FWIW, the versions are not semver but they do follow a defined and regular version schema: https://ai.google.dev/gemini-api/docs/models#model-versions.

2.5 isn't the version number, its the model generation. it would only be updated when the underlying model architecture, training, etc are updated. this release is, as the name implies, the same model but likely with hardware optimizations, system prompt, and fine-tuning tweaks applied.

2.5 Flash is the first time I've felt AI has become truly useful to me. I was #1 AI hater but now find myself going to the Gemini app instead of Google search. It's just better in every way and no ads. The info it provides is usually always right and it feels like I have the whole generalized and accurate knowledge of the internet at my fingertips in the app. It's more intimate, less distractions. Just me and the Gemini app alone talking about kale's ideal germination temperature, instead of a bunch of mommy bloggers, bots, and SEO spam.

Now how long can Google keep this going and cannibalizing how they make money is another question...

I think “Non-AI summary” is going to become a thing. I already enjoyed reading it more because I knew someone had thought about the content.

Any idea what "output token efficiency" refers to? Gemini Flash is billed by number of input/output tokens, which I assume is fixed for the same output, so I'm struggling to understand how it could result in lower cost. Unless of course they have changed tokenization in the new version?

That's a difference in the system prompt, not the model itself.

There several reasons responses from the same model might vary:

- "temperature" - intentional random sampling from the most likely next tokens to improve "creativity" and help avoid repetition

- quantization - running models with lower numeric precision (saves on both memory and compute, without impacting accuracy too much)

- differences in/existence of a system prompt, especially when using something end-user-oriented like Qwen Chat

- not-quite-deterministic GPU acceleration

Benchmarks are usually run at temperature zero (always take the most likely next token), with the full-precision weights, and no additions to the benchmark prompt except necessary formatting and stuff like end-of-turn tokens. They also usually are multiple-choice or otherwise expect very short responses, which leaves less room for run-to-run variance.

Of course a benchmark still can't tell you everything - real-world performance can be very different.

Maybe the same reason why they kept the name for the 2.5 Flash update.

People are lazy at pointing to the latest name.

Why is Grok so popular

I know we have a lot of workloads at my company on older models no one has bothered to upgrade yet

Gemini 2.0 Flash is the best fast non reasoning model by quite a margin. Lot of things doesn't require any reasoning.

Price, 2.0 Flash is cheaper than 2.5 Flash but still very good model.

It’s cheaper and faster. What’s not to understand?

My one big problem with OpenRouter is that, as far as I can tell, they don't provide any indication of how many companies are using each model.

For all I know there are a couple of enormous whales on there who, should they decide to switch from one model to another, will instantly impact those overall ratings.

I'd love to have a bit more transparency about volume so I can tell if that's what is happening or not.

Yeah, why is it that working with AI makes people completely forget what version numbers mean?

gemini-2.5-flash-preview-09-2025 - what are they thinking?

I thought about joking that they had AI name it for them, but when I asked Gemini, it said that this name was confusing, redundant, and leads to unnecessarily high cognitive load.

Maybe Googlers should learn from their own models.

Because the number is model generation.

I tried to put Pro deep research on an actual research task and it didn’t even return anything just kept on working.

This is not my experience. In my experience Gemini 2.5 Pro is the best model in every use-case I tried. There are a few very hard (graduate level) logic or math problems that Claude 4.1 Opus edged-out over Gemini 2.5 Pro, but in general if you have no idea which model will perform best on a difficult question, imho Gemini 2.5 Pro is a safer bet especially since it's significantly cheaper. Gemini 2.5 Flash is really good but imho not nearly as good as Pro in (1) research math (2) creative/artistic writing (3) open ended programming debugging.

On the other hand, I do prefer using Claude 4 Sonnet on very open-ended agentic programming tasks because it seems to have a better integration with VSCode Copilot. Gemini 2.5 Pro bugs out much more often where Claude works fine almost every time.

Can't agree with that. Gemini doesn't lead just on price/performance - ironically it's the best "normie" model most of the time, despite it's lack of popularity with them until very recent.

It's bad at agentic stuff, especially coding. Incomparably so compared to Claude and now GPT-5. But if it's just about asking it random stuff, and especially going on for very long in the same conversation - which non-tech users have a tendency to do - Gemini wins. It's still the best at long context, noticing things said long ago.

Earlier this week I was doing some debugging. For debugging especially I like to run sonnet/gpt5/2.5-pro in parallel with the same prompt/convo. Gemini was the only one that, 4 or so messages in, pointed out something very relevant in the middle of the logs in the very first message. GPT and Sonnet both failed to notice, leading them to give wrong sample code. I would've wasted more time if I hadn't used Gemini.

It's also still the best at a good number of low-resource languages. It doesn't glaze too much (Sonnet, ChatGPT) without being overly stubborn (raw GPT-5 API). It's by far the best at OCR and image recognition, which a lot of average users use quite a bit.

Google's ridiculously bad at marketing and AI UX, but they'll get there. They're already much more than just a "bang for the buck" player.

FWIW I use all 3 above mentioned on a daily basis for a wide variety of tasks, often side-by-side in parallel to compare performance.

IMO the race for Latency/TPS/cost is entirely between grok and gemini flash. No model can touch them (especially for image to text related tasks), openai/anthropic seem entirely uninterested in competing for this.

I would be surprised if this dichotomy you're painting holds up to scrutiny.

My understanding is Gemini is not far behind on "intelligence", certainly not in a way that leaves obvious doubt over where they will be over the next iteration/model cycles, where I would expect them to at least continue closing the gap. I'd be curious if you have some benchmarks to share that suggest otherwise.

Meanwhile, afaik something Google has done, and perhaps relates back to your point re "latency/TPS/cost dimensions" that other providers aren't doing as much is integrating their model into interesting products beyond chat, at a pace that seems surprising given how much criticism they had been taking for being "slow" to react to the LLM trend.

Besides the Google Workspace surface and Google search, which now seem obvious - there are other interesting places where Gemini will surface - https://jules.google/ for one, to say nothing of their experiments/betas in the creative space - https://labs.google/flow/about

Another I noticed today: https://www.google.com/finance/beta

I would have thought putting Gemini on a finance dashboard like this would be inviting all sorts of regulatory (and other) scrutiny... and wouldn't be in keeping with a "slow" incumbent. But given the current climate, it seems Google is plowing ahead just as much as anyone else - with a lot more resources and surface to bring to bear. Imagine Gemini integration on Youtube. At this point it just seems like counting down the days...

I think that the Gemini 3 pro might be next month I am not sure.

can I get the sources of your rumour please? (Yes I know that I can search it but I would honestly prefer it if you could share it, thanks in advance!)

Not sure prices are changed though. :/

Gemini 2.5 Flash runs circles around ChatGPT 5 for many of my tasks, I’m surprised it’s not more popular than it is.

I would assume that it will supersede the model that they currently have. So eventually 2.5 flash will be the new and improved 2.5 Flash rather than 2.6.

Same way that openai updated their 4-o models and the like, which didn't turn out so well when it started glazing everyone and they had to revert it (maybe that was just chat and not api)

This is also my beef...

Anthropic kind of did the same thing [1] except it back-fired recently with the cries of "nerfing".

We buy these tokens, which are very hard to do in limited tiers, they expire after only a year, and we don't even know how often the responses are changing in the background. Even a 1% improvement or reduction I would want disclosed.

Really scary foundation AI companies are building on IMO. Transparency and access is important.

[1] https://status.claude.com/incidents/h26lykctfnsz

chatgpt also has lots of reliability issues

"Improving the efficiency" sounds fine to me (a native English speaker), what's wrong with it in your opinion?

ChatGPT and Claude are mistaken if they think it is incorrect. The parallelism in verb tenses is between "continuing to deliver" and "improving the efficiency". It's a bit wordy, but definitely not wrong.

Who wins in the end? the frogs? the ducks? or the pelicans?