OpenCode's public data dashboard just got a new ranking dimension: unique users per model. It sounds like a small tweak, but it fixes a real distortion that's been quietly skewing how the community reads model adoption.
The token count problem
OpenCode tracks usage across its platform, which now counts over 7.5 million developers every month. Their existing leaderboard ranked models by raw token volume. The issue? Some models are just naturally chatty. They write longer responses, use more tokens per session, and end up looking dominant on a token chart even if only a handful of power users are driving all that volume.
The team flagged this directly: some models are token-heavy, so they skew upwards in rankings. Unique people using a model is a more accurate signal of real adoption. So they built it.
What the numbers actually show
The gap between token rank and user rank is significant. DeepSeek V4 Flash sits at #1 by tokens with 8.1 trillion, and DeepSeek V4 Pro at #2 with 3.9 trillion. But those numbers are partly a function of how those models are used in agentic sessions, not just how many people chose them.
Don't miss what's next in AI
Join 300,000+ engineers and researchers who get the signal, not the noise.
- Full access to in-depth AI research breakdowns
- Be the first to know what's trending before it hits mainstream
- Daily curated papers, repos, and industry moves