GitHub Copilot launches multi-model teaming: HydraFusion cuts costs by up to 67%

Beating AI Insight Flash News: GitHub has added a multi-model orchestration system called HydraFusion to Copilot. The system first determines how to handle a task before calling different models, operating in three modes: simple tasks are assigned to a single model for direct completion; complex tasks start with a lower-cost model, which is escalated to a more powerful model if the result is unsatisfactory; for tasks requiring review, one model completes the work first, while another model from a different family is tasked with identifying errors, after which the first model revises its output. GitHub compared HydraFusion with Claude Opus 5 across three programming agent benchmarks: it scored 4.9 percentage points higher than Claude Opus 5 on TerminalBench 2.1, 1.5 points lower on DeepSWE, and only 0.1 points lower on CheckpointBench (nearly on par); meanwhile, cost reductions reached 67%, 36%, and 65% respectively across the three benchmarks. This approach is very similar to Sakana AI’s Fugu. Both shift the focus from "which model to choose" to "how to organize multiple models". The key difference is that Fugu is a trained orchestrator model that learns to call different agents and can even recursively invoke itself; HydraFusion, by contrast, currently only selects among three fixed execution modes: Single, Cascade, and Critique, and is essentially a multi-model scheduler integrated directly into Copilot. HydraFusion is now available as a research preview for all GitHub Copilot plans, and can be enabled via the experimental features in Copilot CLI. GitHub also noted that the system is currently best suited for single-turn programming tasks that can be clearly defined at once, with multi-turn long tasks still under optimization.