Tensorpath @Tensorpath

New Open Source Model Available on TensorPath! GLM 5.2 is live on the TensorPath AI Factory ⚡ The MOST POWERFUL open source model by far, scoring 51 on Artificial Analysis Intelligence Index just slightly behind Opus 4.8, GPT 5.5 and Fable 5.

Open Source Model Alert! Kimi K2.7 Code is live on the TensorPath AI Factory ⚡ 1T parameters model with less overthinking, with 30% lower reasoning-token usage compared to K2.6

Open Source Model Alert! Xiaomi MiMo V2.5 is live on the TensorPath AI Factory ⚡ Size: 310B Total / 15B Active Context Length: 1M Artificial Analysis Intelligence Index: 49 tie with GPT 5.5 mini x-high and Grok 4.2 Contact us for dedicated deployment on Tensorpath for unlimited token usage!

The BEST Open Source Model Alert! Xiaomi MiMo V2.5 Pro is live on the TensorPath AI Factory ⚡ Size: 1T total / 52B activated Context Length: 1M Artificial Analysis Intelligence Index: 54 (tie with GPT 5.3 Codex, Kimi K2.6) SWE Bench Pro: 57.2 (Close to Claude Opus 4.6) Contact us for dedicated deployment on Tensorpath for unlimited token usage!

Open Source Model Alert! DeepSeek V4 Pro is live on the TensorPath AI Factory ⚡ 1.6T total parameters and 49B activated parameters MoE model with 1M context window, rivalling GPT-5.4 and Claude Opus 4.6 at fraction of the cost, contact us for dedicated deployment on Tensorpath!

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Open Source Model Alert! DeepSeek V4 Flash is live on the TensorPath AI Factory ⚡ 284B total parameters and 13B activated parameters MoE model with 1M context window, rivalling GPT-5.4 mini and Gemini 3 Flash & Pro, contact us for dedicated deployment on Tensorpath!

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Open Source Model Alert! Qwen 3.6 27B is live on the TensorPath AI Factory ⚡ 27B parameters dense model with 53.5% on SWE-Bench Pro and 59.3% Terminal-Bench 2.0, rivalling Claude Opus 4.5 that can fit inside 1 x A100 80G, starting at $1.485 / hour on Tensorpath!

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Open Source Model Alert! Kimi K2.6 is live on the TensorPath AI Factory ⚡ 1T parameters model with 58.6% on SWE-Bench Pro and 66.7% Terminal-Bench 2.0, surpassing both GPT 5.4 and Claude Opus 4.6.

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1-Click Deploy at tensorpath.io

Qwen 3.6 35B-A3B is live on TensorPath AI Factory. SWE-Bench Verified: 73.4 | Terminal-Bench 2.0: 51.5 | Apache 2.0. One-click deploy on Tensorpath.

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Try Bonsai 8B in TensorChat today 👉 apps.apple.com/ca/app/tensorc…

🌳 Bonsai 8B just landed in TensorChat! The world's first true 1-bit LLM from @PrismML Rivals full-precision models at 1/14th the size. Run it privately on your phone. No cloud, no data leaving your device. Fully open source.

TensorChat now supports NVIDIA Nemotron 3 Nano 4B! Lightning-fast on-device inference, compact footprint. Run one of NVIDIA's most efficient open models directly in TensorChat today. Download at apps.apple.com/ca/app/tensorc…

GLM-5.1 is now available to self-host @tensorpath #1 open-source on SWE-Bench Pro. Runs autonomously for 8 hours on long-horizon coding tasks. MIT license. Your infra, your data.

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Gemma 4 E2B is live on TensorChat! Run AI models, RAG, translation models locally on your device. No internet needed, 100% private. Download at apps.apple.com/ca/app/tensorc…, no signup required.

Gemma 4 31B and 26B-A4B are live on Tensorpath! Run on NVIDIA A6000 $0.55/hr on demand, No waitlist, ZDR. Apache 2.0 model, #3 open model in the world, running on your own dedicated GPU.

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The model landscape is shifting fast. A year ago there was no open-source option within 10% of the frontier. Now there is, and it’s practically free to use. That changes how you should think about inference budgets, vendor lock-in, and build vs buy. Follow us for breakdowns on what actually works in AI inference. 6/6

The debate we keep having internally: is 94.6% of the best model at 1/5th to 1/8th the price actually the better engineering decision for most production workloads? Not for research. Not for pushing the frontier. But for shipping products to users — does the marginal quality difference justify 5-8x the cost? Genuine question. We don’t think there’s one right answer. 5/6

GLM-5.1 scores 94.6% of Claude Opus 4.6 on coding benchmarks. Opus charges $5/M input tokens and $25/M output. GLM-5.1 charges $1 and $3.20. That’s 5x cheaper on input. Nearly 8x cheaper on output. And it’s open source under MIT. So why isn’t everyone switching? 1/6