Model Routing

What is Model Routing?

Model routing is the practice of choosing, for each task, which AI model should handle it. Instead of sending everything to the most powerful (and most expensive) frontier model, a routing layer matches the job to a model with enough capability at the lowest cost or latency. Simple tasks go to small or open-weight models; hard reasoning, long-horizon computer use, or complex coding go to frontier models.

Why It Matters for AI Work

As agents take on more work, token spend becomes a major operating cost. Higgsfield CEO Alex Mashrabov says his company spends over $4M a month on models internally and calls the discipline "tokenomics": working out how many tokens a job needs and picking the most efficient ones. He reports 80%+ margins on post-trained open-weight models versus 20-30% on closed models, and says Higgsfield picks the model itself in more than 40% of agentic workflows.

How It Works in Practice

• Classify the task by difficulty, latency needs, and quality bar
• Pick a model tier: frontier for hard work, open-weight or post-trained for routine, high-volume work
• Feed back outcomes: customer decision data can be used to post-train cheaper models that compress multi-step tasks into one
• Let the harness decide: the agent harness, not the user, makes the call

Model Routing at TeamDay

TeamDay agents can run on different models per task, so routine work does not need to consume frontier-model budgets.