the big model, 3) their big model has too many alignment issues to serve to the public.
edit: looks like benchmarks are up on https://artificialanalysis.ai/models/gemini-3-6-flash. It's solidly middle-of-pack. However, if you want to be most fair to flash, look at the intelligence vs time per task and intelligence vs outputspeed benchmarks. This is a very fast model.
edit 2: I use antigravity from time to time and in my experience, 3.5 flash is an underrated model, so long as you know what it's good for. It's very good at frontend (much better than gpt 5.5) and it's fast, so it's a great tool for iteration. I expect 3.6 to be no different.
For small models (which are probably distilled from their big ones) you can serve them economically all the time and not hemorrhage money.
DeepSeek didn't do to OpenAI and Anthropic what nearly everybody claimed they would.
Every single person on HN that loudly proclaimed the end was nigh for GPT & Co. due to DeepSeek, was wrong. They were humiliatingly wrong, and they'll never own up to it. The reason those people were so very wrong, is the same exact reason the Kimi crowd is wrong now. And it's very obvious that they're wrong, but they have intense emotional blinders on. Their thinking process is hyper emotionalism: they want a certain outcome, regardless of if reality aligns to that or not. They're making emotional wishes about how they want things to turn out, and pretending those magic wishes are grounded in reason.
It takes enormous resources to run something equivalent to GPT 5.6 or Fable. Nobody can or wants to do that outside of very limited situations - if you can just reasonably pay as you go instead. As it turns out, you can just pay as you go with GPT and Fable. Their businesses have gotten radically larger since DeepSeek launched. Get it yet?
Domestic China is the only very large audience for their own models, so long as OpenAI and Anthropic stay top tier.
All the hype online from the forums about Kimi, is worthless: those people hyping it can't even come close to running it locally, which is the fantasy. So why are they hyping it? Why did they hype DeepSeek just the same, and learn nothing from its total failure to actually take down OpenAI and Anthropic? Rhetorical questions with obvious answers.
Kimi poses zero actual threat to OpenAI and Anthropic. Those companies will continue to pile up the subscriptions and API usage. Check out GPT's subscriber base today vs when DeepSeek launched. Get it yet? When Model X launches out of China in a year, we'll have this same conversations all over again, and the hypsters will have learned nothing.
While the Kimi fawning is endless, OpenAI will just keep piling up subscriber counts, and Anthropic will keep piling up API usage. Then OpenAI is going to staple a gigantic ad system onto GPT. China can't compete in the model-as-a-service business globally, for the exact same reason they failed so miserably to compete in search globally.
I don't think so. US models are very expensive, and not available in every country. I am not willing to pay $50/1M tokens for writing my pet projects.
the reality is one way or another that as long as there exists an alternative that a USA company could serve with the same compute rented from hyperscalers, this represents a threat, even if the extent to which is unknown
I think the reason OpenAI and Anthropic stay ahead in revenues right now is because the models are improving too quickly to reliably compete with them on cost.
But, once model performance reaches a plateau -- they have to at some point, though perhaps years away -- that's when ability to operate compute infrastructure at scale becomes the secret sauce.
The big AI labs are likely safe until models stop improving fast enough to protect them from competition on cost.
This similar pattern has repeated in most technical booms prior to this.
When hard drive technology was improving fast enough that old hard drives were quickly obsolete, IBM could maintain good margins making hard drives. But once hard drives got good enough and advances were slow enough that innovation was not the only factor considered by drive purchasers, commodity hard drives started to take over and IBM had to exit those businesses.
The same is likely to happen once model improvement slows.
Damnit, I usually don't jump to LLM speech patterns, but this opening had me thinking you were a bot. But after checking your profile, I think you pass as human. I wonder when will be the time, this does not work anymore for me. (Creation date is a strong hint, but abandoned accounts can be hijacked)
My view then was they are optimising the models for inference ability on their own hardware AND use cases, which is often speed and time to first token.
They've somehow seemed to end up with terrible compute shortages, which again is surprising given how good Google is at infra deployments AND have their own hardware. From rumors out there they are turning down enterprise deals for Gemini because they don't have the compute.
The problem is they're falling further and further behind on frontier class on coding especially, and since I wrote that article it's got even worse with open weights models undercutting them on price AND intelligence.
It would be a shame if they cannot beat Kimi K3 or Qwen3.8 Max, both of which are claimed to be Fable-like. If that is true, it will be [or would be] the first time a major American lab falls behind a Chinese competitor.
China can keep up because it's cheaper to run a frontier lab there. They also have more researchers and a stronger cultural inclination for this sort of thing. And I guess the business case in China doesn't have to work as well as it does in the US.
Speed as a differentiator has always been Google's thing. They (used to?) show the microseconds it took to query & rank web-scale search results. Chrome, notoriously, focused on speed at the expense of resource use. The very many efforts to efficiently speed up Android & its runtime since its inception, and so on...
> their big model underperforms chatgpt 5.6
Possible but TFA claims:
We have started our most ambitious pre-training run yet, for Gemini 4 ...Paywalled article, but the headline is basically all you need: https://www.bloomberg.com/news/articles/2026-07-16/google-ge...
4) googles big model just performs worse than K3 and GLM so they choose not to embarass themself.
Like I love Gemini and use it a lot to one-shot whole MR with huge contexts, but its just much worse when its come to tool use and agentic coding.
Fast, light weight, ok intelligence. Perfect for serving 20B+ prompts per day mostly surrounding banal human things.
OAI and Anthropic's cloud spend can cover the revenue gap, as Google is already capturing a large chunk of those guy's revenue.
https://huggingface.co/microsoft/bitnet-embedding-0.6b
It’s a small multilingual embedding model designed for things like search, RAG, and semantic similarity. It supports a fairly large context window and is designed to run efficiently on a CPU in a GPU starved world.
The interesting part is that it builds on BitNet, using ternary weights of -1, 0, and 1 instead of the usual floating-point weights. That should make indexing and searching large amounts of text much cheaper without giving up too much accuracy.
From the outside they look like they're behind in terms of frontier models, but I think they might be the best positioned to not go out of business when the bubble pops.
Also look at the fact that they've been able to deploy AI-assisted search at google scale. It must be another order of magnitude larger (at least) than the model deployments for OpenAI and Anthropic.
Of course unless you're inside Google it's impossible to know for sure.
Lest we forget, "Attention is All You Need" came from Google.
It also came directly from the university of Toronto, and the university of Toronto seeded all American frontier labs (including Grok (why do you think they could start so fast))
I do not think OpenAI or Anthropic are actively chasing margins - though, Anthropic is supposed to be profitable on some form of non-GAAP accounting...
I suspect Google isn't really interested in seeing how far it can get dragged into a race of selling dollars for $0.25, and is more interested to see if it can stay in the race selling $0.50 for a dollar - when everyone else is losing or barely breaking even.
Maybe they don't want to price war with the other labs so they can comfortably maintain healthy margins on selling them compute?
Porting CUDA-based research, debugging, and overall experimentation speed is likely slower.
The GPU is still king for training.
Yes, subs like codex are heavily subsidized. But API billing has massive margins and that's what enterprises pay.
https://www.bloomberg.com/news/articles/2025-12-21/openai-se...
As for Anthropic, the rumors I remember seeing for their API margins were more like 85-90%, but I don't have a reference at hand for those. But once you know the API is wildly profitable and the subscriptions are roughly break-even and not even a big slice of their income, all of the investment makes a lot more sense.
Rumors say 4) it didn't perform well, especially in coding so has been delayed
They literally forced me and my company out of Antigravity by phasing out AI Ultra subscription without any proper product follow-up. Antigravity IDE cannot even have poweruser subscriptions now from Google Workspace an Gemini Enterprise Agent Platform cannot be attached to Antigravity IDE.
Gemini Enterprise Agent Platform has an incredibly abysmal setup process, and if I want to limit spending per-user I have to create projects per user. The fact that you cannot activate Anthropic models on it if the billing still has free credits is almost a joke.
I was a big proponent of Google and Gemini, but they left us reeling with their abrupt product decisions. Forced us to buy $200 subscriptions directly from Anthropic/OpenAI.
The GCP team wants their slice, the other team wants some otjer slice, and so on. Everyone wants some crap for their promotion package.
It's no wonder Meta has shit the bed even worse.
It's also why Google still releases actually decent, useful models despite the product being such a hilarious mess. A lot of the time Gemini models have actually been better as production LLMs as part of LLM-based production applications than OpenAI and Anthropic models when it comes to the complete cost:quality:latency:adherence picture. And they still are. We have products in prod that use Gemini because they're better than any other model at the specific task. But we wouldn't dare use it for anything coding related, or even just as productivity tool to rely on, because as a consumer product it's a joke.
I got a Google One plan for Gemini, but it came bundled with YT Premium lite, and that somehow made it impossible to renew YT Premium for 30 days. I suspect different teams stealing customers from each other.
Like when you try to give Google money they try to squeeze you as much as possible.
At the same time you can get 5 time more limits for free just by registering 10 free Google accounts.
Google subscriptions are one big mess.
Haven't done any serious coding work with the flash models though — but I'm seeing more and more HN comments from people who seem to have picked it up for that in the last couple of months.
When they swapped Google Assistant for Gemini as the default voice provider in Android Auto it was so annoying. My wife's non-work space account can get Gemini to do the normal things like play music and what not, but my Workspace one can't do much of anything at all. I can talk about nearly any random topic with it, but getting it to change the playlist, nah, can't help you there.
It's no surprise to me to see them fumble actually supporting a lot of the consumer features of Gemini into Workspace.
If it weren't for the $10 GCP credit, I'd straight away cancel it. I don't see enough value in Gemini to justify the $20 subscription.
Build a whole new management tree - the current people all do a terrible job.
I have multiple anthropic and OpenAI max plans. For Gemini I just use my Cursor $200 a month plan (which also gives me the ability to try grok, conductor, etc)
They're not even benchmarking against other models now, just against themselves - which tells you everything you need to know.
There is absolutely no loyalty when it comes to coding. Nothing could be more common than people threating to jump ship whenever another frontier or open source model comes.
Google is clearly able to keep growing their free and consumer and small business use cases. Unlike corporate coding, we actually have evidence that solo and small businesses can actually see productivity gains.
Anthropic and OpenAI need to stay dancing like mad, because it's their source revenue which underpins their investments.
Why does Google need to shove something at the top at the same desperate cadence? Other than "recursive self improvement leads to AGI" it seems perfectly fine if they push out something dramatically better every year and half.
You should be happy for them.
This is still very early days. Who is "on top" has flipped back and forth many times already. The next frontier model release (from whomever) will change things again.
Claude Code has largely won individual developer mindshare and has been on top ever since it came out. The benchmarks change, but almost nobody opts to use anything other than Claude IME when I ask them. Enterprise is more competitive since they care about costs and other things, but developers leaning towards Claude puts a thumb on the scales there.
The product doesn't have much lock in, so it is possible to dislodge Claude, and Anthropic could (and some may argue is likely to) just shoot themselves in the foot again and again and again, but Google has never been particularly good at enterprise sales, and they have never actually been at the frontier of intelligence.
I think Google's incentives have mostly about building models for their products, which makes them focus more on the cheap end, and while they need that, it feels like the Innovator's Dilemma is biting them here.
I own a lot of Google stock from working there in the past and have been quite happy about their trajectory up until the last 6 months, but I am getting pretty antsy about their AI story these days.
Claude Code's success is not due to the agent but because the model is considered the best for programming and is very heavily subsidized, compared to pay as you go API prices. Consumers and Enterprise are not really locked in and will go where it makes the most sense.
I think they have almost no loyalty by actual developers.
When we work trial people, 100% of people ask for Claude rather than Codex or anything else.
When I talk to people at non-AI tech events everyone basically says they use Claude and have not tried an alternative.
Developers writ large are actually not that interested in trying multiple tools, they like customizing their chosen tool and tweaking it forever.
I think developers are as susceptible to brand marketing as everyone else. It's why almost everyone has a Macbook.
Basically you may choose to drink brand A water bottle, brand B water bottle or tap water. Oh and you might choose the glass water bottle if you use API/Fable.
It's not true, it's just a play for margin.
Where does that 90% figure come from?
At this rate, if Google has a flagship model, you're better off plugging it into a competitor's tooling than hope Google figures out how to use it.
And the real numbers could be better for Anthropic. It's feasible Opus models are actually cheaper to serve than GLM 5.2 because Anthropic have optimized the hell out of inference.
I guess that’s the big question, will people pay a big margin long term to use their end products / models or will AI tokens be commoditized by many competing players. For coding if I had to pay API costs I’d switch in a heartbeat, enterprise maybe more reluctant?
anthropic probably has more customers that use more of their sub, but for open ai where a lot of their subs are consumers through chatgpt.com, they have a lot of free money to work with there
They are not allowing me to hit their endpoints which agy hits - it's frustrating . i tried to hack it with gemini itself. what i love about gemini is it's so encouraging and ready to help you - even against the agy client : ) .
Even though im so frustrated with this - i still love Gemini for some reason ! Most encouraging model in the world!
Likewise. This seems like a common feel. I have at least spent $4000 and likely a lot more on Gemini API because I really wanted them to win. I gave up.
Why do you care? Why would you spend your own money to a multi trillion dollar company so that they win their own "war" against another multi trillion dollar company?
Please don't get me wrong, I know the question can seem a bit negative, I am really just curious.
Although, I think saying 'wanted them to win' was not accurate. More like, I stuck with them hoping it will get better, and it did get better in many ways, coding was not one of them.
That being said, controlling android and apple mobile devices is kind of a big deal. And their video models are still top notch.
Google's biggest and most important customer for all this AI stuff is google. Do they actually want other customers, or is having other people use their AI just an annoyance at this point, where we use up compute that they'd rather use internally...
* Abruptly ban me and all users from a Google Workspace for no reason whatsoever.
* Abruptly shut down a service.
I'll never give Google my direct money.
It’s so silly that individual people still use their shit. Corporations, I understand - they always choose the most mediocre stacks and tools by default. But why people choose to bring the mediocrity of Google into their lives is beyond me.
This is what happens when you put a McKinsey consultant in the role of CEO of an organization where product managers run the asylum instead of engineers.
HN lives in a bubble.
I have German/Italian/Polish clients virtually all use Gemini and NotebookLM. Talking insurance, banking, consulting, legal.
The real world doesn't look at pointless benchmarks on writing react tailwind crap, they are already google suite users, get the tools, test them and adopt them, end of story.
It's going to be like with angular, never mentioned on the net, widely used in the real world.
Needless to say 3.5 was a disappointment. Curious to see 3.6.
That sounds awful.
For those of us who don't follow the AI hype cycle, what does that have to do with the topic of this thread: Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber?
That is, given a lot of users' contexts, the way they can and the way they want to use these models are increasingly disjoint.
What is Google's recently released AI coding product?
It seemed for a time that Google had finally gotten the ball rolling, but I'm doubting that more and more as time passes. We'll see what happens with 3.5 pro I suppose.
I record whether the answers are correct, and the generation stats (costs, latencies, tokens used, etc.).
I have no idea why the Gemini models do so well.
I have recently added new tests, whose sole purpose was to find some cases on which Gemini 3 Flash fails (I don't like cherry-picking models or tests, but I also find it strange Gemini Flash models leading in accuracy). I made a more complex coding/tool-usage test, that I expected it to fail, it did fail it once locally in my debug tests, but when I finalized the test and ran the entire testing suite for all models, somehow Gemini 3 Flash still got it right...
Gemini models are REALLY intelligent (and they are actually my favorite model to use via the chat app to ask questions), but they somehow fail in real-word coding tasks where they have to modify files, check results, debug, etc.
My tests harness provides a lot of mock data, and limits the number of actions a model can choose from. I am starting to think that maybe the models are not bad, just that the coding harness are not optimized for those type of models, and Google doesn't really provide their own "Codex".
So yes, Gemini models are at the top, even if I actually (not proud of it) tried to make tests that actually favour other coding-focused models.
Also, would be great if you could add GPT 5.6 Sol XHigh and Fable 5 High as well, just to see if at least those beat Gemini which is currently your #1.
> would be great if you could add GPT 5.6 Sol XHigh and Fable 5 High as well
I would like too, but I avoided them for several reasons:
1) Cost - this is a hobby project, those models would cost tens of dollars for each benchmark run, multiply this by tens or hundreds of models and ...
2) Time - the high models are already taking a really long answer to respond (5-10minutes per question). I run each question with 3 repeats (run the same test three times), so it would take 30 minutes per test. If I change my tests, methodology, or add a new test, it would take a really long time to run the benchmark. Also, I like having results immediately when a new model is released, now I can post within 30 minutes of a model's release the benchmark results.
3) High reasoning usually does WORSE on most tests - if you look at the leaderboard, it's sometimes counter-intuitive, but models with high or max reasoning usually do worse than medium and low. This is because the questions are quite targeted/direct, and the models overthink the question and miss the solution. Or the long thinking context makes them perform poorly. The generation tasks (SVGs/HTML animation) are usually better with longer reasoning, but short code fixes, trivia questions, puzzles, etc. are answered by low/med reasoning with more accuracy in general
Also, Fable is borderline un-testable, it refuses to answer many questions, so it scores poorly anyway.
Gemini scores 21/22 because it answers all tests, and it does them correctly, consistently. The only failed test is I think because it miscounted the lines in a file, when responding on which line the bug was in a code snippet.
As far as I can tell it's slightly better than GLM 5.2.
GLM defaults to max effort btw
https://docs.together.ai/docs/glm-5.2-quickstart#reasoning-e...
Light. Lite is product marketing seepage.
Disheartening, but not surprising: the comparison would not be very flattering for Google.
https://tools.simonwillison.net/markdown-svg-renderer#url=ht...
They're easy enough to skip - click the little "-" icon and you'll collapse the entire sub-thread.
It's a decent heuristic because the better models generate better pelicans. That's all. Nobody sane is going to make a bet on a model based on a pelican. But it's cool, it's tradition by now, and it's a semblance of a good first impression for new models.
thank you.
"He doesn't have a tin can in your face shouting from a soapbox that makes it hard to ignore."
He metaphorically does because people upvote his pelicans to the top and the ensuing comment threads are massive/bloated. Huge amounts of the readership of this website are lurkers who don't even know how to hide these giant posts. Look at how bloated this very thread is right now!
Also, a lot of people unironically are whining about him because of sour grapes. Pay them what Simon is likely making, give them as much mindshare/attention as Simon gets, and they wouldn't be so mad.
Anti-incumbency bias and anti-elitist attitudes are good actually.
Agreed, but scrolling past or hitting - were apparently off the table for the complainers. So flagging was yet another tool in their toolbelt and I bet a powerful one at that. If simonw's pelican posts routinely went dead from flagging, he would not make them. You know that, I know that.
> The pelican test continuing to be taken seriously is a great example of that kind of echo chamber.
You can try and support that argument if you like. But I would implore you to realize that it has been had many times recently and the other side does in fact find value and do not see it that way.
> He metaphorically does because people upvote his pelicans to the top and the ensuing comment threads are massive/bloated.
Users upvote the pelicans because they find it interesting. they arent paid trolls or simonw fanatics.
> Huge amounts of the readership of this website are lurkers who don't even know how to hide these giant posts.
They can learn... it's called hackernews. For those interested, that is what the [-] link is for above the comment. Use it and move on.
But, also, as said, you're an industry (and foss!) veteran, so I find it impossible to believe that you haven't had your fair share of baseless bullshit being thrown at you, and with that, you gaining a persona that will not be hit by that, because it clearly knows that it is in fact bullshit.
Unless of course it doesn't really know that with certainty.
As said, I would _love_ to give you the benefit of the doubt, because you might just have a stressful day or whatever, but content marketing is literally your whole thing by now. It is impossible for me to do that with a clean conscience.
Your blog front page currently opens with
> Earlier this month I hosted a fireside chat session at the AI Engineer World’s Fair with Cat Wu and Thariq Shihipar from Anthropic’s Claude Code team.
That is not what "some rando foss maintainer we are morally obligated to be soft with" does.
But I repeat myself.
I think very hard about the ethics of what I'm doing and how I can best use my "platform" (shudder again) in as constructive a way as possible.
I'd rather you do this than him. Chill out, please. The pelican pic is fine.
There's nothing shady here. The disclosure is front and center on his About page on his website.
He's not spamming you. It's one short link, sometimes a link to a first-impressions post. It's interesting and useful for me and the other commenters who keep upvoting his comments. Why are you so antagonistic?
All of the pelicans so far have had really weird flaws / quirks so I am always a little interested to see how well these models perform at this task, since I've seen all the past pelicans and have some anchoring.
Seeing a truly flawless pelican would tell me that the model has true visual reasoning capabilities as well as good taste.
Not only does it give you a super easy-to-grok understanding of the model quality just by looking at the image, but when you compare tokens and costs (both input and output), you really get a good, simple COST x QUALITY evaluation across models.
Simon explains it well: https://simonwillison.net/2026/Jul/16/kimi-k3/#what-can-we-l...
Simon, you should put up a summary table page that you update after every release.
I agree to rednb that at this point it feels like rather obvious brand building, but also, I agree with you that some value is in it.
It does not feel all that authentic though, and it's good to react allergically to lack of authenticity. Bad for a lot of business models, but good for humanity.
> It does not feel all that authentic though, and it's good to react allergically to lack of authenticity. Bad for a lot of business models, but good for humanity.
I hope SimonW keeps them coming.
I get people burning out on the pelican SVG test alongside the rest of the AI burnout, but I guess for myself I'm just choosing to keep enjoying it while I still can.
But why is this an indication of literally anything else?
The 3.6 Flash pelican is just about the best I've seen.
Sponsored blogs and paid newsletters are after all, notoriously poor at subsisting on silence :)
The fish and the cap where always added when I asked an llm to improve it's first attempt.
This continues the trend in LLM progress of better=more stuff
Edit: I wonder if this is a function of the reasoning training, where more tokens/ stuff is rewarded.
2.5 Flash: $0.3 / $2.5
3.0 Flash: $0.5 / $3
3.5 Flash: $1.5 / $9
3.6 Flash: $1.5 / $7.5
---
2.5 Flash-Lite: $0.1 / $0.4
3.1 Flash-Lite: $0.25 / $1.5
3.5 Flash-Lite: $0.3 / $2.5
As is, they are thoroughly outclassed for most usecases. I will say the one area where i do see Gemini punching above its weight class is in tasks that are effectively "Google this for me" / knowledge stuff. So it does have a role, and I do use it. So while I think Google is still in a strong position overall, they are really stuck as a tier 2 AI player right now with text models. They are tier 1 in bio, images, and video.
That being said with any open model we of course do know the total cost (or estimate)
EDIT: It less less verbose in final output though, but it reasons more.
I assume the optimization comes when you have long-running tasks with many tool calls, and by reasoning more, it reduces the number of tool calls needed.
i guess we'll use 3.0 flash but thats going to get replaced too right ?
these flash lite models aren't very reliable or consistent
Given the extremely competitive releases of GLM 5.2 and DeepSeek V4 (both pro and flash), I don't think there'll be appetite for it.
> Beyond today’s releases, Gemini 3.5 Pro is currently testing with partners and we plan to make it broadly available as soon as it’s ready.
> We have started our most ambitious pre-training run yet, for Gemini 4, and are excited by the progress.
Hopefully 3.5 Pro is soon, and that Gemini 4 can be here end of year and finally have an updated knowledge cutoff.
Sure.
And how does that make your day better? I know it does not improve my work in any way shape or form.
I'll take a better coding model that's not multi-modal any time.
If I need an LLM to do images or sound, I'd rather use a dedicated one instead of a jack-of-all-trades-master-of-none model.
You can get decent open-weight models now. That's not difficult. The difficulty is 1) running them and 2) compliance.
My company runs Claude on GCP's Vertex AI solution. We're in the US healthcare IT space, so the models need to be from somewhere that American healthcare agencies and companies have traditionally been okay with sourcing code from - which means the US, Canada, and maybe Europe. The stuff that handles PHI/PII must be in the US. The expense of hosting is more of a PITA than most customers want to go through this early in the technology's lifecycle, and intelligence gains are simply a matter of degree for most business tasks.
In theory, we could find some open-weight model (likely from China) for our development agentic work and host it anywhere you can host AI models. We don't, though, and I think Google, OpenAI/Microsoft, and Anthropic see that as the core of their business.
https://artificialanalysis.ai/#intelligence-comparison-tabs
Differences in token "density" are accounted for by pricing per task
I have a very price sensitive workload that used to run on flash 2.5 lite - it's deprecated now.
The replacement 3.1 flash lite is a lot more expensive, but now also has a sunset date.
3.5 flash lite is even more expensive.
So the price is rising and you have no choice but to keep paying more and more.
but the implementation will be up to your provider and harness, for deepseek, they expose some numbers: https://api-docs.deepseek.com/guides/kv_cache/ and Anthropic has a list of actions invalidating your cache: https://platform.claude.com/docs/en/build-with-claude/prompt...
Basically, you avoid anything dynamic: model change, tool change, etc it's also important that your system prompt or main prompt doesn't have non-static data like the date/time/place or someone's name (the person you interact with in a chatbot for example). That should be left to tool call or search.
If you’re constructing the prompt you don’t have to jam everything together you can arrange it appropriately.
All of the models, you need to have a consistent input to get the cache hit. So if you are chatting with a document, and change the system prompt, it will be a cache miss, even if the rest of the items are all the same. If you even pass in the document in not the same order as the prompts, it will be a cache miss. Or if you add tool calls or structured outputs, it will be a cache miss. (Since those generally go at the beginning of the prompt call, not at the end.)
Most of the time when reading documents from URLs directly it will never cache. (Need to typically pass in the bytes directly, or use the provider document store index.)
Gemini has a 4096 minimum token size with the 3 version models before even getting a cache hit. OpenAI it is lower (1024), and is automatic, but only happens in increments of 124. Anthropic can also get cache hits at 1024 tokens, but you need to explicit ask for it (and pay extra).
Caching by default typically lives for 5 minutes since the last cache hit across providers. But some of them you can ask for longer. AWS for Anthropic models can be tricky with multiple endpoint routing, so can get cache misses if it happens to route to a different endpoint.
Features stay in Beta for ages, whatever that actually means, and released ones get deprecated things fast.
Where some of the competitions treats deprecating entire services as "let’s not put it on your frontpage, put deprecation notices all over the doc, and politely ask new users not to start new project with them".
google's inability or unwillingness to provide stable timelines for model deprecation makes it risky to build complex workflows using their models
For my use case, `gemini-3.1-flash-lite` is ~20% higher accuracy than the next best model of comparable cost (considering both proprietary and open-weight alternatives)
They are not anywhere close according to pretty much every benchmark (even v4-flash is considerably ahead and its way cheaper than flash-lite). Maybe tuning prompts/tools/etc. might be useful?
I presume you can't use deepseek?
we are switching to Deepseek.
These days no company even has completion models where one controls the text input fully. Worthless.
You can also just write code like you did a year or two ago.
If you're using it for other purposes, then I give you permission to ignore my comment; there's no reason to descend into name calling.
Anyone have any good alternatives?
I tested Jules and while the idea is good in theory, I found the model's intelligence to be very lackluster.
It might be overkill features-wise, but there's a free tier and it likely won't be left for dead anytime soon.