At Dot, we understand deeply that the future of AI does not rest in the hands of one singular model, but rather, intelligent orchestration. That is why we open-sourced Dot Loom, and today, we are shipping its biggest upgrade yet. We gave OpenAI, Claude and Dot the same 6 backend nightmares: -Billing races -Tenant data leaks -Webhook replays -Streaming refund bugs -OAuth failures -SSRF attacks Results from a blinded Deepseek judge: OpenAI: 100% quality, 12.67 credits Claude: 88.3% quality, 3.50 credits Dot: 70% quality, 1.00 credit Claude reached 88.3% of the top score using 28% of the credits. Dot reached 70% using 8%. But Dot Loom is not an OpenAI or Claude wrapper, those are just recognizable examples. You can combine any models you want: -OpenAI writes, Claude reviews. -Claude writes, DeepSeek reviews. -Qwen drafts, OpenAI finalizes. A local Ollama model handles easy work, then a stronger cloud model checks risky requests. Loom is model and provider agnostic. It works with Dot, OpenAI-compatible APIs, @OpenRouter, @Ollama, LM Studio and local models. With the Dot API, one key and one endpoint gives you access to @OpenAI, @ClaudeAI, @Deepseek_ai , @Alibaba_Qwen and Dot models. You can also bring your own providers and configure every role yourself. Loom controls how much inference each task receives: • Lean: 1 model call • Balanced: writer + reviewer • Strict: writer + critic + finalizer • No paid router call • Model-specific credit, call and latency budgets • Raw answers, costs, token usage, judge reasons and receipts Our goal is to use cheaper models where they're enough, stronger models where they matter, and independent models when verification is critical. Open source, with raw benchmark results, charts, receipts, and reproducible runners: github.com/usedotai/dot-l…
