Shadowing Practice: Gemini 4 Argon explained in 5min.. - Learn English Speaking with Video

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It's been about seven months since Google announced a new flagship model, Gemini 4 Argon.

Even during Google I .O back in May, I expected Google to drop Gemini 3 .5 Pro.

But Google has been releasing a lot of flash models instead,

and a lot of them basically struggle to get market adoption given a variety of other flagship models

and cheaper alternatives from Chinese open models.

And Google finally made a flagship model with the codename Argon, and the preliminary benchmarks show a pretty strong number as you can see.

The first thing you might have noticed here is coding benchmarks, more specifically, scoring 77 .9 % on DeepSuite,

which would put Gemini 4 Argon above every other model we currently have.

But it's always good to have some healthy skepticism here.

Recently, EPOC found issues on DeepSuite Benchmark, finding 23 tasks that are flawed.

And looking back to when the benchmark was actually released back in May, the entire 113 tasks and their solutions are made public,

which means labs that have access to these could easily study them during post -training and benchmarks their models around public benchmarks.

And when we look at other benchmarks that Google reported here, a large majority of them are public.

So the further we go out in time, labs that release their models later have a slight advantage to potentially include them during post -training.

And of course, as users, all of this makes it really difficult to track real progress in AI since

so many public benchmarks like DeepSuite can be contaminated pretty fast.

Not only that, benchmarks get saturated a lot faster now than before, which forces benchmarks to be refreshed more frequently.

So consumers now carry a huge burden keeping track of real scientific progress in AI.

But regardless of the case, achieving this kind of leap in benchmark is a huge improvement from Google.

Now, when we take a step back and look at how Google is competing with other labs, a lot of people still primarily use clock code,

codecs, or open harnesses when it comes to coding.

And despite being a strong model, Google still hasn't really solved making their harness anti -gravity a truly competitive option for developers.

For a recent report, Codex has more than doubled the amount of weekly active users at 5 million compared to 2 .4 million for anti -gravity.

And on face value, Gemini 4 Argon currently undercuts both Anthropic

and OpenAI when it comes to the cost of intelligence until the temporary discount ends and the price doubles again.

But the cost benefit that we find here doesn't actually translate in Pareto Frontier, as you can see.

Argon falls below the Pareto Frontier, showing you that Anthropic and OpenAI still leads ahead in both the harness and model adoption.

Now, one angle we should think about, though, is the upcoming IPO for Anthropic and possibly OpenAI.

While OpenAI and Anthropic have a huge incentive to subsidize the model through subscription pretty heavily to grow their users,

Google already sits on top of one.

And given that Google is already a publicly traded company,

they want to be more careful about how to allocate their existing compute towards actually creating value within their ecosystem,

which could help increase their perceived revenue.

Even looking at how Argon is being rolled out in segments, the model is set to be made available to ultra -subscription users

and paid API users where there's already a huge revenue being

captured instead of fighting the subscription rates in channeling their compute towards that.

On a recent Google's Q2 earning call, they said that their API is processing 22 billion tokens per minute,

which is 6 billion more than the previous quarter.

That's more than 11 quadrillion tokens per year in just API channel alone.

And when you start factoring in other channels that Google owns in its distribution,

we really get a closer picture into what Google really means when they said that they're supply constrained.

And eventually, OpenAI and Anthropic will have to solve this scaling problem while maintaining healthy margins at the same time,

not just subsidizing subscription to capture more users.

So looking at Gemini for Argon, Google is demonstrating their capability in keeping pace at the frontier

while they also try to figure out how to capture value at scale.

One interesting note on Argon is how Google extended their output tokens from 64 ,000 to 1 million tokens.

This might not sound like a big deal, but it's definitely a huge move from Google

because maintaining coherence as autoregressive models generate longer and longer tokens get increasingly difficult the longer the output tokens get.

Scientists like Yen Le Koon made criticism on autoregressive models for this very reason,

where autoregressively generating one token after another can compound errors over long trajectory, let alone 1 million from Argon.

If you can imagine each token that's 99 % reliable, chaining 100 independent steps together would leave about 37 % chance of every step being correct.

Now, of course, that's just in theory, assuming that every token error is independent.

But the fact

that Google has enough confidence to extend their commercial product to

1 million output tokens really opens up door for longer trajectory tasks like code migration

and long -running research tasks that could benefit from a model that can generate more.

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Vocabulary and speaking notes for this lesson

This speaking lesson is built on the video “Gemini 4 Argon explained in 5min.”. The speaker keeps coming back to these words: Google, model, benchmark, token, Argon. This video has 56 sentences and 877 words to shadow. The speech runs for 4:56. The speaker talks at a natural 178 words per minute, close to everyday conversation. Only 77% of the words are among the 3,000 most common in English, so the vocabulary is demanding.

Key vocabulary in this video

15 words from the video worth learning, with pronunciation and meaning:

Word	Pronunciation	Meaning
benchmark noun	/ˈbɛn(t)ʃmɑːk/	A standard by which something is evaluated or measured.
token noun	/ˈtoʊkən/	Something serving as an expression of something else.
subscription noun	/səbˈskɹɪpʃən/	Access to a resource for a period of time, generally for payment.
output noun	/ˈaʊtpʊt/	Production; quantity produced, created, or completed.
capture verb	/ˈkæp.(t)ʃɚ/	To take control of; to seize by force or stratagem.
lab noun	/læːb/	A Labrador retriever.
generate verb	/ˈd͡ʒɛn.ə.ɹeɪt/	To bring into being; give rise to.
frontier noun	/ˈfɹʌntɪə/	The part of a country which borders or faces another country or unsettled region.
flagship noun	/ˈflæɡʃɪp/	The ship occupied by the fleet's commander (usually an admiral); it denotes this by flying his flag.
harness verb	/ˈhɑɹ.nəs/	To place a harness on something; to tie up or restrain.
solve verb	/sɒlv/	To find an answer or solution to a problem or question; to work out.
extend verb	/ɛkˈstɛnd/	To increase in extent.
gravity noun	/ˈɡɹævɪti/	Of an activity such as a ceremony, a person's conduct, etc.: the quality of being deeply serious and solemn, especially in a dignified manner; seriousness…
adoption noun	/əˈdɑp.ʃən/	The act of adopting.
trajectory noun	/tɹəˈd͡ʒɛktəɹi/	The path an object takes as it moves.

Phrasal verbs you will hear

Word	Pronunciation	Meaning
figure out verb		To come to understand; to discover or find a solution; to deduce.
open up verb		To open.
step back verb	/stɛp ˈbæk/	To stop what one is doing and evaluate the current situation.

Grammar in this video

The structures the speaker uses most, with the exact words from the video:

Structure	In the video
Passive voice be + past participle — the focus is on what happens, not who does it	are flawed · was actually released · can be contaminated
Present perfect have/has + past participle — a past action that still matters now	It's been · hasn't really solved

Pronunciation to watch

The speaker uses 3 contractions and reduced forms, such as doesn't, hasn't, they're. Say them the short way, as you hear them.

The “sh” and “zh” sounds: benchmark /ˈbɛn(t)ʃmɑːk/, subscription /səbˈskɹɪpʃən/, capture /ˈkæp.(t)ʃɚ/, flagship /ˈflæɡʃɪp/, adoption /əˈdɑp.ʃən/
Long words — get the stress right: trajectory /tɹəˈd͡ʒɛktəɹi/, primarily /ˈpɹaɪ.mə.ɹə.li/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, potentially /pəˈtɛnʃ(ə)li/, developer /dɪˈvɛləpɚ/

How to practise with this video

Listen to the whole video once without speaking and note the words you do not know.
Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
Record yourself and compare with the original, paying attention to words like benchmark, token, subscription.

What is the Shadowing Technique?

Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.

Shadowing technique: read the full step-by-step guide →

Shadowing Practice: Gemini 4 Argon explained in 5min.. - Learn English Speaking with Video

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