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Comment by simonw | Hacker News Reader

This price drop is significant. For <128,000 tokens they're dropping from $3.50/million to $1.25/million, and output from $10.50/million to $2.50/million.

For comparison, GPT-4o is currently $5/million input and $15/million output and Claude 3.5 Sonnet is $3/million input and $15/million output.

Gemini 1.5 Pro was already the cheapest of the frontier models and now it's even cheaper.

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whats confusing they have different pricing for output. Here [1] it's $5/million output (starting 1st october) and on vertex AI [2] it's $2.5/1 million (starting 7 october) - but characters - so it's overall gonna be more expensive if you wanna compare to equivalent 1 million tokens. It's actually even more confusing to know what kind of characters they mean? 1 byte? UTF-8?

[1] https://ai.google.dev/pricing

[2] https://cloud.google.com/vertex-ai/generative-ai/pricing

They do mention how characters are counted in the Vertex AI pricing docs: "Characters are counted by UTF-8 code points and white space is excluded from the count"

They CHANGED the pricing for the first link; originally the output said price drop from $10.50 to $2.50 but now it says $10.50 to $5.00.

I wonder if they're pulling the wall-mart model. Ruthlessly cut costs and sell at-or-below costs until your competitors go out of business, then ratchet up the prices once you have market dominance.

You think Google would engage in monopolistic practices like that?

Because I do

I have no idea if this is dumping or not. At Microsoft/Google scale, what does it cost to serve a million LLM tokens?

Tough to disentangle the capex vs opex costs for them. If they did not have so many other revenue streams, potentially dicey as there are probably still many untapped performance optimizations.

Isn't Walmart still incredibly cheap? They have a net margin of 2.3%

I think that's one of those things competitors complain about that never actually happens (the raising prices part).

https://www.macrotrends.net/stocks/charts/WMT/walmart/net-pr...

Probably not. Do they really believe they are going to knock OpenAI out of business, when the OpenAI models are better?

Instead I think they are going after the "Android model". Recognize they might not be able to dethrone the leader who invented the space. Define yourself in the marketplace as the cheaper alternative. "Less good but almost as good." In the end, they hope to be one of a small number of surviving members of an valuable oligopoly.

Android is more popular than iOS by a large margin and it's neither less good or cheaper, it really depends on the smartphone.

The latest Google Pixel phone (you know, the one that Google actually set the price for) appears to cost the exact same as the latest iPhone ($999 for pro, $799 for non-pro). And I would argue against the "less good" bit too.

I think this analysis is not in keeping with reality, and I doubt if that's their strategy.

I doubt anyone buys Pixel phones at full price. They are discounted almost right out of the gate.

Cheapness has a quality all its own.

Gemini is substantially cheaper to run (in consumer prices, and likely internally as well) than OpenAI's models. You might wonder, what's the value in this, if the model isn't leading? But cheaper inference could potentially be a killer edge when you can scale test-time compute for reasoning. Scaling test-time compute is, after all, what makes o1 so powerful. And this new Gemini doesn't expose that capability at all to the user, so it's comparing apples and oranges anyway.

DeepMind researchers have never been primarily about LLMs, but RL. If DM's (and OAI's) theory is correct--that you can use test-time compute to generate better results, and train on that--this is potentially a substantial edge for Google.

In Home Assistant you can use LLMs to control your Home with your voice. Gemini performs similar to the GPT models, and with the cost difference there is little reason to choose OpenAi

Using either frontier model for basic edge device problems is wasteful. Use something cheap. We're asking "is there a profitable niche between the best & runner-up models?" I believe so.

Google still has an unbelievable training infrastructure advantage. The second they can figure out how to convert that directly to model performance without worrying about data (as the o1 blog post seemed to imply OAI had) they’ll be kings.

This is why Sam Altman keeps releasing things a few days before Deepmind. He is worried Google will overtake them more so than other companies.

> Probably not. Do they really believe they are going to knock OpenAI out of business, when the OpenAI models are better?

Would OpenAI even exist without Google publishing their research? The idea that Google is some kind of also-ran playing catch up here feels kind of wrong to me.

Sure OpenAI gave us the first productized chatbots, so in that sense they "invented the space," but it's not like Google were over there twiddling their thumbs - they just weren't exposing their models directly outside of Google.

I think we're past the point where any of these tech giants have some kind of moat (other than hardware, but you have to assume that Google is at least at parity with OpenAI/MS there).

Yes, and it's the exact same thing OpenAI/Microsoft and Facebook are doing. In Facebook's case, they are giving it away for free.

There's lot of room to cut margins in the AI stack right now (see Nvidia's latest report); low prices are not an sure indication of predatory pricing. Which company do you think is most likely to have the lowest training and inference costs between Anthropic, OpenAI and Google? My bet goes to the one designing,producing and using their own TPUs.

GPT-4o is 2.5/10$. Unless you look at an old checkpoint. GPT-4o was the cheapest frontier model before.

I can’t see that price on https://openai.com/api/pricing/ - it’s listing $5/m input and $15/m output for GPT-4o right now.

No wait, correction: That’s confusing: it lists 4o first and then lists gpt-4o-2024-08-06 as $2.50/$10.

apologies: it's taken us a minute to switch the default `gpt-4o` pointer to the newest snapshot

we're planning on doing that default change next week (October 2nd). And you can get the lower prices now (and the structured outputs feature) by manually specify `gpt-4o-2024-08-06`

> “You can”

No, “I” can’t.

Open AI has always trickled out model access, putting their customers into “tiers” of access. I’m not sufficiently blessed by the great Sam to have immediate access.

On, and Azure Open AI especially likes to drag their feet both consistently, and also on a per-region basis.

I live in a “no model for you” region.

Open AI says: “Wait your turn, peasant” while claiming to be about democratising access.

Google and everyone else just gives access, no gatekeeping.

> Google and everyone else just gives access, no gatekeeping.

Well, Gemini Pro was delayed in Europe for many months. Same for Claude.

For legal/regulatory reasons, not for the arbitrary favouritism reasons of OpenAI.

> For comparison, GPT-4o is currently $5/million input and $15/million output and Claude 3.5 Sonnet is $3/million input and $15/million output.

Google is the only one of the three that has its own data centers and custom inference hardware (TPU).

It doesn't matter if it's cheap, it's unusable.

Can you expound on why do you find it unusable?

They CHANGED the pricing from $2.50 to $5.00 stealthily unannounced. Look at the web site again; it says $5 per million now, and this comment on this website might be the ONLY evidence in the world that I wasn't gaslighting myself or hallucinating!

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