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Hy4 preview powers dynamic builds for a 770B open model. 770B total parameters, 49B active per token, native 1M context, Apache 2.0, direct vLLM and SGLang support, plus official FP8 weights. In this demo, Hy4 creates a colorful, playable 2D space shooter game directly from a prompt, featuring active... show more

Tyler Wayne

407,163 subscribers

77,986 views • 12 days ago •via X (Twitter)

Gaming Science & Technology

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31 Comments

Amazing12 days ago

Direct support for common serving tools can make testing far simpler.

iano12 days ago

Large context windows become useful when they support complex creative projects.

E12 days ago

Seeing a model build something interactive is more useful than another benchmark alone.

Jayden Cole11 days ago

Open models allow developers to verify claims in their own environments.

Bonita🤎12 days ago

The active parameter count matters when speed and capability need balancing.

ETHAN JAMES11 days ago

Developers value clear integration with standard serving engines out of the box.

Alice The Ai Expert12 days ago

770B parameters building a playable space shooter from a single prompt with particle effects and score tracking open source Hy4 is showing serious power!

Kiage Clinton, KC12 days ago

Open licensing gives more teams a chance to experiment with their own ideas.

EDDY VU12 days ago

49B active per token is super lean for a model this size. Having official FP8 weights ready at launch saves so much quantization guesswork.

Fatuma11 days ago

Transparent architectures make it easier to evaluate real operational costs.

MAUREEN11 days ago

Balancing scale and serving efficiency is essential for practical engineering.

RAZA | AI EXPLORER12 days ago

A 770B open model building playable games from a prompt is seriously impressive.

Elise Tech Ai11 days ago

Hy4 preview is insane — 770B open model building a playable game from a prompt is next-level!

RICHIE..😎11 days ago

Combining huge scale with efficient token activation makes deployment much more practical.

Aaliya12 days ago

770B parameters is a huge model for this kind of work.

Weigo12 days ago

A playable game is a practical way to show what dynamic building can look like.

King Levi11 days ago

Flexible open architectures make it easier to tailor models to specific niches.

George11 days ago

Efficient token usage makes large open models much more economical to run.

Stones11 days ago

Tools that generate functional applications open new doors for indie developers.

REX JAMES11 days ago

Rapid interactive prototyping highlights the practical utility of modern architectures.

JA KISII™ 🇰🇪12 days ago

Open weights give developers more room to test real workflow limits.

Betty11 days ago

Open releases encourage wide testing and unexpected real world applications.

CATHEY11 days ago

Interactive demos demonstrate model capabilities far better than static benchmarks.

Sani Ai Tech12 days ago

Seeing interactive arcade scenes drop from a brief is wild

Ochora🇰🇪☆, KC12 days ago

Long context is useful when a project needs the model to retain more of the build.

Chole Syntax Expert AI11 days ago

770B open model building playable games from a promp Hy4 preview is wild Gonna test it on WorkBuddy

Alex Ryan11 days ago

Open source access invites community collaboration and rapid workflow refinement.

Tyla11 days ago

Direct support for optimized inference engines accelerates production adoption.

chenly🥹11 days ago

Building interactive software directly from prompts shows significant pipeline progress.

JOMBA11 days ago

Running capable models on accessible hardware lowers the barrier for developers.

JADUONG11 days ago

A massive context window keeps complex logic consistent across entire builds.

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12,224 views • 16 days ago

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63,568 views • 23 days ago

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Manish Kumar Shah

42,263 views • 22 days ago

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Aryan Rakib

63,296 views • 11 days ago

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Ziwen

18,451 views • 24 days ago

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Rebecca Adson

61,820 views • 4 days ago

I’ve been testing Hy4 preview in WorkBuddy, and the most interesting part is not simply the model size, it’s how much practical work it can handle with a relatively focused active parameter count. Hy4 preview brings together stronger code understanding, generation, and editing; improved document and information processing; workflow automation; web and game development; cross-tool collaboration; and more reliable completion of complex, multi-step tasks. In other words, it is designed for work that requires planning, tool use, iteration, and follow-through, not just a quick answer in a chat window. Compared with its initial release, the current Hy4 preview is noticeably faster and better-performing in practical workflows. Following an upgrade released yesterday, it can complete tasks with fewer conversation rounds and lower token usage, while reasoning more quickly and making the overall user experience feel smoother from the first instruction to the final result. For my test, I gave it a demanding Three.js game-prototyping task with a 770B-parameter model and 49B active parameters. The result was more revealing than a simple first-look demo: Hy4 preview handled the core logic, edge cases, and follow-up changes while maintaining the broader context of the project. That combination of capability, speed, context, and active compute is what makes its cost-effectiveness worth examining. A fair evaluation should use the same prompt and environment configuration across models, changing only the model itself. That makes it easier to assess task completion, planning quality, tool-calling stability, reasoning speed, token efficiency, and performance over longer workflows without confusing the result with different settings. If you want to test the model yourself, access Hy4 preview through WorkBuddy and see how it performs on a real coding, document, automation, or creative task: Tencent Hy Tencent AI WorkBuddy

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Tyler Wayne

56,152 views • 11 days ago

I wanted to see where Hy4 preview would stop, so I gave it a deliberately demanding prompt: “Build a working Cyber Incident Command Center.” Not a mockup. A real, responsive app with a live threat map, animated attack routes, incident simulation, filters, investigation panels, event logs, ownership controls, and working isolate/resolve actions. Hy4 preview planned the build, wrote the application, ran it locally, tested the interactions, checked the mobile layout, fixed issues, and opened the finished product for review. Then I clicked “Simulate.” A new incident appeared, the dashboard updated, I filtered it by severity, opened the evidence trail, isolated the affected system, and resolved it. The KPI counters changed with the action. That’s a much more useful coding demo to me than watching code appear in an editor. Hy4 preview is Hunyuan’s third major release in six months. It’s fully open-sourced, with 770B total parameters, 49B activated, and a 1M+ token context window. Tencent built it around productivity scenarios through close work with experts in software engineering, gaming, finance, and security, plus direct co-design with tools like WorkBuddy. In an internal blind test covering 203 engineering tasks and 163 Tencent experts, it scored 2.99/4—slightly ahead of Kimi K3 at 2.94 and GLM 5.3 at 2.92. And the pricing stays practical: $0.834/M input $2.501/M output $0.042/M cache hits If you want to test it yourself, Hy4 preview is free inside WorkBuddy for the next two weeks, for a limited time. Tencent Hy Tencent AI WorkBuddy

I wanted to see where Hy4 preview would stop, so I gave it a deliberately demanding prompt: “Build a working Cyber Incident Command Center.” Not a mockup. A real, responsive app with a live threat map, animated attack routes, incident simulation, filters, investigation panels, event logs, ownership controls, and working isolate/resolve actions. Hy4 preview planned the build, wrote the application, ran it locally, tested the interactions, checked the mobile layout, fixed issues, and opened the finished product for review. Then I clicked “Simulate.” A new incident appeared, the dashboard updated, I filtered it by severity, opened the evidence trail, isolated the affected system, and resolved it. The KPI counters changed with the action. That’s a much more useful coding demo to me than watching code appear in an editor. Hy4 preview is Hunyuan’s third major release in six months. It’s fully open-sourced, with 770B total parameters, 49B activated, and a 1M+ token context window. Tencent built it around productivity scenarios through close work with experts in software engineering, gaming, finance, and security, plus direct co-design with tools like WorkBuddy. In an internal blind test covering 203 engineering tasks and 163 Tencent experts, it scored 2.99/4—slightly ahead of Kimi K3 at 2.94 and GLM 5.3 at 2.92. And the pricing stays practical: $0.834/M input $2.501/M output $0.042/M cache hits If you want to test it yourself, Hy4 preview is free inside WorkBuddy for the next two weeks, for a limited time. Tencent Hy Tencent AI WorkBuddy

Clara Bennett

12,864 views • 22 days ago

🚨 Tencent Hunyuan just dropped Hy4 preview—and it’s already making its way into the open-source big leagues. Three major releases in six months. That’s seriously fast. Hy4 preview comes with: 770B total parameters 49B active parameters A 1M+ token context window And it’s built with one clear focus: productivity. We’re talking coding, office work, science, software engineering, gaming, finance, security, and more. But here’s the part that really caught my attention: Hy4 preview wasn’t developed in isolation. It was co-designed with real products like WorkBuddy. Tencent worked with experts from across its ecosystem to build the training data, then tested the model on real-world tasks. In a blind test involving 163 internal experts and 203 engineering tasks, the scores were: Hy4 preview: 2.99/4 Kimi K3: 2.94/4 GLM 5.3: 2.92/4 And performance isn’t the only impressive part. On benchmarks, Hy4 preview clearly outperforms GLM 5.2 and is basically neck-and-neck with GLM 5.3. Then there’s the pricing: $6/M input tokens $18/M output tokens $0.30/M cache hits That’s flagship-level performance without the flagship-level price. You can try Hy4 preview for FREE through WorkBuddy over the next two weeks: Tencent Hy @TencentAl_News

🚨 Tencent Hunyuan just dropped Hy4 preview—and it’s already making its way into the open-source big leagues. Three major releases in six months. That’s seriously fast. Hy4 preview comes with: 770B total parameters 49B active parameters A 1M+ token context window And it’s built with one clear focus: productivity. We’re talking coding, office work, science, software engineering, gaming, finance, security, and more. But here’s the part that really caught my attention: Hy4 preview wasn’t developed in isolation. It was co-designed with real products like WorkBuddy. Tencent worked with experts from across its ecosystem to build the training data, then tested the model on real-world tasks. In a blind test involving 163 internal experts and 203 engineering tasks, the scores were: Hy4 preview: 2.99/4 Kimi K3: 2.94/4 GLM 5.3: 2.92/4 And performance isn’t the only impressive part. On benchmarks, Hy4 preview clearly outperforms GLM 5.2 and is basically neck-and-neck with GLM 5.3. Then there’s the pricing: $6/M input tokens $18/M output tokens $0.30/M cache hits That’s flagship-level performance without the flagship-level price. You can try Hy4 preview for FREE through WorkBuddy over the next two weeks: Tencent Hy @TencentAl_News

AGAFE

131,568 views • 23 days ago

Tencent just released Hy4 Preview: a 770B-parameter open-source model with 49B active parameters and a 1M-token context window. The most interesting part is its agentic research capability. Tencent says Hy4 coordinated several Codex sessions in parallel, evaluated their results and adjusted the research direction, outperforming Codex working alone across eight benchmarks. Hy4 is aimed at long-horizon coding, multi-file office work and scientific research. In sum: a solid, good release. A few months ago this would be absolute sota.

Tencent just released Hy4 Preview: a 770B-parameter open-source model with 49B active parameters and a 1M-token context window. The most interesting part is its agentic research capability. Tencent says Hy4 coordinated several Codex sessions in parallel, evaluated their results and adjusted the research direction, outperforming Codex working alone across eight benchmarks. Hy4 is aimed at long-horizon coding, multi-file office work and scientific research. In sum: a solid, good release. A few months ago this would be absolute sota.

Chubby♨️

49,575 views • 24 days ago

Tencent just dropped Hy4 preview, and it's officially open source now. 770B total params, 49B active, 1M+ token context. three major releases in six months, and this one just landed in the top tier of open source models. → Built through Tencent's co-design loop: training data shaped with experts across software engineering, gaming, finance, and security, then built directly alongside products like WorkBuddy → In a blind test with 163 internal experts across 203 engineering tasks, it scored 2.99/4, ahead of GLM 5.3 at 2.92 and Kimi K3 at 2.94 → Benchmarks put it clearly past GLM 5.2 and neck and neck with GLM 5.3 → Pricing stayed low: $0.834/M input, $2.501/M output, $0.042/M cache hits I asked it to write a weekly status update email template for a team lead, then generate a filled-out example with placeholder projects, and export both as Word docs. one prompt, no follow-up, and it handed back both files clean and ready to send. free to try for the next two weeks through WorkBuddy: Tencent Hy Tencent AI WorkBuddy

Tencent just dropped Hy4 preview, and it's officially open source now. 770B total params, 49B active, 1M+ token context. three major releases in six months, and this one just landed in the top tier of open source models. → Built through Tencent's co-design loop: training data shaped with experts across software engineering, gaming, finance, and security, then built directly alongside products like WorkBuddy → In a blind test with 163 internal experts across 203 engineering tasks, it scored 2.99/4, ahead of GLM 5.3 at 2.92 and Kimi K3 at 2.94 → Benchmarks put it clearly past GLM 5.2 and neck and neck with GLM 5.3 → Pricing stayed low: $0.834/M input, $2.501/M output, $0.042/M cache hits I asked it to write a weekly status update email template for a team lead, then generate a filled-out example with placeholder projects, and export both as Word docs. one prompt, no follow-up, and it handed back both files clean and ready to send. free to try for the next two weeks through WorkBuddy: Tencent Hy Tencent AI WorkBuddy

D-Coder

109,289 views • 22 days ago

tencent hunyuan just shipped their third major model in six months. that pace is hard to keep up with. Hy4 preview is now live in workbuddy, and it's fully open source. the specs: 770B total params, 49B active, 1M+ token context window. but the number that matters is where it's pointed — real work. coding, office tasks, research, documents. here's the part i find interesting. tencent didn't build this in isolation. they put the training data together with their own experts across software engineering, gaming, finance and security, then designed the model side by side with products like workbuddy. model and product shaping each other instead of one being bolted onto the other. and it shows up in testing. 163 internal experts ran a blind test across 203 engineering tasks. hy4 preview scored 2.99/4 — ahead of kimi k3 at 2.94 and glm 5.3 at 2.92. on public benchmarks it clearly beats glm 5.2 and sits about level with glm 5.3. then the pricing. $6 per million input tokens, $18 per million output, $0.3 per million on cache hits. flagship results at a price you don't have to think about. and for the next two weeks it's free inside workbuddy.

tencent hunyuan just shipped their third major model in six months. that pace is hard to keep up with. Hy4 preview is now live in workbuddy, and it's fully open source. the specs: 770B total params, 49B active, 1M+ token context window. but the number that matters is where it's pointed — real work. coding, office tasks, research, documents. here's the part i find interesting. tencent didn't build this in isolation. they put the training data together with their own experts across software engineering, gaming, finance and security, then designed the model side by side with products like workbuddy. model and product shaping each other instead of one being bolted onto the other. and it shows up in testing. 163 internal experts ran a blind test across 203 engineering tasks. hy4 preview scored 2.99/4 — ahead of kimi k3 at 2.94 and glm 5.3 at 2.92. on public benchmarks it clearly beats glm 5.2 and sits about level with glm 5.3. then the pricing. $6 per million input tokens, $18 per million output, $0.3 per million on cache hits. flagship results at a price you don't have to think about. and for the next two weeks it's free inside workbuddy.

Fakhr

55,316 views • 24 days ago

I wanted to see what Hy4 preview could actually do when I gave it a real creative project instead of another “write me some code” prompt. So I went with something I’d genuinely want to play: 🐬 a cute dolphin as the hero 🌊 colorful underwater worlds 💎 glowing pearls to collect 🐠 sea creatures and obstacles 🪸 coral reefs, bubbles & hidden treasure 🎮 smoother animations and progressively harder levels The result is this underwater platformer 👇 And this is where Hy4 preview gets interesting to me. Behind the scenes, we’re talking about 770B parameters, 49B active parameters and a 1M+ token context window. It’s also fully open-sourced. But big numbers only matter if they translate into something useful. What I’m really testing is how much actual work the model can take off my plate understanding the idea, handling the implementation, keeping all the moving parts connected, and getting from a blank canvas to something that feels like a real experience. And there’s another update that makes this even more interesting: Hy4 preview was upgraded yesterday to significantly reduce both conversation turns and token consumption. That means less back-and-forth, faster thinking, and a smoother overall experience when working through complex tasks. That’s also why the cost-effectiveness angle stands out to me. If a model can handle more of the workflow while using fewer tokens and requiring fewer iterations, the value isn’t just in the benchmark score. It’s in how much more you can actually build with it. This dolphin has me wanting to test Hy4 preview with some much crazier ideas next. 🐬👀 Tencent Hy Tencent AI ☞

I wanted to see what Hy4 preview could actually do when I gave it a real creative project instead of another “write me some code” prompt. So I went with something I’d genuinely want to play: 🐬 a cute dolphin as the hero 🌊 colorful underwater worlds 💎 glowing pearls to collect 🐠 sea creatures and obstacles 🪸 coral reefs, bubbles & hidden treasure 🎮 smoother animations and progressively harder levels The result is this underwater platformer 👇 And this is where Hy4 preview gets interesting to me. Behind the scenes, we’re talking about 770B parameters, 49B active parameters and a 1M+ token context window. It’s also fully open-sourced. But big numbers only matter if they translate into something useful. What I’m really testing is how much actual work the model can take off my plate understanding the idea, handling the implementation, keeping all the moving parts connected, and getting from a blank canvas to something that feels like a real experience. And there’s another update that makes this even more interesting: Hy4 preview was upgraded yesterday to significantly reduce both conversation turns and token consumption. That means less back-and-forth, faster thinking, and a smoother overall experience when working through complex tasks. That’s also why the cost-effectiveness angle stands out to me. If a model can handle more of the workflow while using fewer tokens and requiring fewer iterations, the value isn’t just in the benchmark score. It’s in how much more you can actually build with it. This dolphin has me wanting to test Hy4 preview with some much crazier ideas next. 🐬👀 Tencent Hy Tencent AI ☞

Md Riyazuddin

63,562 views • 12 days ago

This is dots3-note preview from dots studio in a puzzle game it hasn't seen before. No game-specific prompt. It has to work out the controls, objective and new mechanics from the screen. It starts pressing buttons. Open-weight: 280B total / 16B active, 512K context.

This is dots3-note preview from dots studio in a puzzle game it hasn't seen before. No game-specific prompt. It has to work out the controls, objective and new mechanics from the screen. It starts pressing buttons. Open-weight: 280B total / 16B active, 512K context.

Khushboo Verma

39,872 views • 28 days ago

Hy4 preview just dropped, so I tested it on WorkBuddy against three other models: DeepSeek V4 Flash, GPT-5.6-Luna, and GLM-5.3... the top 4 models on this week’s OpenRouter coding leaderboard. The best part? They’re all available in WorkBuddy, so you don’t need to download multiple Agent apps. For the test, I used one single prompt to build one of those games you’ve definitely seen in ads: swipe/move through number gates, make calculations, and eventually defeat the boss. The results were pretty interesting 👇 For a task of this difficulty, everyone performed well except DeepSeek. It seemed to finish coding without checking for bugs, and the game couldn’t run properly. GPT-5.6-Luna worked, but felt like an arithmetic multiple-choice quiz. A little boring. GLM-5.3 and Hy4 preview were much more impressive. GLM was slower (video sped up 2.2×) and only had three movement positions, while Hy4 offered continuous horizontal movement and a more natural speed. What surprised me most was Hy4’s game design: besides basic arithmetic, it added weapons that reduce enemy damage or increase stolen power, making the game much more fun. Also worth noting: Tencent’s Sherry quantization compresses the 770B MoE model from ~1.5TB BF16 to 214GB, while keeping performance close to the original... making local deployment much more practical. Hy4 preview is free on WorkBuddy until September 10. Give it a try 👇

Hy4 preview just dropped, so I tested it on WorkBuddy against three other models: DeepSeek V4 Flash, GPT-5.6-Luna, and GLM-5.3... the top 4 models on this week’s OpenRouter coding leaderboard. The best part? They’re all available in WorkBuddy, so you don’t need to download multiple Agent apps. For the test, I used one single prompt to build one of those games you’ve definitely seen in ads: swipe/move through number gates, make calculations, and eventually defeat the boss. The results were pretty interesting 👇 For a task of this difficulty, everyone performed well except DeepSeek. It seemed to finish coding without checking for bugs, and the game couldn’t run properly. GPT-5.6-Luna worked, but felt like an arithmetic multiple-choice quiz. A little boring. GLM-5.3 and Hy4 preview were much more impressive. GLM was slower (video sped up 2.2×) and only had three movement positions, while Hy4 offered continuous horizontal movement and a more natural speed. What surprised me most was Hy4’s game design: besides basic arithmetic, it added weapons that reduce enemy damage or increase stolen power, making the game much more fun. Also worth noting: Tencent’s Sherry quantization compresses the 770B MoE model from ~1.5TB BF16 to 214GB, while keeping performance close to the original... making local deployment much more practical. Hy4 preview is free on WorkBuddy until September 10. Give it a try 👇

Orikan

40,312 views • 18 days ago

Laguna S 2.1 is built to run locally. We heard the ask for a serious open model in the 80–120B range. Laguna S 2.1 is our answer, and we want it to redefine what this size class can do. Weights ship in BF16, FP8, INT4 and NVFP4, with official GGUF and MLX conversions, plus official DFlash draft models for faster inference. Day-one support across llama.cpp, ollama, Valerie Lee Make Up, SGLang, NVIDIA TRT-LLM, Hugging Face transformers and ZML. Watch it run on an NVIDIA AI DGX Spark.

Laguna S 2.1 is built to run locally. We heard the ask for a serious open model in the 80–120B range. Laguna S 2.1 is our answer, and we want it to redefine what this size class can do. Weights ship in BF16, FP8, INT4 and NVFP4, with official GGUF and MLX conversions, plus official DFlash draft models for faster inference. Day-one support across llama.cpp, ollama, Valerie Lee Make Up, SGLang, NVIDIA TRT-LLM, Hugging Face transformers and ZML. Watch it run on an NVIDIA AI DGX Spark.

Poolside

31,242 views • 2 months ago

Tencent's Hy3 lands today with real coding and agent ability and a price that undercuts the models you'd put it against. It's a 295B MoE with only 21B active parameters, a big part of why it stays cheap to serve, plus a 256K context window and an Apache 2.0 license that even outperforms GLM-5.1 although GLM is 2.5x larger. On OpenRouter it runs at $0.14 per million input tokens and $0.58 per million output. For a model Tencent benchmarks against much larger flagships, that pricing is insane. Price-performance-ratio is China's real moat. I already wrote about Tencent Hy3 earlier and the significant leap in quality it represents. Therefore, I'm pleased to recommend this model from Tencent Hy. Open Router link down below: #Hy3 #Hunyuan #AICoding

Tencent's Hy3 lands today with real coding and agent ability and a price that undercuts the models you'd put it against. It's a 295B MoE with only 21B active parameters, a big part of why it stays cheap to serve, plus a 256K context window and an Apache 2.0 license that even outperforms GLM-5.1 although GLM is 2.5x larger. On OpenRouter it runs at $0.14 per million input tokens and $0.58 per million output. For a model Tencent benchmarks against much larger flagships, that pricing is insane. Price-performance-ratio is China's real moat. I already wrote about Tencent Hy3 earlier and the significant leap in quality it represents. Therefore, I'm pleased to recommend this model from Tencent Hy. Open Router link down below: #Hy3 #Hunyuan #AICoding

Chubby♨️

23,439 views • 2 months ago

Tried Hy4 preview through WorkBuddy and gave it a pretty vague prompt for a first person roller coaster ride, front row seat, make it feel fast, that was basically it. No detailed spec, no reference video, just a rough idea in a sentence. What came back actually held up. Steep drop that felt like the bottom dropping out, banked turns that leaned the right way, scenery rushing by close enough that you register the speed instead of just watching it happen. For something built off a loose prompt like that, I did not expect it to nail the feel this well. Coding and 3D rendering have clearly gotten a real upgrade here, and this took one pass to look this good. Free to try in WorkBuddy for the next two weeks if you want to mess with it yourself. Tencent Hy Tencent AI

Tried Hy4 preview through WorkBuddy and gave it a pretty vague prompt for a first person roller coaster ride, front row seat, make it feel fast, that was basically it. No detailed spec, no reference video, just a rough idea in a sentence. What came back actually held up. Steep drop that felt like the bottom dropping out, banked turns that leaned the right way, scenery rushing by close enough that you register the speed instead of just watching it happen. For something built off a loose prompt like that, I did not expect it to nail the feel this well. Coding and 3D rendering have clearly gotten a real upgrade here, and this took one pass to look this good. Free to try in WorkBuddy for the next two weeks if you want to mess with it yourself. Tencent Hy Tencent AI

Chubby♨️

27,793 views • 23 days ago

🚨 ALIBABA JUST OPEN-SOURCED THE MOST POWERFUL MODEL IN THE WORLD - Qwen3.5 plus And it changes the AI race forever. This is not another “bigger model” release. This is a complete architectural reset. 397B parameters → only 17B active at inference, Qwen3.5 plus delivers performance on par with GPT-5.2 and Gemini-3-Pro. A new Hybrid Sparse MoE + Linear Attention architecture + Multi-Token Prediction = near-instant long-context responses and massive speed gains. Built as a true native multimodal model, it doesn’t just chat it builds. With a single prompt: “Use the React framework to create a visually impressive Mapbox demo featuring locations in Beijing and Shanghai” it generates a live interactive 3D map UI, structured components, real layouts, and production-ready frontend logic. This is real agentic, multimodal coding not a static code block. MMLU-Pro: 87.8 GPQA: 88.4 IFBench: 76.5 ⚡ up to 8× faster inference And it’s fully open-sourced under Apache-2.0. No locked APIs. No restricted tiers. 201 languages supported bringing frontier AI to developers, startups, and researchers worldwide. The race is no longer about the biggest model. It’s about the most usable intelligence. Open source just entered the top tier.

🚨 ALIBABA JUST OPEN-SOURCED THE MOST POWERFUL MODEL IN THE WORLD - Qwen3.5 plus And it changes the AI race forever. This is not another “bigger model” release. This is a complete architectural reset. 397B parameters → only 17B active at inference, Qwen3.5 plus delivers performance on par with GPT-5.2 and Gemini-3-Pro. A new Hybrid Sparse MoE + Linear Attention architecture + Multi-Token Prediction = near-instant long-context responses and massive speed gains. Built as a true native multimodal model, it doesn’t just chat it builds. With a single prompt: “Use the React framework to create a visually impressive Mapbox demo featuring locations in Beijing and Shanghai” it generates a live interactive 3D map UI, structured components, real layouts, and production-ready frontend logic. This is real agentic, multimodal coding not a static code block. MMLU-Pro: 87.8 GPQA: 88.4 IFBench: 76.5 ⚡ up to 8× faster inference And it’s fully open-sourced under Apache-2.0. No locked APIs. No restricted tiers. 201 languages supported bringing frontier AI to developers, startups, and researchers worldwide. The race is no longer about the biggest model. It’s about the most usable intelligence. Open source just entered the top tier.

RAVI KUMAR SAHU

380,123 views • 7 months ago