Pratique du Shadowing: Gemini 4 Argon explained in 5min.. - Apprendre l'anglais à l'oral avec la vidéo

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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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Vocabulaire et conseils d’expression pour cette leçon

Cette leçon d’expression orale s’appuie sur la vidéo « Gemini 4 Argon explained in 5min. ». Les mots qui reviennent le plus souvent : Google, model, benchmark, token, Argon. Cette vidéo contient 56 phrases et 877 mots à répéter en shadowing. La partie parlée dure 4:56. Le locuteur parle à un rythme naturel d’environ 178 mots par minute, proche d’une conversation courante. Seuls 77 % des mots font partie des 3 000 mots les plus courants en anglais, le vocabulaire est donc exigeant.

Vocabulaire clé de cette vidéo

15 mots de la vidéo à retenir, avec leur prononciation et leur sens :

Mot	Prononciation	Sens
benchmark nom	/ˈbɛn(t)ʃmɑːk/	critère, aune
token nom	/ˈtoʊkən/	symbole
subscription nom	/səbˈskɹɪpʃən/	abonnement
output nom	/ˈaʊtpʊt/	sortie
capture verbe	/ˈkæp.(t)ʃɚ/	saisir, capturer
generate verbe	/ˈd͡ʒɛn.ə.ɹeɪt/	générer
frontier nom	/ˈfɹʌntɪə/	frontière
flagship nom	/ˈflæɡʃɪp/	navire amiral, bateau amiral
harness verbe	/ˈhɑɹ.nəs/	harnacher
solve verbe	/sɒlv/	résoudre, régler
extend verbe	/ɛkˈstɛnd/	étendre
gravity nom	/ˈɡɹævɪti/	gravité
adoption nom	/əˈdɑp.ʃən/	adoption
trajectory nom	/tɹəˈd͡ʒɛktəɹi/	trajectoire
earn verbe	/ɝn/	gagner

Les verbes à particule que vous entendrez

Mot	Sens
figure out verbe	cerner
open up verbe	ouvrir

La grammaire de cette vidéo

Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :

Structure	Dans la vidéo
Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait	are flawed · was actually released · can be contaminated
Present perfect have/has + participe passé — une action passée qui compte encore maintenant	It's been · hasn't really solved

Prononciation à surveiller

Le locuteur utilise 3 contractions et formes réduites, comme doesn't, hasn't, they're. Prononcez-les sous leur forme courte, telles que vous les entendez.

Les sons « sh » et « zh »: benchmark /ˈbɛn(t)ʃmɑːk/, subscription /səbˈskɹɪpʃən/, capture /ˈkæp.(t)ʃɚ/, flagship /ˈflæɡʃɪp/, adoption /əˈdɑp.ʃən/
Mots longs — placez bien l’accent: trajectory /tɹəˈd͡ʒɛktəɹi/, primarily /ˈpɹaɪ.mə.ɹə.li/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, potentially /pəˈtɛnʃ(ə)li/, developer /dɪˈvɛləpɚ/

Les sons difficiles pour les francophones :

/h/ — il se souffle, il n’est pas muet: harness /ˈhɑɹ.nəs/, coherence /koʊˈhɪɹ.ən(t)s/
/tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: generate /ˈd͡ʒɛn.ə.ɹeɪt/, trajectory /tɹəˈd͡ʒɛktəɹi/, margin /ˈmɑɹ.d͡ʒɪn/
/r/ anglais — langue recourbée, sans frotter la gorge: subscription /səbˈskɹɪpʃən/, generate /ˈd͡ʒɛn.ə.ɹeɪt/, frontier /ˈfɹʌntɪə/, harness /ˈhɑɹ.nəs/, gravity /ˈɡɹævɪti/

Comment s’entraîner avec cette vidéo

Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme benchmark, token, subscription.

Qu'est-ce que la technique du Shadowing ?

Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.

Technique du shadowing : lire le guide complet étape par étape →

Pratique du Shadowing: Gemini 4 Argon explained in 5min.. - Apprendre l'anglais à l'oral avec la vidéo

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Vocabulaire et conseils d’expression pour cette leçon

Vocabulaire clé de cette vidéo

Les verbes à particule que vous entendrez

La grammaire de cette vidéo

Prononciation à surveiller

Comment s’entraîner avec cette vidéo

Qu'est-ce que la technique du Shadowing ?