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I wonder how big the Pro model is that Google is using behind the scenes to train these smaller ones. Going on baseless speculation, the lack of accompanying p
by postalcoder 2mo ago
I wonder how big the Pro model is that Google is using behind the scenes to train these smaller ones.
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 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.
- anthonypasq 2mo agoLogan Kilpatrick said on an interview not too long ago that flash 3 and 3.5 are the same pre-train. all gains on top of 3 flash are post-training
- mchusma 2mo agoMaybe, but they said they have “started” the Gemini 4 pretrain. So not having done any significant pretrain in a year or so seems odd to me.
- WarmWash 2mo agoPre-trains take a huge chunk of your compute offline, incurring both an raw expense (24/7 max power for all training clusters) and an opportunity cost (could have sold excess compute during that time). They also don't come with any great guarantees, as lots of techniques look good on small scale and crumble or plateau once scaled.
- petercooper 2mo agoI wonder if the broad use of AI overviews on Google search results is having an impact. Maybe the numbers make it more profitable to use their compute on several billion searches a day rather than selling API access.
- WarmWash 2mo agoI think it's a safe bet that Google seems more interested in making a model that improves Google rather than making a model that improves workers. 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.
- neutronicus 2mo agoThe AI mode on Google search is pretty impressive. Helped me figure out what a bunch of stuff I was seeing out the window was while traveling.
- SadErn 2mo agoMicrosoft also seems to be working in this space. They recently released this: https://huggingface.co/microsoft/bitnet-embedding-0.6b 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.
- paxys 2mo agoAI overview is just a summarization of the top 2-3 results. Of course at Google scale that will still need a ton of compute, but the requirement for generating an overview is many orders of magnitude lower than asking the same question in Gemini.
- 2mo ago
- jauntywundrkind 2mo agoOr perhaps 4) it's outcompeted severely by other models & releasing it would only tarnish their name
- spyckie2 2mo agoI wonder if they waited for the new TPU generation to train a larger base model.
- Tenoke 2mo agoIt's also very possible that they know their big model underperforms chatgpt 5.6 and fable by too much, so they are focusing on what they can get wins in like speed instead.
- verelo 2mo agoThis is the feeling i get too. Cant produce quality, but can produce something that is super fast...so take the wins where they are.
- copperx 2mo agoWe don't have enough fast models, so I see this as a positive. I just test drove Gemini Flash Lite and it's crazy fast.
- tonyhart7 2mo agowe need fast + cheap AI model, those gemini 2.0 flash is superb like it literally pennies
- dotancohen 2mo agoFor a coding LLM specifically, when is fast a good tradeoff for quality?
- verelo 2mo agoI wouldnt say it is, but there are circumstances when speed is helpful. I wouldn't argue that coding is one of them.
- rockinghigh 2mo agoA coding agent driven by a large LLM can delegate smaller tasks to a faster model. For example searching through the codebase for references, examples, or established patterns. They are treated as tools and don't pollute the main agent's context.
- 2mo ago
- tpm 2mo ago"3.5 pro is testing with partners! will hopefully land soon." https://x.com/OfficialLoganK/status/2079596415509303596 https://x.com/OfficialLoganK/status/2079596415509303596
- awongh 2mo agoIt seems like there are some credible rumors that Google is actually winning in terms of actually building models that work and don't lose money- between how they're able to price them, the TPU advantage and their capex advantage (being able to raise debt + just having a lot of cash - well I said not lose money... more like not go bankrupt). 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.
- redox99 2mo agoThey basically don't exist in the currently most profitable LLM market (coding). Yes, subs like codex are heavily subsidized. But API billing has massive margins and that's what enterprises pay.
- awongh 2mo agoDoes it have "massive" margins? Afaik no one has said publicly what margins there are on an API call?
- SyneRyder 2mo ago"As of October [2025], OpenAI's compute margins reached 70%, up from 52% at the end of 2024 and double the rate in January 2024, [The Information] said, citing a person familiar with the figures." https://www.bloomberg.com/news/articles/2025-12-21/openai-sees-better-margins-on-business-sales-report-says 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.
- 2mo ago
- re-thc 2mo ago> the lack of accompanying pro models with these flash releases either means: Rumors say 4) it didn't perform well, especially in coding so has been delayed
- ocamoss 2mo agoMaybe it's like Meta not releasing the big version of Llama 4 a year or two ago
- SXX 2mo agoI choose fourth option. 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.
- tonyhart7 2mo agoor they just don't want compete in coding space ??? they have search,youtube,android,office suite like gmail,maps,spreadsheet etc coding is the least of their problem/priority
- imtringued 2mo agoConsidering how cheap the subscriptions are, it looks like agentic coding is a low margin business. If they can sell you a subscription for the chat, it is profitable, but if you try to use the subscription to its limits, you're probably making them lose money.
- tonyhart7 2mo agoabsolutely, knowing OpenAI try to break into ads market tell the whole direction that pure AI is not that profitable tbh (especially with how cheap chinnese model are) the integration on ecosystem is the bread are
- rjh29 2mo agoI think it's 2. I frequently get told there's no capacity for Pro and the query is answered by Flash with extended thinking. And tbh it's hard to tell the difference between the two, especially if you're not coding with it.
- hn8726 2mo agoIt's hard to tell the difference because they nerfed Pro to oblivion, it used to be much, much better model (even for non-coding/chat)
- zwaps 2mo agoMore likely they don't manage to advance benchmarks on the SOTA level anymore. In other words: They can't beat 5.6 nor Fable
- joshu 2mo ago2.5 flash was absurdly capable on a cost basis
- atif089 2mo agoFriend works for Google vendor who generates data for training. His team alone is 200 people (in US). He says there are many similar vendors and teams with thousands of people in India and other countries.
- steve-atx-7600 2mo agoArtificial analysis always seemed sketchy as hell. If you read some of there methodology you’ll see a lot of <=3 repetitions on a particular pass for a given model. So low for calling a frontier model over the public internet ????
- retinaros 2mo agoFlash versions were often ultra competitive and their best in the range along with openai mini models. Always been gemini most useable and best model with nano b. Frontier is much more competitive. Anthropic haiku is like 2025 flash…
- smcleod 2mo agoI suspect the entire 3.x family is fundamentally problematic and we'll need to see an architecture change before they're half decent like back in the 2.5 days again.
- reacharavindh 2mo agoI wish someone would convince Google to may be leave the Google search be without AI responses and use all their resources for a Gemini subscription/API..
- bjackman 2mo ago> This is a very fast model. I was already impressed by how fast 3.5 Flash was. But I've never compared it to other models in its class for coding. Why? Coz models in that class are not very useful to me. Time saved waiting for responses usually just turns into time wasted replying to low quality responses. Google need to release a Pro model ASAP. I am skeptical of the "maybe they don't have the compute to run it" thing. Anthropic were (probably) in that situation with Mythos and they announced it anyway - that's the obvious play for investor relations as well as hype for your product.
- Kinrany 2mo ago> 3) their big model has too many alignment issues This implies that normally models are aligned and there is merely a number of issues to fix.
- Zigurd 2mo agoI've been using 3.5 flash in Android Studio on a Dart/Flutter project, some of it pretty complex and using brand new API's and native code for agentic tool calling in the app. It used to be that you could find the edges of the training set pretty easily. No longer.
- _alternator_ 2mo agoWhat I think is really going on is an attempt to segment the market in favor of Google's strengths. It's a bet that models are "good enough" for many use cases even before they reach human-level intelligence, and Google is trying to capture workflows where quantity beats quality. They are likely deliberately avoiding the SoTA race for a few reasons: 1. Their best models are marginally better than current SoTA releases. 2. They'd like to let Ant/OAI make mistakes with safeguards / let them get the regulatory heat. The unknown unknowns are huge with SoTA models (eg OAI accidentally hacking huggingface) and they are protecting their reputation. 3. They want to encourage companies to become cost conscious because they can likely win on price in the long run. Getting market share in "quantity beats quality" workflows forces companies to establish processes to choose the "cheapest acceptable model", which is a good environment for Google.