One API to access the best AI models in the world. Faster agents, lower costs.

Joined January 2026
Commonstack retweeted
used @commonstack_ai and built lumenforge a bio lumen experimental architecture studio for fun. created with @Kimi_Moonshot latest K3 which has been a absolute stunner when it comes to assisting in design. hybrid seed > activation > articulation > symbiosis > metropolis
Kimi K3, the latest frontier by Moonshot AI is now available on @commonstack_ai! It is packed with 2.8T parameters with KDA enabling up to 6.3x faster in decoding. Built for self evolving and long horizon agentic tasks. Give the moon a shot 🌕
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Commonstack retweeted
A lot of routing work evaluates isolated prompts, but real agent systems are fundamentally multi-step and budget-constrained. Cool to see benchmarks moving toward execution-grounded, end-to-end evaluation instead of just token-level proxies. TwinRouterBench is a strong step toward realistic agentic routing evaluation — especially the separation between static supervision and dynamic SWE-bench execution. Excited to see where this goes!
Excited to share that TwinRouterBench has been accepted to the #RLEval Workshop at #CAIS2026 🎉 As LLM apps become long-horizon agents, one request can trigger many model calls across planning, tool use, retrieval, coding, and verification. That makes per-step LLM routing a core infrastructure problem: sending each call to the cheapest sufficient model without breaking downstream success. TwinRouterBench introduces: ⚡ Static track: 970 router-visible prefixes from 520 instances across SWE-bench, BFCL, mtRAG, QMSum, and PinchBench 🚀 Dynamic track: live SWE-bench Verified evaluation with official task resolution + realized API spend Key result: a router trained on static labels achieves comparable SWE-bench resolve rate while cutting API cost by ~53% vs. an unrouted Opus 4.6 baseline. Paper: arxiv.org/html/2605.18859v1 Code: github.com/CommonstackAI/Twi… Dataset: huggingface.co/datasets/Amor… Website: commonstackai.github.io/Twin… #LLM #AgenticAI #LLMRouting #Benchmark #SWEBench
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DevNTell - Intelligence for Anyone, Anywhere: A Look Into @gradient_HQ's Mission to Decentralize AGI | Guest: @alex_mirran | Host: @narb_s xitter.cf/i/broadcasts/1RKZzjVQb…
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Commonstack retweeted
Great vibes at Nanyang Technological University today - thanks for having us as part of Exam Welfare Week. Loved chatting with students and excited to see what they build with @commonstack_ai If you were there - thanks for stopping by 🙌 If not, we’re running it back tomorrow with more free credits 👀 Come through.
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Commonstack retweeted
been getting a lot of feedback since the last post so here is a big update for uncommonroute lovers: ↑ significant improvements in savings and accuracy ↑ fixed claude code misclassifying simple tasks as tool-heavy ↑ tightened feedback sensitivity for averaged perceptron tier adjustments ↑ one-command uninstall ↑ dashboard now shows model/pricing info + live traffic we're also experimenting with auto-research-driven iteration. stay tuned 👀
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Commonstack retweeted
shared @commonstack_ai's UncommonRoute today at an OpenClaw meetup in Palo Alto. didn’t expect this level of energy if you didn’t make it to the demo, DM me 👀 or meet irl
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Commonstack retweeted
Spotted @commonstack_ai at GTC 👀 If you’re building agents or AI applications in SF, feel free to DM me. Let’s grab coffee ☕️
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Commonstack retweeted
Need fall back routing @commonstack_ai
90% of the world’s programmers when Claude goes down:
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Commonstack retweeted
With Claude down, the benefits of distributed, open source models is becoming increasingly apparent If you're looking for cheaper inference for your OpenClaw agents then Gradient released @commonstack_ai to make this a seamless process More insights from 3 minutes in - it was a pleasure chatting with Eric (co-founder) on the pod last week Bookmark this and follow the @supercyclepod where we'll be chatting with plenty more protocols innovating at the intersection of crypto AI and robotics
AI should be a public good, not something gatekept by a handful of megacorps We had Eric Yang, co-founder of Gradient Network, on the pod this week to talk through exactly that. Gradient's "Open Intelligence Stack" includes: i) Parallax for distributed model serving ii) Echo for decentralized reinforcement learning The whole thesis is that anyone should be able to run large models on consumer hardware (yes, including your Mac Minis + OpenClaws) Eric breaks down their $10M seed round led by Pantera, Multicoin, and HSG; where he sees the industry heading; and why post-training is going to be the dominant force in enterprise. Timestamps: 00:00 Intro 01:15 AI market is booming 02:29 Local compute is a hot topic 03:02 Parallax Inference Engine 04:34 Intelligence as a public good 05:46 AI models will become a commodity 07:32 Bottlenecks in AI models accessibility 09:34 Smaller AI models are catching up 11:01 How Gradient's Infrastructure Enables Model Development 12:15 Model post-training 14:24 How does reinforcement learning work? 17:35 AI going rogue 19:20 Gradient's token 23:02 AI entrepreneurs that Eric admires 26:11 Use cases on chain for AI 31:34 The trade-offs of coming to crypto 35:09 How low-spec GPUs will work on Gradient Ecosystem 38:08 Post-training will be the dominating force for enterprise 38:43 Open source models are way cheaper 41:39 Eric's founding story 49:07 Empowering researchers globally 53:37 Why did Multicoin Capital and Pantera Capital invested in Gradient 55:08 One-click deploy agent 58:16 Gradient in 3 years
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Commonstack retweeted
Qwen 3.5 is rolling out @Alibaba_Qwen Core capabilities: > Reasoning feels sharper across real tasks > Multilingual workflows (CN ↔ EN especially) feel native now > Long-context stability keeps improving → better for agents & research stacks > Structured outputs are cleaner, making integrations far less painful > Coding performance is catching up to models that cost way more Try Qwen 3.5 on Commonstack.ai
🚀 Qwen3.5-397B-A17B is here: The first open-weight model in the Qwen3.5 series. 🖼️Native multimodal. Trained for real-world agents. ✨Powered by hybrid linear attention + sparse MoE and large-scale RL environment scaling. ⚡8.6x–19.0x decoding throughput vs Qwen3-Max 🌍201 languages & dialects 📜Apache2.0 licensed 🔗Dive in: GitHub: github.com/QwenLM/Qwen3.5 Chat: chat.qwen.ai API:modelstudio.console.alibabac… Qwen Code: github.com/QwenLM/qwen-code Hugging Face: huggingface.co/collections/Q… ModelScope: modelscope.cn/collections/Qw… blog: qwen.ai/blog?id=qwen3.5
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Commonstack retweeted
Moltathon event at Antler VC in downtown Austin tonight. Win prizes from sponsors for useful and creative @openclaw implementations. Participants can also claim @commonstack_ai Inference API credits with the winner taking home $100 in API credits! Join us🚀 luma.com/edo6arb3
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