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What can a 770B model actually build? Hy4 preview is making that question more practical. 770B total / 49B active Native 1M context Apache 2.0 Day-0 vLLM + SGLang Official FP8 ~1.5TB → ~214GiB with mixed per-layer quant The interesting part: reported accuracy barely moves vs BF16. Here’s Hy4... show more

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12,224 views • 16 days ago •via X (Twitter)

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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 controls, enemies, visual particle effects, plus score tracking. It is a preview. Test it, measure tokens/s, compare it with BF16 or FP8, and see what it builds. Try it in WorkBuddy: Tencent Hy WorkBuddy #Hy4Preview #OpenSourceAI #GameDev #AI

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 controls, enemies, visual particle effects, plus score tracking. It is a preview. Test it, measure tokens/s, compare it with BF16 or FP8, and see what it builds. Try it in WorkBuddy: Tencent Hy WorkBuddy #Hy4Preview #OpenSourceAI #GameDev #AI

Tyler Wayne

77,986 views • 12 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

I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI

I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI

Toha Khan

66,214 views • 17 days ago

Codex can now run Hy4 preview! Tencent dropped it today. 770b total but only 49b active per token, 1m context, apache 2.0. A frontier model you can actually download. Their own blind eval put it ahead of GLM 5.3 and Kimi k3 on engineering work, 163 experts scoring 203 tasks. 65.7 on swe bench pro. Hy4 now sits in the picker next to sol 5.6. OpenCode and Command code already serves it on the $10 plan, so no api key, no per token metering, and my native models never moved. Here is how you set it up:

Codex can now run Hy4 preview! Tencent dropped it today. 770b total but only 49b active per token, 1m context, apache 2.0. A frontier model you can actually download. Their own blind eval put it ahead of GLM 5.3 and Kimi k3 on engineering work, 163 experts scoring 203 tasks. 65.7 on swe bench pro. Hy4 now sits in the picker next to sol 5.6. OpenCode and Command code already serves it on the $10 plan, so no api key, no per token metering, and my native models never moved. Here is how you set it up:

Ziwen

18,451 views • 24 days ago

🚨 Tencent just dropped a serious open-source AI contender. Meet Hy4 Preview built for productivity, coding & agentic workflows. 770B total parameters. 49B active. 1M+ token context. But the numbers aren’t the most interesting part. Hy4 Preview is designed to handle what actually matters: planning, tool use, coding, research, document workflows and complex multi-step tasks. In Tencent’s internal blind evaluation across 203 engineering tasks, it scored 2.99/4, edging out GLM 5.3 (2.92) and Kimi K3 (2.94). From full-stack development and research to workplace automation and even game development, this looks less like “just another LLM” and more like a model built to get real work done. Worth watching 👀 🔗 Try it with WorkBuddy: Tencent Hy Tencent AI WorkBuddy

🚨 Tencent just dropped a serious open-source AI contender. Meet Hy4 Preview built for productivity, coding & agentic workflows. 770B total parameters. 49B active. 1M+ token context. But the numbers aren’t the most interesting part. Hy4 Preview is designed to handle what actually matters: planning, tool use, coding, research, document workflows and complex multi-step tasks. In Tencent’s internal blind evaluation across 203 engineering tasks, it scored 2.99/4, edging out GLM 5.3 (2.92) and Kimi K3 (2.94). From full-stack development and research to workplace automation and even game development, this looks less like “just another LLM” and more like a model built to get real work done. Worth watching 👀 🔗 Try it with WorkBuddy: Tencent Hy Tencent AI WorkBuddy

RAVI KUMAR SAHU

63,568 views • 23 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

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

Tyler Wayne

56,152 views • 11 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

I tested Tencent’s Hy4 preview model in WorkBuddy on a real frontend build, not a benchmark screenshot. I gave it one practical brief: create an original neon courier game in a single HTML file, with Canvas rendering, keyboard controls, collision detection, scoring, a countdown timer, a boost mechanic, sound effects, and reliable restart logic. The point was not to see whether it could describe a game. I wanted to see whether it could turn a creative idea into a coherent, playable result while handling the details that often break a quick prototype: state changes, movement, collisions, feedback, layout, and interaction flow. Tencent’s Hy4 preview is designed for stronger code understanding, generation, and editing, together with document and information processing, workflow automation, web and game development, cross-tool collaboration, and complex multi-step task completion. That makes it relevant not only for answering questions, but also for taking a task from brief to working output. The current preview version is faster and more effective than the first release. After the latest upgrade, the workflow takes fewer conversation rounds, uses fewer tokens, and reaches useful results more quickly. The difference is most noticeable in the handoff between idea, implementation, revision, and final testing: there is less waiting and less back-and-forth before the result becomes usable. For context, the reference materials describe Tencent’s Hy4 preview as a 770B-parameter model with 49B active parameters, a 1M-token context window, and Apache 2.0 weights. The practical question is how those capabilities translate into real work. In this case, I’m testing whether the model can produce a finished, playable game rather than just a promising code fragment. The one-minute video shows the complete process: selecting Tencent’s Hy4 preview model in WorkBuddy, submitting the build prompt, reviewing the generated result, playing the game, and checking the controls, collisions, score, timer, boost effect, sound, and restart flow. Try your own coding, frontend, document, or automation task in WorkBuddy: Tencent Hy Workbuddy Tencent AI

I tested Tencent’s Hy4 preview model in WorkBuddy on a real frontend build, not a benchmark screenshot. I gave it one practical brief: create an original neon courier game in a single HTML file, with Canvas rendering, keyboard controls, collision detection, scoring, a countdown timer, a boost mechanic, sound effects, and reliable restart logic. The point was not to see whether it could describe a game. I wanted to see whether it could turn a creative idea into a coherent, playable result while handling the details that often break a quick prototype: state changes, movement, collisions, feedback, layout, and interaction flow. Tencent’s Hy4 preview is designed for stronger code understanding, generation, and editing, together with document and information processing, workflow automation, web and game development, cross-tool collaboration, and complex multi-step task completion. That makes it relevant not only for answering questions, but also for taking a task from brief to working output. The current preview version is faster and more effective than the first release. After the latest upgrade, the workflow takes fewer conversation rounds, uses fewer tokens, and reaches useful results more quickly. The difference is most noticeable in the handoff between idea, implementation, revision, and final testing: there is less waiting and less back-and-forth before the result becomes usable. For context, the reference materials describe Tencent’s Hy4 preview as a 770B-parameter model with 49B active parameters, a 1M-token context window, and Apache 2.0 weights. The practical question is how those capabilities translate into real work. In this case, I’m testing whether the model can produce a finished, playable game rather than just a promising code fragment. The one-minute video shows the complete process: selecting Tencent’s Hy4 preview model in WorkBuddy, submitting the build prompt, reviewing the generated result, playing the game, and checking the controls, collisions, score, timer, boost effect, sound, and restart flow. Try your own coding, frontend, document, or automation task in WorkBuddy: Tencent Hy Workbuddy Tencent AI

Rebecca Adson

61,820 views • 4 days ago

🚨 I just built a game with an open-source AI model. And honestly… I didn’t expect it to be this capable. Tencent Hunyuan just released Hy4 preview, and it’s already pushing into the top tier of open-source models. Three major releases in six months. That pace is crazy. Here’s what Hy4 preview brings: → 770B total parameters → 49B active parameters → 1M+ token context window → Fully open-source But the numbers aren’t even the most interesting part. Hy4 preview was built around one goal: real-world productivity. Coding. Engineering. Office work. Science. Gaming. Finance. Security. And Tencent didn’t build it in isolation. Hy4 preview was co-designed alongside real products like WorkBuddy, using expertise and real-world data from across Tencent’s ecosystem. So I decided to test it the way I actually like testing AI models: I gave it a game idea and let WorkBuddy help turn it into a playable experience. 🎮 From the initial concept to the actual game logic, it was surprisingly smooth. And the benchmark results back up the hype: 163 internal experts 203 engineering tasks Hy4 preview — 2.99/4 Kimi K3 — 2.94/4 GLM 5.3 — 2.92/4 It also beats GLM 5.2 on benchmarks and comes remarkably close to GLM 5.3. Then comes the part I really like: 💰 ¥6/M input tokens 💰 ¥18/M output tokens 💰 ¥0.30/M cache hits Flagship-level capability without the flagship-level price. And right now, you can try Hy4 preview FREE through WorkBuddy for the next two weeks. If you’re curious what it can actually do, don’t just read the benchmarks. Build something with it. 🔗 Tencent Hy Tencent AI

🚨 I just built a game with an open-source AI model. And honestly… I didn’t expect it to be this capable. Tencent Hunyuan just released Hy4 preview, and it’s already pushing into the top tier of open-source models. Three major releases in six months. That pace is crazy. Here’s what Hy4 preview brings: → 770B total parameters → 49B active parameters → 1M+ token context window → Fully open-source But the numbers aren’t even the most interesting part. Hy4 preview was built around one goal: real-world productivity. Coding. Engineering. Office work. Science. Gaming. Finance. Security. And Tencent didn’t build it in isolation. Hy4 preview was co-designed alongside real products like WorkBuddy, using expertise and real-world data from across Tencent’s ecosystem. So I decided to test it the way I actually like testing AI models: I gave it a game idea and let WorkBuddy help turn it into a playable experience. 🎮 From the initial concept to the actual game logic, it was surprisingly smooth. And the benchmark results back up the hype: 163 internal experts 203 engineering tasks Hy4 preview — 2.99/4 Kimi K3 — 2.94/4 GLM 5.3 — 2.92/4 It also beats GLM 5.2 on benchmarks and comes remarkably close to GLM 5.3. Then comes the part I really like: 💰 ¥6/M input tokens 💰 ¥18/M output tokens 💰 ¥0.30/M cache hits Flagship-level capability without the flagship-level price. And right now, you can try Hy4 preview FREE through WorkBuddy for the next two weeks. If you’re curious what it can actually do, don’t just read the benchmarks. Build something with it. 🔗 Tencent Hy Tencent AI

Aryan Rakib

63,296 views • 11 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

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

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 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

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

🎉 Congrats to MiniMax (official) on releasing MiniMax M3! Frontier coding and agentic capabilities, native image and video input, computer use, and a 1M-token context window, all in a single open model. At the heart of M3 is MSA, a new sparse attention architecture: instead of attending densely over the full KV cache, each query scores 128-token KV blocks and runs attention only over the top blocks. That is what makes 1M-token context practical to serve. M3 runs in vLLM with day-0 support, verified on NVIDIA and AMD hardware: ✨ MSA sparse attention with dedicated prefill and decode kernels ✨ 1M-token context serving with prefix caching and chunked prefill ✨ BF16 and MXFP8 checkpoints, with MoE backends for both Hopper and Blackwell ✨ Native multimodal input (image + video) ✨ Tool calling, reasoning parsing, and thinking-mode control for agent workloads Day-0 support like this is a true team effort. Grateful to the teams at MiniMax (official), NVIDIA AI, AI at AMD, and Inferact, and to the vLLM community for making it happen. 🙏 Deep dive into the implementation, kernel work, and deployment recipes: 🔗

🎉 Congrats to MiniMax (official) on releasing MiniMax M3! Frontier coding and agentic capabilities, native image and video input, computer use, and a 1M-token context window, all in a single open model. At the heart of M3 is MSA, a new sparse attention architecture: instead of attending densely over the full KV cache, each query scores 128-token KV blocks and runs attention only over the top blocks. That is what makes 1M-token context practical to serve. M3 runs in vLLM with day-0 support, verified on NVIDIA and AMD hardware: ✨ MSA sparse attention with dedicated prefill and decode kernels ✨ 1M-token context serving with prefix caching and chunked prefill ✨ BF16 and MXFP8 checkpoints, with MoE backends for both Hopper and Blackwell ✨ Native multimodal input (image + video) ✨ Tool calling, reasoning parsing, and thinking-mode control for agent workloads Day-0 support like this is a true team effort. Grateful to the teams at MiniMax (official), NVIDIA AI, AI at AMD, and Inferact, and to the vLLM community for making it happen. 🙏 Deep dive into the implementation, kernel work, and deployment recipes: 🔗

vLLM

40,306 views • 3 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

Qwen3.8-27B running at full BF16 on a free Kaggle TPU is kind of ridiculous. No quantization. No tiny context window. No expensive GPU instance. Just Qwen3.8-27B running on a Kaggle TPU v5e-8. The reported numbers: ~130 tok/s decode ~10,000 tok/s prefill 262K context That prefill number is especially wild. You can throw a huge amount of code or context at the model and ingest it extremely quickly, while still getting around 130 tokens per second during generation. And because it’s running in full BF16, you’re not relying on an aggressive quant just to make the model fit. But the really interesting part isn’t even the raw throughput. You can expose it as an OpenAI-compatible endpoint. That means you can plug the model into tools that already understand OpenAI-style APIs. Claude Code. Codex. OpenCode. And other compatible clients. So the workflow becomes pretty simple: Spin up the Qwen3.8-27B endpoint on Kaggle. Point your coding tool at the API. And suddenly you have a 27B coding model sitting behind the same interface you’d normally use for hosted models. The 262K context is also a huge deal for agentic coding. Large repositories can fit into a single context. Long conversations don’t need to be constantly trimmed. And tools can feed much more information back to the model without hitting a tiny context ceiling. The fact that this can be built around a free TPU environment is what makes this especially interesting. We’re getting to a point where experimenting with serious open models doesn’t always require owning a $2,000 GPU or paying for a large cloud instance. Free compute + open weights + an OpenAI-compatible API + existing coding agents. That’s a pretty powerful combination. Qwen3.8-27B is already an interesting model. Running the full BF16 version at ~130 tok/s with 262K context on free Kaggle TPU compute makes it a lot more interesting.

Qwen3.8-27B running at full BF16 on a free Kaggle TPU is kind of ridiculous. No quantization. No tiny context window. No expensive GPU instance. Just Qwen3.8-27B running on a Kaggle TPU v5e-8. The reported numbers: ~130 tok/s decode ~10,000 tok/s prefill 262K context That prefill number is especially wild. You can throw a huge amount of code or context at the model and ingest it extremely quickly, while still getting around 130 tokens per second during generation. And because it’s running in full BF16, you’re not relying on an aggressive quant just to make the model fit. But the really interesting part isn’t even the raw throughput. You can expose it as an OpenAI-compatible endpoint. That means you can plug the model into tools that already understand OpenAI-style APIs. Claude Code. Codex. OpenCode. And other compatible clients. So the workflow becomes pretty simple: Spin up the Qwen3.8-27B endpoint on Kaggle. Point your coding tool at the API. And suddenly you have a 27B coding model sitting behind the same interface you’d normally use for hosted models. The 262K context is also a huge deal for agentic coding. Large repositories can fit into a single context. Long conversations don’t need to be constantly trimmed. And tools can feed much more information back to the model without hitting a tiny context ceiling. The fact that this can be built around a free TPU environment is what makes this especially interesting. We’re getting to a point where experimenting with serious open models doesn’t always require owning a $2,000 GPU or paying for a large cloud instance. Free compute + open weights + an OpenAI-compatible API + existing coding agents. That’s a pretty powerful combination. Qwen3.8-27B is already an interesting model. Running the full BF16 version at ~130 tok/s with 262K context on free Kaggle TPU compute makes it a lot more interesting.

FHILY👑

35,794 views • 15 days ago

MiniMax (official) H3 is live in SGLang Diffusion, with day-0 serving support 🎬 This open model matches Seedance 2.0 at 1/3 the cost, or $0 if you run it locally on 2x 5090 or 1 RTX 6000. With SGLang Diffusion, you can build visual concepts, motion design, e-commerce creatives, precise video edits, animation and stylized visuals, all locally on your own machine. No API bill, no waitlist. H3 takes text, image, video and audio in a single context, and generates 5-15s clips at native 2K, 24fps with native stereo audio. SGLang Diffusion also runs on NVIDIA AI Blackwell and Hopper, and AMD MI355X / MI300X. Let's create something with MiniMax H3 👇

MiniMax (official) H3 is live in SGLang Diffusion, with day-0 serving support 🎬 This open model matches Seedance 2.0 at 1/3 the cost, or $0 if you run it locally on 2x 5090 or 1 RTX 6000. With SGLang Diffusion, you can build visual concepts, motion design, e-commerce creatives, precise video edits, animation and stylized visuals, all locally on your own machine. No API bill, no waitlist. H3 takes text, image, video and audio in a single context, and generates 5-15s clips at native 2K, 24fps with native stereo audio. SGLang Diffusion also runs on NVIDIA AI Blackwell and Hopper, and AMD MI355X / MI300X. Let's create something with MiniMax H3 👇

LMSYS Org

100,189 views • 1 month ago

MiniMax H3 is native in ComfyUI, day zero with the weights. Model highlights: → Text-to-video, prompt only → Image-to-video → First-and-last-frame - control the opening frame, the closing frame, or both → Reference-to-video - carry a subject, a motion, or a voice through the clip → In-place editing - modify a shot you already have instead of regenerating it The capability MiniMax (official) leads with is multimodal context understanding, and it's what collapses those five tasks into one model. H3 takes images, audio, and video together and resolves them against a prompt that explains how they relate. Download the workflows & learn more in our blog 👇

MiniMax H3 is native in ComfyUI, day zero with the weights. Model highlights: → Text-to-video, prompt only → Image-to-video → First-and-last-frame - control the opening frame, the closing frame, or both → Reference-to-video - carry a subject, a motion, or a voice through the clip → In-place editing - modify a shot you already have instead of regenerating it The capability MiniMax (official) leads with is multimodal context understanding, and it's what collapses those five tasks into one model. H3 takes images, audio, and video together and resolves them against a prompt that explains how they relate. Download the workflows & learn more in our blog 👇

ComfyUI

234,529 views • 1 month ago

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

vLLM

104,039 views • 1 month ago