शैडोइंग अभ्यास: OpenAI fights back - वीडियो के साथ अंग्रेजी बोलना सीखें

they're back okay let me catch you guys up quick last week anthropic dropped a new model opus 5 .5

and it was unbelievably good it was so unbelievably good

that openai rushed out two model drops gpt6 soul

and gpt6 luna you might have noticed i didn't do a video on those models there's a reason they weren't

that good i was not particularly impressed with either of them

and didn't really have much to say

but there was one other model i happened to get early access to

that is now available for all that model is called gpt 6 .1 sol

and this model made it very very hard to film

that sonnet 5 .5 video because i knew something was coming something surprisingly cheap surprisingly capable

and most surprisingly better than astra

so yeah i got a lot to say about this one i'm filming this at two in the morning, right after filming my Sonnet 5 .5 videos.

So pardon me for stumbling over a few words here and there.

I'm doing my best to get this out as reasonably quickly as possible because I want to have some coverage.

And I'll be real, it is also quite fun to cover these things before they are out.

So you're getting my true honest take and not the distilled version of what everyone else is saying.

I'm sure this model is going to cause some pretty crazy waves.

So it'll be nice to have my take out initially separately first.

As always, I feel obligated to remind you i do have early access

but i'm not being paid in any way shape

or form openai has no influence over what

and how i say things just

when they politely asked me to wait until the model is out to talk about it

which makes a lot of sense

but i have to wait for one other thing first today's

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Let's talk about this model a bit

because it is not quite what I expected and it's probably not what you guys expected either.

Especially when you consider the GPT -6 sole just came out like a week ago.

It'll be around a one week gap from 6 .0 sole to 6 .1 sole.

I also want to disclose the numbers i'm currently showing on my screen are unlikely to be exactly accurate

because i am running terminal bench for myself in the first

two times i ran it i screwed things up the third

one seems to be doing much better i didn't run it on medium initially

so there's a miss it there but low high x high

and max although the max run is incomplete

so i'm currently backfilling scores from x high for the ones

that max either got wrong or in a previous run

or didn't do yet because it takes like eight plus hours on some of these tasks.

This bench is nuts.

I'm more skeptical of benchmarks than ever now

that I've been running a lot more of them myself in order to get the coverage I want to give here.

For what it is worth, Terminal Bench 4 is state -of -the -art score here, as is Deep SWE, although this one's weirder

because it goes down on X high and max and stays even on low and high, but those even low and high scores are scoring around what Astra did on high.

The difference being it's doing it for comically cheaper switch over

to the log scale you'll see what i mean this model

on low costs 21 cents versus aster on low costing a dollar 46

and opus 5 .5 on max getting the same score for 14 .65

while i will gladly admit

that deep sweet is far from a perfect measure of how

good a model is at day -to -day code work the fact

that 6 .1 soul is scoring the same as opus and is also 73x cheaper.

is at least worth noticing.

Here's where I'm going to say some of the things that I probably shouldn't.

Considering that GPT -6 Sol came out last week on Tuesday, and this model's coming out this week on Tuesday,

I think it's reasonable to infer that 6 .1 Sol was not meant to be 6 .1 Sol.

There are things I'm not supposed to say, and I'm definitely walking a thin line here by sharing it, so I hope this proves I'm not paid off by OpenAI,

because I'm about to give you guys info that I.. yeah, just let me get through this.

First and foremost, 6 .1 is significantly smarter of the GPT -6 Sol, thereby indicating this isn't just a .1 bump, there is something fundamentally different here.

Next point is that it has meaningfully slower tokens per second.

That tends to indicate the model is bigger, hard to know for sure.

Seems like this model might be different.

Most importantly, we have Tebow's tweet.

What am I referring to there?

Well, right before I started filming, Tebow dropped quite a wall of text.

The thing I want to emphasize here is first off that the pro $200 subscription is back.

But more importantly, more importantly is this sentence, they are changing how they calculate the usage in the sub.

In effect, if you do the math, it will net out at half the dollar in API spend compared to the old pro $200 plan.

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Why in the world would they do this, especially right now where there's allegedly an internal code red

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because opus 5 .5 is so unbelievably good

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and has made the 200 cloud code sub such an unbelievable

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value the only reason in the world tebow would post this

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right now is uh i don't know maybe a new model is coming where their margins aren't as good

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so the ability to subsidize has gone down

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because remember you can get eight to nine thousand dollars of usage in a month on the 200

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cloud code plan

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and you can get over 12 grand on the 200 codex

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plan i did actually run a lot of numbers before this

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and the amount you could get on Astra did go down slightly closer to like eight grand

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or so hard to know for sure because they differ for everyone everywhere

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and it's hard to log all of this stuff

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but for my math roughly nine grand a month the usage

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and that's where the price for this model comes in this model is two dollars per million input tokens

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and ten dollars per million out this makes it way cheaper

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than 5 .6 soul was at launch half the price of 5 .6 Sol after discounts

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and the same price as GPT -6 Sol, one -fifth the price of Astra.

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However, this is not the whole story because cash reads matter.

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And the cash read price for this model is going to be 10 cents per mil in.

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That's a big deal.

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OpenAI has not changed cash read price.

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As far as I know ever before, it's always been exactly 10 % of the normal read price.

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That makes it a 90 % discount.

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And now it's a 95 % discount.

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That means they cut the cash read cost in half massively reducing the cost for real world agentic use

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which to be clear is what we're using these for most of the time

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so this makes the model absurdly cheap for doing real world code work

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that also means

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that they are almost certainly cutting into their margins historically these

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margins are rumored to be as high as 95 percent like

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for every 10 you spend they only have to spend 50 cents

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and as crazy as that sounds it makes a lot of sense especially

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when you consider how expensive it is to make and train these models.

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But that also gives them wiggle room to change things around a bit, which appears to be what's happening here.

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That also means that if they were to keep subsidizing the same level that they were on the subscriptions, that your electricity cost for your sub would be more than you're paying.

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So I get why they have to change this.

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They've kind of just left the details out there for us to reverse engineer.

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So take this as you will 6 .1 coming

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so fast seems to indicate it is not just a new snapshot of gpt6

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so let's talk more about this model as i was showing

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earlier seems really good at terminal bench every time we refresh the numbers change

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because new runs come in and it looks like max failed some things

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that x high passed which is why i just dropped a bit

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but again pretty much all of these even high

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and x high are scoring higher than anything else ever has

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and this is for me running this benchmark on random VMs on my network.

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So not the best suite to test against.

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I also had to drop three particular tasks from it because they expected an H100 to work against, which I make decent money.

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I don't make H100 money, okay?

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But none of this is real world code work.

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So let's talk a bit about that.

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Obviously, we'll have all the fun things like fish slap near the end.

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So stay tuned for that.

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But I just want to fixate a bit on the costs here.

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Because the most expensive run with 6 .1 sol for me was about $1 .38 per task.

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And the cheapest run with Opus 5 .5 was $5 .12.

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That's a 4 to 5x gap from the cheapest Opus to the most expensive Sol.

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So at this point, I would imagine you are hoping

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and praying this model is good and that it can actually replace Opus 5 .5 for day -to -day work.

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And I promise we'll get some good answers to that in a bit.

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But first, we need to talk a bit about model behaviors here, because this model is a part of the GPT -6 family,

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which means it has behaviors that are worth talking about.

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I know I cite this diagram a lot, but there's a reason for it.

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The thing that made me so frustrated with GPT -6 Astra wasn't

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that it was less intelligent than the best models from Anthropic, because it was more intelligent than the best models from Anthropic, and I would argue in many ways still is.

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But there is a problem.

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It is also dumb.

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It is smart and dumb at the same time.

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GWT -6 Astra would just randomly spike into the dumbest bullshit I've seen a model do this year, even worse than some of the small open -weight models I play with.

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It's still so deeply frustrating that Astra does this, because on the other end, when it does well, it's unbelievable.

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But these spikes got to the point where I effectively churned.

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I was only using my codec subs for computer use, and I ended up just leaning on to Fable 5 .1

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and obviously now Opus 5 .5 for almost all of my day -to -day work.

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So have they addressed the spikiness?

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Has GPT 6 .1 Sol fixed the problems that I was so frustrated about with Astra?

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I would say mostly.

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Not entirely, but for the most part, yeah, this is a much better model.

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Its peaks are not as high.

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This is not the incredible revolutionary 3D capabilities that we saw with Astra.

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In fact, I would put it slightly below 5 .5 Opus in most of those types of things.

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It is not as good at computer use as Astra, although it is close enough to the point where I have

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been happy using it for all of my day -to -day work.

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I actually had 6 .1 Sol go through all of my emails

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and find invoices that I had forgotten to pay or was behind on, mostly like investing stuff, and set up new tabs in Chrome for every investment I needed to wire,

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fill out all the details for me, and just leave me to hit send.

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It didn't get a single thing wrong and called out additional stuff

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that I absolutely would have missed if I was doing this work myself

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so I'm literally trusting this model to wire money for me it's trustworthy enough for

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that and honestly I don't know if I would have trusted Astra with

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that due to the spikiness six point one soul much much

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less spiky from what I've heard from the other testers they

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seem to agree with this analysis I know for a fact that Julius

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and Ben who have also been testing have had a much

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better experience with this than Astra in terms of the spikiness

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Julius called the model incredible Ben called it incredibly boring I

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think that's the best place you can be for a model drop like this

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but as i had mentioned before its peaks are not as impressive

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while it does quality work the majority of the time there are some tasks

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that are just at the edge of its capability

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that it will start to do weirder stuff on for the most part it's fine

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but i i'm still reaching for opus a decent bit we'll

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talk more about the comparison later i do default to this

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model for a bunch of stuff though first off as i

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mentioned before computer use i can't wait for ultra fast to be like an actual thing you can use with OpenAI models, because when it is, this model is going to be crazy on it,

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because it can already figure out how to navigate computer use totally fine.

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If it can suddenly do it six times faster, it's going to be unbelievably fun.

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Still not quite as good as Astra, but more than good enough that for the price difference, I wouldn't even think twice about it.

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But as I mentioned before, there are certain things I would still occasionally use Astra for that I am more than happy to use Sol for.

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One of those things is deep code reviews.

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I have still found OpenAI models

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and the like Rottweiler nature where they'll dig into a problem

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and shake it and tear it to pieces until they find every single thing wrong with it.

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I find 6 .1 Sol to be incredibly capable in this particular way.

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So as you can probably guess, I had 6 .1 Sol do some deep audits on Orchestrator V2 and other parts of my real world code bases.

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In my Orchestrator V2 audit, it performed nearly identically to Astra.

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I do believe it was slightly higher a score.

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Okay, not in this analysis, but in my other analysis, it did actually score very, very slightly higher, but it did it at about half the price, $297 versus $584.

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Sonnet was still cheaper, and Opus was slightly cheaper as well.

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The difference being, neither of these models were anywhere near as thorough with their analysis.

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6 .1 Sol dug deep to find things, which is why it was able to get a score comparable to Astra, although it did admittedly burn way more tokens.

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Another task I've had a lot of fun testing with is asking the model to find opportunities to improve a code base.

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In this case, to improve T3 code.

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This is the one where Grok 4 .7 scored strangely well.

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Of course, Astro scored way better at an 83 .8 versus the 80 .7 from Grok 4 .7.

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But GBD61 Sol hit it out of the park with an 87 .4.

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I didn't save all the prices for these runs.

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It's been a bit, okay?

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But for Opus 5 .5, it cost five bucks

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and for sonnet 5 .5 it cost almost nine dollars with gbd61 solid was two dollars

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and 15 cents that's the difference this model's token price is cheaper than sonnet

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but its token utilization is still maintaining openai's usual efficiency which results in just crazy price to performance.

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This whole thread was particularly fun

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because I had Opus 5 .5 review this model with a different name obviously so I didn't know what it was.

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I went and edited the history after.

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And it concluded very quickly this was a frontier tier model.

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Its reviews and bug repros matched the fixes that later merged.

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Its first draft code had real bugs which review bots caught.

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Four reviewers are still running.

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The local T3 code reviews back frontier tier again in a

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blind 10 model bench on the same prompt 6 .1 souls

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placed first of the a7 .4 it found the fish slop runs

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and compared those two it did say 6 .1 souls quality

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output was slightly below astra's as well as the two open ai models with opus 55

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and sona 55 which we will absolutely show you in a bit

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but i do want to call out the price here

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because it only cost seven dollars to run versus 15 for sona 55

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and 50 for opus opus's honest tier call was

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that this model is incredible for scoped work top of the frontier refine what's wrong

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and tell me the truth i would choose it over astra

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and about level with opus for a long unattended building this was below frontier follows its process rules even

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when they stop all progress

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and it does not ask for help this i absolutely noticed i had mentioned before well a few times now

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that my ts rust port

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that i'm making with opus 5 .5 is going way better than

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when i was working on that same port using Astra and Sol in the past.

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I had that port running for a while with this model and it made no progress.

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It burned a shitload of tokens, but it didn't actually improve the compiler at all.

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Opus was able to from scratch restart it

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and get it working in a day after I had spent months

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and hundreds of thousands of dollars in tokens with this model as well as with Astra and 56 Sol.

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Opus did it in like a grand in like a night with just two subscriptions with the cloud plan

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so for unattended long like heavy rewrite type stuff anthropic is just comically far ahead right now

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and it also didn't have great judgment

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when i was using it for managing my fleet

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and for those wondering my fleet is all the computers i use for running all my agents

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and code because one computer is far from enough i don't run any of them on this macbook now

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so when i use this model to manage the fleet it made a couple dumb mistakes here

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and there to be fair so is opus astra is the only one

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that hasn't really made too many of those dumb mistakes

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but like I'm gonna be

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so real I am entirely done using Astra for this model

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after I had Opus do all of this review I asked

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it how much does it think this model should cost it

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guessed $5 per mil in 50 cents cashed in 30 per

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mil out putting it at Opus's prices roughly it said that

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because it's performing like Opus its speed should add a premium

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because it is quite fast and it's not a pro model

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which is where it expects those higher like hundred dollar out tiered pricing things to come.

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ProModel isn't really a thing anymore.

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We just use Fable and Aster, but you get the idea.

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This is the funniest part of the whole thread, though.

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If OpenAI wants people to adopt it, I would expect $3 per mil in, $0 .30 for cashed, and $20 instead.

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That would still be a fair price for what it does.

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To which I responded, if I told you it was $2 in, $10 out, and $0 .10 per mil cash read, what would you think?

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I'd call that very aggressive pricing.

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For how you use it, it costs is about a quarter of what I guessed.

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The cash price does most of the work.

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Agent workloads are 96 % cash reads.

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So 10 cents for cash reads matters more than the $2 and $10 headline prices.

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When I looked at all of my sessions, its price guess would have been $5 ,700.

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But after looking at these new prices, it redid the math and it would have been $1 ,550.

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That is a massive decrease.

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And for all my PR review type tasks, it was expecting those to be up to $10 and it's actually only up to $3.

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And that's where like heavy PRs with 10s of 1000s of lines of code, according to Opus.

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So don't blame me blame Opus for saying this.

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First off Opus says it becomes the default model for scoped work.

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Second off, it says that bloated system prompts barely matter anymore because of the cash pricing.

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It's just noise.

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Third, it says long loops are still a bad idea, but not because of money.

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It's because according to it, the TSRL support wasted four days and made no progress at all.

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And Opus even said they'd be suspicious of it lasting.

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They expect this price to go up in the future.

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I cannot fathom OpenAI ever increasing the price for a model, but Opus thinking they will is hilarious and shows just how good a value the model is.

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I love this call out here.

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GBD 6 .1 Sol did three rounds of work for about half the cost of Sonnet's single round.

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A lot of this comes down to how context was managed, both because 6 .1 Sol is much more efficient so it's not doing as many calls that bloat the context.

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It's not outputting as many tokens that are like building up over time.

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So the average number of tokens being read per request was only around 110 ,000 tokens versus 360 ,000 for Sonnet 5.

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The result is that Solout used under half as many input tokens as Sonnet making this model significantly more efficient.

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Speaking of efficiency I want to talk about these deep SWE scores a tiny bit more

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because this is a weird bench for me to have forked

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and include in these things i actually did it for a different reason not to compare against 6 .1 sol

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but to compare against a new release from open router

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jev router open router added jev router to try

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and optimize costs with your requests

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and i thought it was an incredibly stupid idea once i

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started running it against benchmarks i confirmed it's an incredibly stupid idea it turns out a model

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that cannot reason

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that is given a prompt into no context cannot make a good decision around how hard the problem is

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and jev router ended up being deep seek v 4 .1

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flash router for the vast majority of its runs around 60

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of all the requests went straight to deep seek 4 .1 flash

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so didn't like it that much it also routes to other smarter models

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which should give it more of an advantage

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but it ended up being more expensive than gpt6 astro was on low

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while also taking four to five times longer

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because six astro low took 4 .6 minutes

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and jev router took 20 jev router's average task took 104

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steps whereas gpt6 astros took 19 you get the idea it wasn't very good

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but the whole point of jev router is

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that it would be as cheap as possible to get a certain score

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that was the promise on the tin whether

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or not you believe them is up to you not me

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i think it's bullshit regardless jev router was routing to deep

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seek 4 .1 flash for the majority of its requests despite

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jev router routing to the cheapest possible small open weight models

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from whatever provider will give it away for free 6 .1

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soul on low got the same score for an eighth the price.

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OpenAI is here to destroy any wins anyone else has in terms of efficiency.

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Completing this bench in 4 .8 minutes for 21 cents with

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the second highest score I've ever seen on it is a massive achievement, tying Opus 5 .5, which took 50 minutes per task on max,

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a tenth the time and a 70th the price for the same score.

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If your work fits within the things 6 .1 Sonnet does well, you should probably use it for everything.

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But if your work doesn't fit in it particularly well, you should probably keep using Opus, and maybe give Opus the ability to call 6 .1 Sol when it should for various tasks.

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I'm almost certainly going to be setting things up so

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that Opus 5 .5 can call 6 .1 Sol to do investigation work to try and root cause bugs,

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to do analysis of codebases to figure out what things need to be touched and why, to help me triage real world work, to help me review the work that Opus does and more.

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I'm kind of spoiling the ending here, aren't I?

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I'm going to keep using Opus 5 .5 for now.

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Before I explain why, let me do the thing that I'm most excited for.

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Fish Slop!

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The first thing you might have noticed is the inclusion of Slop in Fish Slop.

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This model did the horrible thing I hate, where it surrounded the game in a bunch of absolutely garbage UI

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and coming to this right after the 5 .5 sonnet demo hurts me deeply

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because sonnet 5 .5 did not make graphics anywhere near this good looking

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but at least it made a ui that was nowhere near this awful

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and man do i wish the bad ui is where the

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issue stopped i will turn on the sound oh god it's blaring

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it is stunning looking the fish are some of the best

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the model for the sub is way better the propellers work way better i'm gonna mute the sound

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because that is looking pretty bad i haven't even heard it honestly

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but damn like looks beautiful but

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if you actually are playing it one of the first things you'll notice is

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that the movement feels significantly worse than it does in either the Opus

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or the Sonnet versions that I have demoed in the past.

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Yeah, it moves jank.

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It also has a significantly worse frame rate than the versions from the other models.

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It does have higher graphic fidelity, so that makes sense.

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The models here with the plants are significantly better than they were with the Sonnet version.

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The derives of the fidelity of the extras in the tank is absolutely hilarious.

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Like, yeah.

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But goddamn, I'm so tired of the unnecessary text everywhere.

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This model doesn't worse than almost any I've ever seen before.

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We got take a breather, paused.

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Your little world can wait.

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Back to the reef.

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Start start a new tank, slop01, feeder submarine, a little underwater chaos,

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the big little goal, your little ecosystem, little fish become big earners, four meals, and they're all grown up.

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Make the family a little bigger, a little golden overachiever.

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There's so many of these.

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There's like 20 plus of them.

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And I promise you guys, as soon as I saw this, I took a screenshot, i sent it to open ai

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and i crashed out in the slack

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because i cannot fathom how they haven't fixed this problem this

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model is unacceptably garbage at ui it has regressed again and

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if you're looking for a model that can make front ends that don't suck

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go spend your money somewhere else

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because it should not be spent here this model sucks at

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front end it sucks at design it has no taste

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and you're gonna have to bring your taste yourself still

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but it is admittedly really good at blender

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if you give it like a screenshot of a thing you want it to model in 3d

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and say hey you have blender over the cli go make

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this it will it'll do a pretty damn good job

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but i would never have it make the actual mechanics for my games

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because it feels awful to play it also has like nowhere

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near as much gameplay loop in fact the first time i

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tried demoing this before filming it just randomly game overed as

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i was like getting started in the first 30 seconds

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and never like said why actually i think i technically beat it

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i also want to like take this version and hand it to opus

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or sonnet and say hey can you make this play better

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because the graphics are good but the game sucks but

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when you combine how cheap it was to make this

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because like this was five dollars i think to generate that's pretty insane

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and if you combine that with like ultra fast if that ever happens

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suddenly you're going to be able to make a game in

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a few minutes on demand we're actually now getting to

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that threshold where game development is about to flip upside down

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because of how models are finally understanding three -dimensional space

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and the tooling necessary to do these types of things it's

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happening as per usual i was not allowed to put the

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code i wrote with this model inside of t3 code

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or other open source projects during the testing window

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so i had to use it exclusively on my internal projects like lakebed as well as for auditing other work

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which means i mostly uses for auditing other work and I was very impressed.

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This is a real PR I was working on to fix

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a bug where my new little work tree like setup window

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that would appear in a new thread in T3 code would disappear if you left and came back.

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I had cloud code work on this but this problem went pretty deep

421

so I wanted to make sure that whatever solution I came up with was very very very well vetted.

422

While I personally still do not trust this model to write

423

the code I'm trying to land I absolutely trust it to review things.

424

Ignore the GPT -6 soul there, just a placeholder.

425

So when I had 6 .1 soul look through this, it found real problems that were entirely missed by Fable and by Opus.

426

First, it called out that follow -up messages can stay blocked after the agent starts, which is very annoying if you want to queue a message.

427

And also that recovered setup progress was disappearing too early.

428

It figured all of these things out with a combination of reading the code and analyzing it as well as computer use.

429

And it's able to prevent me from merging a real regression in t3 code.

430

So I literally just copy pasted those things to Claude and then told it to take another look.

431

Said better, but I'll still fix two small gaps.

432

Remember, I can't use this model of the code for this project at the time.

433

So I copy pasted that again over to Claude and it eventually got it good enough.

434

And then I finally merged.

435

But that is what I like this model for.

436

And I cannot wait to push its limits for actually coding.

437

Although I will say from the code that I did have the misfortune of reading, it is harder to justify merging this code than it is

438

for code from opus normally i would make you guys wait for the opus versus soul video

439

or the sonnet versus soul video

440

but i'll just spoil the details now i like using them in tandem

441

because i find soul to be way better at reviewing

442

and digging into the details but i find opus a more pleasant collaborator

443

and significantly better at actually implementing code without getting blocked constantly throughout its work

444

and even now with the rust rewrite of typescript i find

445

myself in a similar pattern where i have opus 5 .5 just going

446

and going and going making the code base work and work well.

447

And then I had Sol come in and do an audit.

448

And this is the funniest part.

449

I remember before I said that I had Sol and Astra working on that TSROS port for effectively months, I told Sol to come in,

450

and it got it from 83 .7 % to 100 % in under a day.

451

I was blown away by that, that it had somehow unblocked the work that Astra and Sol were doing as well as 6 .1 Sol.

452

And I was absolutely blown away by that, that it had taken the work that 5 -6 -Soul, 6 -1 -Soul, and Astra had done over months

453

and got it unblocked where it had been stuck for weeks and finished it.

454

I was much more blown away when I had 6 -1 -Soul take a look at that work and critique it.

455

And what it brought up was that of the 1 .8 million lines of code, 1 .3 million were not being used.

456

The reason was because Opus concluded all of the code from all the other agents was useless slop

457

that had no chance of being recovered.

458

And it chose to rewrite it from scratch itself in another crate.

459

So on one hand, the only reason the code worked was Opus.

460

But on the other hand, the only reason the slop was still around was also Opus.

461

So I had to have this model come in

462

and clean up the mess that other OpenAI models had made because Opus didn't even notice the mess was still there.

463

What I'm trying to say is this model absolutely has a place in your workflows.

464

It could probably even be your default coding model and you wouldn't have too many issues with it.

465

But I still find Opus to be a better collaborator overall.

466

That said, I have almost no reason to use Sonnet anymore because this will effectively take its place.

467

And you bet your butt the moment this model comes out, I'll be going and making adjustments inside of my clod config

468

because I already have it set up so

469

that I can use Sol inside of clod code

470

because I want to make sure Opus knows this is the model to have review its work

471

and investigate the things going on in the code base.

472

This is a damn good model, and I'm really happy to have it.

473

I wish we had something bigger, smarter, and more capable overall.

474

I was really hoping for something to truly dethrone Opus 5 .5 as my daily driver.

475

This isn't it, and I'm not planning on canceling any of my Claude subs as a result of this release, but I am planning on taking a lot more advantage of my Codex subs in my day -to -day work,

476

admittedly in Claude code.

477

This is an awesome release and an unbelievable price for what you're getting, but this does potentially mark the start of the end of the subsidization era, so make sure you're subscribed so that you can be here

478

when I cover all of that

479

and more god I hope this doesn't get me canceled online

480

I have no idea how others feel about this beyond like

481

a handful of early access testers I've talked to I legitimately don't know

482

if you are going to love it or hate it

483

or land somewhere between I will know in a few hours I guess

484

because it's a yeah it's three in the morning I am

485

going to go to bed now this is a tiring one

486

hopefully I did a good job let me know in the comments

487

and until next time peace nerds god I'm so dead All right

📺 उसी चैनल से

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इस पाठ की शब्दावली और बोलने के सुझाव

यह C1 स्तर का स्पीकिंग पाठ वीडियो “OpenAI fights back” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: model, opus, code, sol, price। इस वीडियो में शैडोइंग के लिए 487 वाक्य और 6233 शब्द हैं। बोला गया हिस्सा 30:54 लंबा है। वक्ता तेज़ बोलता है, लगभग 202 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। केवल 81% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 11 सबसे कठिन शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
router संज्ञा	/ˈɹaʊ.tɚ/	राउटर
frontier संज्ञा	/ˈfɹʌntɪə/	सीमा, सरहद
astrology संज्ञा	/əˈstɹɒlədʒi/	ज्योतिष
randomly क्रिया विशेषण	/ˈɹændm̩li/	यदृच्छया, यादृच्छिकतः
benchmark संज्ञा	/ˈbɛn(t)ʃmɑːk/	मानक, मापदंड
rust संज्ञा	/ɹʌst/	मोरचा, जंग
conclude क्रिया	/kənˈkluːd/	समापन
paste संज्ञा	/peɪst/	लेपन
subscription संज्ञा	/səbˈskɹɪpʃən/	सदस्यता
surround क्रिया	/səˈɹaʊnd/	घेरना
mechanic संज्ञा	/mɪˈkænɪk/	मिस्त्री, मेकेनिक

वीडियो में आने वाली फ़्रेज़ल वर्ब

शब्द	उच्चारण	अर्थ
come back क्रिया		वापस आना
fill out क्रिया	/fɪl ˈaʊt/	भरना

दोहराने लायक वाक्य

वीडियो के छोटे, पूरे वाक्य जिन्हें आप रोज़ की बातचीत में इस्तेमाल कर सकते हैं:

What am I referring to there?
I didn't save all the prices for these runs.
I'd call that very aggressive pricing.
I'm kind of spoiling the ending here, aren't I?
Said better, but I'll still fix two small gaps.

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Present perfect continuous have/has been + -ing — पहले शुरू हुआ काम जो अब भी जारी है	we've been getting · i've been moving · I've been running
शर्त वाले वाक्य if + उपवाक्य, will/would + क्रिया — शर्त और उसका नतीजा	if you do the math, it will net
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	has made · has gone · has not changed
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	is called · being paid · was not meant

ध्यान देने योग्य उच्चारण

वक्ता 115 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे I'm, didn't, I've। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“th” की ध्वनियाँ: anthropic /ænˈθɹɒp.ɪk/, fathom /ˈfæðəm/
“sh” और “zh” की ध्वनियाँ: benchmark /ˈbɛn(t)ʃmɑːk/, screenshot /ˈskriːn.ʃɑt/, subscription /səbˈskɹɪpʃən/
लंबे शब्द — बलाघात सही रखें: importantly /ɪmˈpɔɹ.tənt.li/, astrology /əˈstɹɒlədʒi/, unbelievable /ˌʌnbɪˈliːvəbl̩/, surprisingly /səˈpɹaɪzɪŋli/, collaborator /kəˈlæbəɹeɪtɚ/

हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:

शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: slop /slɑp/, screenshot /ˈskriːn.ʃɑt/, sponsor /ˈspɒn.səː/
/æ/ — “ऐ” से ज़्यादा खुला: anthropic /ænˈθɹɒp.ɪk/, randomly /ˈɹændm̩li/, aster /ˈæs.tə(ɹ)/, collaborator /kəˈlæbəɹeɪtɚ/, fathom /ˈfæðəm/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर router, frontier, astrology जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

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वीडियो में आने वाली फ़्रेज़ल वर्ब

दोहराने लायक वाक्य

इस वीडियो का व्याकरण

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इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?