- 2 days ago
Qwen 3.7 Max is a new powerful AI model in 2026. In this video, I tested its performance and compared it with Gemini, DeepSeek, and other AI tools.
👉 Check website Blog:
https://www.aitoolsnexa.com/
In this video:
- Qwen 3.7 Max features
- Real testing results
- Comparison with top AI models
- Should you use it?
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ai tools 2026
qwen 3.7 max
new ai model
chatgpt alternative
gemini ai
deepseek ai
ai comparison
👉 Check website Blog:
https://www.aitoolsnexa.com/
In this video:
- Qwen 3.7 Max features
- Real testing results
- Comparison with top AI models
- Should you use it?
Subscribe for more AI updates 🚀
ai tools 2026
qwen 3.7 max
new ai model
chatgpt alternative
gemini ai
deepseek ai
ai comparison
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TechTranscript
00:00Looks like Alibaba does not sleep as they're already back with the launch of a new flagship
00:05model, the QN 3.7 Max, which is built for the agent era. The QN 3.7 Max is designed
00:12as a
00:13versatile agent foundation model that is capable of advanced coding, plus debugging, quite good
00:20at front-end prototyping, complex multi-file refactors, office workflow automation, multi-agent
00:26orchestration, and long horizon autonomous execution. Now performance-wise, the QN 3.7 Max
00:33is performing strongly across multiple benchmarks like Terminal Bench 2.0, Sway Bench where it scores
00:3960.6, as well as many other agent and coding benchmarks. You can see that there's massive
00:46gains and it is basically on par with models like Opus 4.6 Max, Himike 2.6, in certain cases
00:53even
00:54surpassing it, and I personally believe that this is the best Chinese model that is out there right
00:59now. It also demonstrates exceptional strength on difficult reasoning evaluations alongside strong
01:05multilingual capabilities. But what's wild is that Alibaba is now genuinely entering conversations
01:11alongside proprietary giants like Anthropic, Google, and OpenAI because this is the closest
01:18QN has been in the frontier race. Because the QN 3.7 Max now scores a 56.6 on the
01:25Artificial Analysis
01:26Intelligence Index. That is a 4.8 point boost in terms of overlapping the QN 3.6 Max preview.
01:34This is with major gains in scientific reasoning, coding, and agistic capabilities. If you want the
01:40best AI tools, workflows, and drops before everyone else, join my free newsletter with the link in the
01:46description below which is completely free. QN 3.7 Max just outperformed the Claude Opus 4.7 as well as
01:53GPT 5.5 on a real long horizon agistic coding task where models had to iteratively improve a self-training
02:01tetris bot across 10 autonomous loops where QN actually achieved the biggest improvement with a 56% gain
02:08at the lowest cost which was $1.30. Now, this is massively outperforming Opus 4.7
02:15which had gotten a 28% gain but it costed about $12.15 and GPT 5.5 had incurred a
02:257% gain but was a lot
02:28cheaper at around $2.85. But you can see that Alibaba is moving fast and it is efficient while
02:34getting the task done. Now, in my own personal benchmark, the World of AI benchmark suite, you can
02:40see that in our leaderboard, the QN 3.7 Max is high up there, ranked number 8th right now overall
02:47across
02:47a lot of the different domains like front-end, gaming, 3D graphics, SVG, all of the different
02:54domains that you see listed over here. And you can clearly see that this model does exceptional in
02:59almost every front while being comparable to the Frontier models. Now, one of the most differentiating
03:05factors of why this model is good is because of how perfected it is at long horizon planning
03:11and execution. This is a model that is able to execute quite well autonomously in these long
03:18horizon tasks because it is something that is able to sustain coherent reasoning across something like
03:24a 35-hour autonomous executions workflow. This involves using multiple tool calls. In this case,
03:31it used 1200 tool calls continuously where it is able to debug, profile, rewrite, and even improve
03:39code without losing context or drifting off. Now, interestingly, this model is actually not
03:44multimodal, meaning it is not able to process any of the other modalities like audio, image, and video.
03:50It is currently priced at $2.50 per 1 million input tokens and $7.50 per 1 million output tokens.
03:58To start using this model, you can easily do so through their chat, which is something that you
04:03can access completely for free and create an account to use the Coin 3.7 Max. You can change
04:08it between the thinking and fast mode. You can also access it via their API. To start things off,
04:14let's take a look at the macOS clone that the model did an exceptional job at generating. This is where
04:19it had done quite well in generating all the applications. And right away, within the bottom toolbar,
04:25you will notice that all of the different applications have an SVG icon represented for
04:31each of the apps, which is incredible. But the fact that it was able to use the full context to
04:37actually generate multiple apps is the most remarkable thing. Now, what I really like is that
04:43on the top bar, you have all the functional components that you would expect of a macOS.
04:48So everything on the top toolbar actually works. You have the ability to change the brightness,
04:53which is actually kind of cool. You can also use spotlight. And you can even use the launchpad
04:57as well to open up almost anything. Now, this is where you have the Finder app, everything has been
05:03coded out, which is nice. You have Safari, which doesn't look like it coded up properly. But still,
05:08regardless, you have all of these different applications that were generated. And that is just
05:13incredible because that's what you're looking for. And the fact that it's able to actually
05:17generate almost all of these things precisely to what a macOS looks like is the best thing ever.
05:24Now, photos doesn't look like there's much to it, as well as the maps, but everything else looks like
05:29has been generated precisely. The app store, you have the system preference, the terminal,
05:35the calculator. You also have a text editor, a paint app, which actually works. So that is nice.
05:41And then you also have a snake game that was added. So that's actually pretty cool.
05:45Now, there is also weather app, clock, as well as a preview. So overall, it did a great job in
05:52generating almost all of the components of a macOS. So that is definitely nice to see.
05:58Now, front end wise, I do believe that the model does a pretty decent job, but not the best.
06:03It is decent at most of the prompts. You can see most generations with all the different
06:09front end prompts I had sent in are decent, not the best. It is kind of tacky, but overall it
06:16does
06:16get the job done. I will give you one thing though, when it comes to creativity, it does a pretty
06:20decent
06:21job. But still, regardless, you do notice that there are certain components that aren't well
06:27generated with instructions that are given. But when you are to properly detail what you expect and what
06:33to actually use for the generations, it does do a decent job like the scroll triggers or like using
06:40different typographies. When it comes to following instructions, I believe the model does a pretty
06:45good job at that. Really liking the scroll triggers in most of these generations. It does actually the
06:51best out of any of the Chinese models that I've tested. So that is actually a good thing.
06:56When you are to actually provide it a screenshot as a reference, it does a decent job. And this is
07:03where it's able to clone UIs quite well. And this is where I'd requested it to clone an Airbnb image,
07:09which I had provided. And you can see that it did a decent job in cloning almost all of the
07:15components
07:15thoroughly. Also, this is kind of interesting. This is where the Quen 3.7 Max had basically generated
07:22this editorial SAS based off the input that I gave it. And this looks much like any of the outputs
07:29that Claude is actually generating. And you can see that with the typography, as well as the color
07:34styling, which looks really, really similar to what Claude actually outputs, which leads you to think,
07:40is Quen actually training off of Claude outputs? Here, I'd requested the model to actually generate a
07:46voxel pelican on a bicycle. And this is a pretty good generation right here. You can see that it
07:51creatively actually generated in voxel art. And you can see that this is a real world front end task
07:58with 3D engineering. And you can see that the model did a pretty decent job in creating the
08:03functional 3S gene, the 3JS screen, the 3JS scene with the strong spatial reasoning. You have rendering
08:11ambience as well. And overall, with its creativity and visual quality from a single prompt, it did a
08:17great job with this output. Another 3D scene atmosphere prompt was to actually generate a
08:24Zelda low poly landscape. And this is where it did a really good job in the ambience as well as
08:30creating
08:30the environment. Now, not everything is perfect. But the fact that we got a lot of the components
08:36nailed down is great. And you can see that with its creativity, it's able to actually create
08:40this 3D poly environment. This is a benchmark that I had requested the model to create a realistic
08:47aquarium. And this is something that tests how well the model is obviously in 3GS, but how it's able
08:53to simulate an aquarium where it is able to manage the physics of all of the individual fishes. And you
08:59can see this is one of the better generations I have seen. The fins properly move for each of the
09:06different fishes. You can see the UI control with the panel, you have the rendering system and real time
09:12optimization. What's really cool is if I am to enable the feeding mode, I can actually click on
09:18the different or the top of the water. And you can see that there is food actually being dropped into
09:24the aquarium. And this is where the fishes all rise up to eat up the food. That's a small little
09:30feature. But the fact that is able to actually code that out and specifically able to evaluate
09:36how well this long-to-form front-end engineering task is able to incorporate things like spatial
09:42reasoning. You have different visual elements and the quality is definitely nice to see. And the fact
09:47that it's able to thoroughly generate all this in a single prompt is nice to see. When it comes to
09:53animation logic, rendering quality, as well as styling accuracy, this is where the model actually does a
10:00really good job. And it's able to excel in this case where it is able to output the front-end
10:06code that is thoroughly generating all of the components accurately based off the prompt that
10:11you provided.
10:15In SVG, the model does exceptionally well. And this is something that I've really noticed with
10:21a couple of these different prompts, which you'll see. This is the SVG world map. This is where almost
10:27every model fails at. And you can see that the model does quite bad. I don't know why my benchmark
10:32is saying good. I should tweak that. But you can see in these other prompts like the SVG pelican
10:37illustration. You can see that it does a decent job with the generation. It tried to add animation.
10:43It is doing quite well with the animated NYC SVG. It's able to properly and thoroughly depict what the
10:50SVG of New York City would look like. You can also see that with the animated infographics,
10:56the SVG painting prompt, as well as the butterfly, which you see over here. So almost all of these prompts
11:03are generated quite well and exceptionally well with SVG code based off the prompt that was given.
11:09Next is where the model was requested to create a 3D solar system. And you can see that it did
11:15a
11:15decent job. All the different planets have their own attributes. Saturn has its ring. You can see that
11:21Jupiter has its eye. And what you'll notice is the lighting and architecture to actually generate this
11:29scene is thoroughly accurate. As you can see that there is the dark side of all the different planets
11:35that are facing away from the sun, which is definitely nice. And the fact that it even generated
11:41the asteroid belt. And overall, it did do a decent job. Now, one thing I'm not too sure,
11:47maybe someone can let me know in the comments, the Saturn actually moved like that. Next is where I'd
11:52requested it to create a Minecraft clone. Now we can actually take a look at this by downloading it.
11:57And this is the Minecraft clone that it had generated. Now right away, what I like is that
12:02there is actually water, but the water physics is definitely not generated properly because you can
12:08actually walk through it and you can't really see everything thoroughly like what you would in the
12:13actual sandbox game. But the fact that you're able to break blocks, you have different time environments,
12:18you also have the ability to place blocks, you can break it. And one thing I've also noticed is that
12:24it even generated actual cave systems. So if we are to actually dig deep into the terrain,
12:32you might actually run through a cave system. And you can see that there's different blocks
12:37that have been added inside this cave system. If you liked this video and would love to support
12:43the channel, you can consider donating to my channel through the super thanks option below. Or you can
12:49consider joining our private discord where you can access multiple subscriptions to different AI tools
12:54for free on a monthly basis, plus daily AI news and exclusive content, plus a lot more. I gotta say,
13:01the Quen 3.7 Max is surprisingly solid at architectural reasoning as well as working through different web
13:08development tasks. As you saw through my benchmark, as well as the other different benchmarks that
13:13showcased at the start of the video, you can see that the model does quite a well job at sustaining
13:18coherent autonomous workflows. And it's able to get the task done based off the detailed prompt that you
13:24give it. And this is something that could be a handy tool to use in many different workflows due to
13:30it
13:30following different instructions thoroughly based off the prompt that you give it. I'll leave all the links that
13:36used in today's video in the description below. But with that thought, guys, thank you guys so much
13:40for watching. I hope you enjoyed today's video. Make sure you go ahead and subscribe to the second
13:44channel. Make sure you join the newsletter, join the discord, follow me on Twitter. And lastly,
13:48make sure you guys subscribe, turn on the notification bell, like this video, and please take a look at our
13:52previous videos so that you can stay up to date with the latest AI news. But with that thought, guys,
13:56have an amazing day, spirit of positivity, and I'll see you guys fairly shortly. Peace out, fellas.
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