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Open weights just caught up to the frontier. GLM-5.2 from Z.ai tops the open-model rankings on Artificial Analysis and Arena.ai's Agent Arena. It's now live on CoreWeave Serverless Inference at $1.39 in and $4.40 out per 1M tokens. Ship more for less.

CoreWeave

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16,637 görüntüleme • 1 ay önce •via X (Twitter)

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This free model just beat every closed-source AI on coding benchmarks. Open weights. 6x cheaper than Opus. The labs don't want you to know it exists. > GLM-5.2 from Zai just topped Code Arena - the first open-weights model to ever hold the #1 coding spot. Not a leaked weight, not a fine-tune. A fully open model beating GPT and Claude on their own turf. It's live on Hugging Face Inference API right now. 5 providers: Novita, Together AI, Fireworks, Deepinfra, Zai. OpenAI-compatible client. → Go to huggingface(.)co → grab HF_TOKEN from account settings → plug into any OpenAI-compatible client 6x cheaper than Opus. Companies bleeding on AI bills are already routing to this for orchestration, caching, and token optimization. The smart money moved before the headline dropped. > Now you know. huggingface(.)co/zai-org/GLM-5.2 Bookmark this before everyone else figures it out.

This free model just beat every closed-source AI on coding benchmarks. Open weights. 6x cheaper than Opus. The labs don't want you to know it exists. > GLM-5.2 from Zai just topped Code Arena - the first open-weights model to ever hold the #1 coding spot. Not a leaked weight, not a fine-tune. A fully open model beating GPT and Claude on their own turf. It's live on Hugging Face Inference API right now. 5 providers: Novita, Together AI, Fireworks, Deepinfra, Zai. OpenAI-compatible client. → Go to huggingface(.)co → grab HF_TOKEN from account settings → plug into any OpenAI-compatible client 6x cheaper than Opus. Companies bleeding on AI bills are already routing to this for orchestration, caching, and token optimization. The smart money moved before the headline dropped. > Now you know. huggingface(.)co/zai-org/GLM-5.2 Bookmark this before everyone else figures it out.

Atenov int.

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Here's an example of a creation by GLM 5.2 on Design Arena, made from only one prompt. Just six months ago, there was a big enough difference between open and closed weight models that we considered having two separate evaluations. Open weights have come a long way. Congrats to the Z.ai team for this achievement!

Here's an example of a creation by GLM 5.2 on Design Arena, made from only one prompt. Just six months ago, there was a big enough difference between open and closed weight models that we considered having two separate evaluations. Open weights have come a long way. Congrats to the Z.ai team for this achievement!

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Jensen announced on stage at #COMPUTEX2026 that we'll be among the first cloud providers to bring up NVIDIA Vera Rubin NVL72, to help accelerate the era of AI factories. As part of NVIDIA's full-stack AI factory platform, Vera Rubin NVL72 is designed to run frontier and open-source models with exceptional performance, efficiency, and scalability—delivering more intelligence per watt and lower token costs at scale. It's GO time. ⚡️

Jensen announced on stage at #COMPUTEX2026 that we'll be among the first cloud providers to bring up NVIDIA Vera Rubin NVL72, to help accelerate the era of AI factories. As part of NVIDIA's full-stack AI factory platform, Vera Rubin NVL72 is designed to run frontier and open-source models with exceptional performance, efficiency, and scalability—delivering more intelligence per watt and lower token costs at scale. It's GO time. ⚡️

CoreWeave

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Our self-improving agents optimized the full @vLLM_project inference stack, with up to 16% more throughput and interactivity for DeepSeek v4 Pro and Z.ai GLM 5.2 on B200s (no MTP). Every change was verified and our agents got better and faster at it with each iteration.

Our self-improving agents optimized the full @vLLM_project inference stack, with up to 16% more throughput and interactivity for DeepSeek v4 Pro and Z.ai GLM 5.2 on B200s (no MTP). Every change was verified and our agents got better and faster at it with each iteration.

Asari AI

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The first-ever measured silicon numbers for @NVIDIA Vera Rubin NVL72 are in 😲 First measured performance shows 10x more tokens per megawatt than Blackwell. No projections. Real results from live hardware.

The first-ever measured silicon numbers for @NVIDIA Vera Rubin NVL72 are in 😲 First measured performance shows 10x more tokens per megawatt than Blackwell. No projections. Real results from live hardware.

CoreWeave

485,191 görüntüleme • 1 ay önce

(Z.ai) is now a model provider inside TRAE. You can BYOK and add GLM-4.6 and GLM-4.5 to TRAE IDE. GLM-4.5 supports up to 128K context and GLM-4.6 expands up to 200K context. Both deliver strong coding and reasoning performance on the benchmark and offers dual “thinking/fast” modes. Available now in the latest version.

(Z.ai) is now a model provider inside TRAE. You can BYOK and add GLM-4.6 and GLM-4.5 to TRAE IDE. GLM-4.5 supports up to 128K context and GLM-4.6 expands up to 200K context. Both deliver strong coding and reasoning performance on the benchmark and offers dual “thinking/fast” modes. Available now in the latest version.

TRAE

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Cerebras Code just got an UPGRADE. It's now powered by GLM 4.6 Pro Plans ($50): 300k ▶️ 1M TPM @ 24M Tokens/day Max Plans ($200): 400k ▶️ 1.5M TPM @ 120M Tokens/day Fastest GLM provider on the planet at 1000 tokens/s and at 131K context. Get yours before we run out 👇

Cerebras Code just got an UPGRADE. It's now powered by GLM 4.6 Pro Plans ($50): 300k ▶️ 1M TPM @ 24M Tokens/day Max Plans ($200): 400k ▶️ 1.5M TPM @ 120M Tokens/day Fastest GLM provider on the planet at 1000 tokens/s and at 131K context. Get yours before we run out 👇

Cerebras

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1/ Introducing Glider - the smallest model to beat GPT-4o-mini on eval tasks ⚡🚀 - Open source, open weights, open code - Explainable evaluations by nature - Trained on 183 criteria and 685 domains Try it out for free at 🔥

1/ Introducing Glider - the smallest model to beat GPT-4o-mini on eval tasks ⚡🚀 - Open source, open weights, open code - Explainable evaluations by nature - Trained on 183 criteria and 685 domains Try it out for free at 🔥

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New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

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HappyHorse 1.0 is now on OpenArt 🐎 It's the #1 ranked text-to-video model on Artificial Analysis right now. Generate up to 15 seconds 1080p video with synchronised audio in a single pass and support for 7 languages.

HappyHorse 1.0 is now on OpenArt 🐎 It's the #1 ranked text-to-video model on Artificial Analysis right now. Generate up to 15 seconds 1080p video with synchronised audio in a single pass and support for 7 languages.

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Registration for #FullyConnected26 is open: 🗓️ September 29 – Oct 1 📍San Francisco 3 days and 3 tracks to accelerate the superintelligence loop. 2,000+ AI Leaders in the room. This is where you need to be.

Registration for #FullyConnected26 is open: 🗓️ September 29 – Oct 1 📍San Francisco 3 days and 3 tracks to accelerate the superintelligence loop. 2,000+ AI Leaders in the room. This is where you need to be.

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This model is insane at design. I asked GLM 5.2 (left) and Opus 4.8 (right) to build me a landing page and you can't even tell the difference. GLM cost $0.06 while opus cost $0.49. More than 6x cheaper while being faster + more token efficient. Another win for open source AI.

This model is insane at design. I asked GLM 5.2 (left) and Opus 4.8 (right) to build me a landing page and you can't even tell the difference. GLM cost $0.06 while opus cost $0.49. More than 6x cheaper while being faster + more token efficient. Another win for open source AI.

Hassan

1,579,926 görüntüleme • 2 ay önce

Big win for open-source LLMs! DeepSeek V4 Pro holds the top open-weights score on SWE-bench Verified, in the GPT-5.5 range. GLM 5.2 leads the open-weight intelligence index and sits near the closed frontier on long-horizon coding. But this leaderboard number is a weak proxy for real performance. It comes from one task set, run through one harness, served at one precision. The same weights can even score differently across providers, since many hosts quantize activations to fp8 and drift the model off its reference weights. Real performance is determined based on whether a model can read a repo, make coordinated edits across files, run the tests, and recover when one breaks. By that measure, the top open models hold up, but only inside the right harness. The teams that actually put DeepSeek V4 into production pipelines as a frontier substitute got there through the harness they built around the model, not by picking a stronger model. If you want to see this in practice, Cline (64k+ stars) has actually built that harness around open models, tuned so they run at production quality. And it's tuned so that these LLMs can run at production quality, with plan and act modes, checkpoints, and terminal feedback. ClinePass is the new access layer on top of it. It runs a curated set of those models inside Cline, narrowed to the ones tested for coding-agent use, with 2 to 5x the standard rate limits and no separate provider accounts, keys, or billing to track. The video below shows the setup, and I worked with the team to put this together. It runs alongside custom keys and local models as well, not in place of them.

Big win for open-source LLMs! DeepSeek V4 Pro holds the top open-weights score on SWE-bench Verified, in the GPT-5.5 range. GLM 5.2 leads the open-weight intelligence index and sits near the closed frontier on long-horizon coding. But this leaderboard number is a weak proxy for real performance. It comes from one task set, run through one harness, served at one precision. The same weights can even score differently across providers, since many hosts quantize activations to fp8 and drift the model off its reference weights. Real performance is determined based on whether a model can read a repo, make coordinated edits across files, run the tests, and recover when one breaks. By that measure, the top open models hold up, but only inside the right harness. The teams that actually put DeepSeek V4 into production pipelines as a frontier substitute got there through the harness they built around the model, not by picking a stronger model. If you want to see this in practice, Cline (64k+ stars) has actually built that harness around open models, tuned so they run at production quality. And it's tuned so that these LLMs can run at production quality, with plan and act modes, checkpoints, and terminal feedback. ClinePass is the new access layer on top of it. It runs a curated set of those models inside Cline, narrowed to the ones tested for coding-agent use, with 2 to 5x the standard rate limits and no separate provider accounts, keys, or billing to track. The video below shows the setup, and I worked with the team to put this together. It runs alongside custom keys and local models as well, not in place of them.

Avi Chawla

44,124 görüntüleme • 1 ay önce

Llama 2: Now on Hugging Chat 🤗🦙 Try out the 70B Chat model for free with super fast inference, web search, and powered by open-source tools! 👉

Llama 2: Now on Hugging Chat 🤗🦙 Try out the 70B Chat model for free with super fast inference, web search, and powered by open-source tools! 👉

Hugging Face

403,673 görüntüleme • 3 yıl önce

I finally got an open model to do structural biology by itself 🔥 GLM-5.2 drives the Mol* viewer, judges its own render through Qwen3-VL, and refines until the drug pops in its pocket. Then I spun it in 3D. All open, on Hugging Face. What should it build next?

I finally got an open model to do structural biology by itself 🔥 GLM-5.2 drives the Mol* viewer, judges its own render through Qwen3-VL, and refines until the drug pops in its pocket. Then I spun it in 3D. All open, on Hugging Face. What should it build next?

Maziyar PANAHI

61,651 görüntüleme • 1 ay önce

> be Kimi Founder > Zhilin Yang. nobody in the West knows your name. > build K1. K2. K2.5. K2.6. K2.7. > each one sets the open-source record > everyone says China can't reach the frontier > raise $2B. hit $20B valuation. > keep your head down. keep shipping. > July 16. drop K3. > 2.8 trillion parameters. > the biggest open model ever released. > 1 million token context. native multimodal. > Kimi Delta Attention — 6.3x faster decoding. > Artificial Analysis ranks it #4 overall. > right behind GPT-5.6 Sol and Fable 5. > open weights July 27. free for anyone. > the closed labs charge for the frontier. > you gave it away. > different game.

> be Kimi Founder > Zhilin Yang. nobody in the West knows your name. > build K1. K2. K2.5. K2.6. K2.7. > each one sets the open-source record > everyone says China can't reach the frontier > raise $2B. hit $20B valuation. > keep your head down. keep shipping. > July 16. drop K3. > 2.8 trillion parameters. > the biggest open model ever released. > 1 million token context. native multimodal. > Kimi Delta Attention — 6.3x faster decoding. > Artificial Analysis ranks it #4 overall. > right behind GPT-5.6 Sol and Fable 5. > open weights July 27. free for anyone. > the closed labs charge for the frontier. > you gave it away. > different game.

Kirill

412,939 görüntüleme • 1 ay önce

🍌 nano banana has been the most viral model we've ever seen... over 1M+ votes per day and 2M chats daily at its peak! nano banana by Google DeepMind is finally here. frontier models land first on Arena. try it for free today

🍌 nano banana has been the most viral model we've ever seen... over 1M+ votes per day and 2M chats daily at its peak! nano banana by Google DeepMind is finally here. frontier models land first on Arena. try it for free today

Aryan Vichare

15,357 görüntüleme • 1 yıl önce

We are thrilled to be a launch partner for Meta Llama 3. Experience Llama 3 now at up to 350 tokens per second for Llama 3 8B and up to 150 tokens per second for Llama 3 70B, running in full FP16 precision on the Together API! 🤯

We are thrilled to be a launch partner for Meta Llama 3. Experience Llama 3 now at up to 350 tokens per second for Llama 3 8B and up to 150 tokens per second for Llama 3 70B, running in full FP16 precision on the Together API! 🤯

Together AI

88,229 görüntüleme • 2 yıl önce

It's over. OpenAI just crushed it. We have their o3-level open-source model running on Groq Inc at 500 tokens per second. Watch it build an entire SaaS app in just a few seconds. This is the new standard. Why the hell would you use anything else??

It's over. OpenAI just crushed it. We have their o3-level open-source model running on Groq Inc at 500 tokens per second. Watch it build an entire SaaS app in just a few seconds. This is the new standard. Why the hell would you use anything else??

Matt Shumer

498,675 görüntüleme • 1 yıl önce

MiniMax H3 is now available on GMI Cloud the #1 open model in Video Arena: across both Text-to-Video and Image-to-Video (video made by MiniMax (official) H3)

MiniMax H3 is now available on GMI Cloud the #1 open model in Video Arena: across both Text-to-Video and Image-to-Video (video made by MiniMax (official) H3)

GMI Cloud

16,935 görüntüleme • 17 gün önce