Qubrid AI

Open, Inference-first AI Infrastructure Platform for Enterprise Developers. Get help:

[Qubrid AI @Qubrid_AI]

As a Day 0 partner with https://t.co/ZsXfo381Nr, we have GLM-5.2 live today - no waitlist, full API access, one line to integrate.

Full technical deep dive (every benchmark + API guide):
https://t.co/0X6yQ9zxYg

Get started on Qubrid AI:
https://t.co/tDQSSyqWeS

[Qubrid AI @Qubrid_AI]

@Qubrid_AI is a Day 0 launch partner with @Zai_org for GLM-5.2 - the new #1 open-weights model in the world.

It beats GPT-5.5 on coding, matches Claude Opus 4.8 on long-horizon tasks, costs 6x less, and it's MIT licensed.

It's live on Qubrid AI right now. Here's the breakdown 🧵

[Qubrid AI @Qubrid_AI]

Input: $1.10 / 1M tokens
Cached: $0.275 / 1M tokens
Output: $3.851 / 1M tokens

Frontier-level agentic coding. Open weights. MIT license. Self-hostable.

This is what the open-source AI stack looks like in 2026.

[Qubrid AI @Qubrid_AI]

We benchmarked K2.7 Code against 7 other open-source coding models on Qubrid - DeepSeek V4-Pro, V4-Flash, Qwen3-Coder-480B, Qwen3-Coder-Next, Qwen3.7, MiniMax M3.

Full breakdown of who wins where, real pricing, and tradeoffs:

https://t.co/hM4V2rciS0

[Qubrid AI @Qubrid_AI]

@Kimi_Moonshot K2.7 Code is now live on Qubrid AI.

An open-source model just beat GPT-5.5 and Claude Opus 4.8 on MCP tool-use accuracy.

81.1 vs 74.3 vs 76.4.

Let that sink in. 🧵

[Qubrid AI @Qubrid_AI]

The architecture:

- 1T params, 32B active (MoE)
- 262K context
- Text + image + video input
- 30% fewer reasoning tokens vs K2.6

That last one matters. Reasoning tokens bill as output tokens. On 300-step agent loops, 30% compounds into real money.

[Qubrid AI @Qubrid_AI]

🚀 @Alibaba_Qwen 3.7 Plus is now live on @Qubrid_AI.

Build with @alibaba_cloud's latest multimodal model using a single API, unified billing, and instant access through the Qubrid platform.

🎁 Launch Offer: Get 20% OFF to get started.

Try it here: https://t.co/Jj17XSkjvW

📖 Technical blog: https://t.co/pSvFdzXQ2Y

#Qwen #Qwen37Plus #AI #LLM #AIAgents #MultimodalAI #QubridAI #GenerativeAI #Developers

[Qubrid AI @Qubrid_AI]

@MiniMax_AI M3 is now live on Qubrid AI.

https://t.co/1MaURyoDb7

- 1M-token context.
- Native multimodality.
- Frontier coding performance.
- Long-horizon agentic workflows.

One of the most technically interesting open-weight model releases of the year.

We're proud to be a Day 0 launch partner and are offering 50% off for early adopters.

More on the launch, benchmarks, architecture, API access, pricing, and partnership:

https://t.co/roSFWgovkK

[Qubrid AI @Qubrid_AI]

@DealsForge Looking forward to the results 👀

If you need additional capacity for those agentic runs, Qwen 3.7 Max is also available on Qubrid:

https://t.co/pLW6aDOgcx

[Qubrid AI @Qubrid_AI]

@EartherAI Love seeing A100s being used the way they should be - more experiments, faster iteration, better outcomes.

Keep us posted on the results. And if you ever need more A100 capacity, you know where to find us 👀

https://t.co/cNYyCSpSLv

[Qubrid AI @Qubrid_AI]

@sunwukong559 @solofounders @julianweisser @HThieblemont @vipulved @ChaseLochmiller No founder should have to spend their time scavenging for compute.

DM us. We can help with A100 capacity and get you shipping instead of hunting for GPUs.

https://t.co/cNYyCSpSLv

[Qubrid AI @Qubrid_AI]

@OnKaii21 @Michaelvll1 @karpathy Happy to help.

If you're looking for up to 8 A100 GPUs for a 10-day research run, DM us. For academic workloads, we can offer A100 80GB pricing as low as $1/hr and help you find the most cost-effective setup.

https://t.co/cNYyCSpSLv

[Qubrid AI @Qubrid_AI]

@1xfalso 350 hours sounds expensive until you find the right GPU provider 👀

If you're looking for A100 capacity, Qubrid has you covered.

https://t.co/yMgvK7VetK

[Qubrid AI @Qubrid_AI]

@tobiaswup @runpod @vast_ai Looks like you're missing Qubrid 😉

A100 80GB pricing can go as low as ~$1/hr for qualifying workloads, with H100, H200, B200 & B300 GPUs available on demand.

https://t.co/TuWmoc8e7u

[Qubrid AI @Qubrid_AI]

@MikeWShell We're seeing the same trend.

A100s continue to be one of the best value GPUs for training, fine-tuning and agentic workloads. Interestingly, many teams on Qubrid are still accessing A100 80GB instances at around $1/hr for qualifying workloads.

https://t.co/cNYyCSpSLv

[Qubrid AI @Qubrid_AI]

@cheapcuda AI compute shouldn't cost more than your chai budget ☕

A100 80GB access on Qubrid can be as low as $1/hr for qualifying workloads. H100s, H200s, B200s & B300s available too.

https://t.co/TuWmoc8e7u

[Qubrid AI @Qubrid_AI]

@AIex_3 @huggingface The future of AI infra is simple: ask for compute, get compute.

No one should need to spend half a day setting up cloud infrastructure just to fine-tune a model. A100 80GB access can be as low as ~$1/hr on Qubrid for qualifying workloads.

https://t.co/cNYyCSpSLv

[Qubrid AI @Qubrid_AI]

@GPUaaS Good to see additional H100 and H200 capacity entering the market.

We've seen the same trend at Qubrid - teams increasingly want flexibility, fast provisioning, and transparent pricing as they scale AI workloads.

https://t.co/TuWmoc8e7u