Taskade does not pick one AI provider and stop there. Inside a single project, agent, or automation, work can flow through a frontier model from OpenAI, Anthropic, or Google, and also through open-weight models like Qwen, Kimi, DeepSeek, MiniMax, and GLM. The point is choice, not allegiance.
TL;DR: Open-source AI models are the workhorse layer of a modern stack. Taskade routes high-volume, repetitive steps to open-weight models like Qwen, Kimi, DeepSeek, MiniMax, and GLM, and reserves frontier models for hard reasoning. You get lower cost, faster runs, and you never lock into one vendor.
Why Open-Source Models Exist
For a long time, the best AI lived behind a single API. That changed when research labs in China and around the world started releasing model weights publicly. Anyone can now run, study, and improve them.
This matters because it breaks the assumption that more expensive always means more useful. A well-tuned open model can classify an email, summarize a meeting, extract structured data, or draft a first pass of a document at a fraction of the cost of a frontier model. The frontier still wins on the hardest tasks. The open layer wins on the volume.
What You Actually Get
Inside Taskade, an AI agent or an automation step can be set to a specific model, or it can use auto-routing. Auto-routing reads what the step is doing and picks an appropriate model for it.
The practical effect is that a project can use a frontier model for the planning step, an open-source model for fifty downstream content steps, and a small fast model for tagging at the end. You do not manage three separate accounts. You do not write three different integrations. You write the work once.
Open-source models live inside the Intelligence layer of Workspace DNA. They power the same agents that read your projects and trigger your automations, just at a different price point.
When to Reach for an Open Model
Open-source models shine on tasks where the shape of the answer is well understood:
- Classifying support tickets into known categories
- Pulling structured fields out of free text
- Summarizing meeting notes into action items
- Drafting first-pass copy that a human will edit
- Running bulk transformations across a long list of projects
For tasks that need deep reasoning, long planning, or sensitive judgment, you can still route to a frontier model. The two layers work together. Neither replaces the other.
What It Means for Cost
The simple version. Open-weight models cost less per call. When the same work runs hundreds or thousands of times a month inside an automation, the difference compounds. A team that routes routine steps to open models can run far more work on the same credit budget. The savings are real, and they are spent on doing more, not on doing the same thing for less.
Why Not Always Open
Open models are catching up fast, but the frontier is still the frontier. The newest reasoning models from OpenAI, Anthropic, and Google still lead on hard math, deep coding tasks, and long agent loops. The right answer is rarely "only open" or "only frontier." It is both, applied where each one is strongest. Taskade is built to make that choice easy.