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NorthCUA Models and Harness: Benchmarks, Downloads and What Is Actually Available
NorthCUA pairs open computer-use models with a reusable runtime, but its downloadable checkpoint and reported scores need to be read separately.
NorthCUA is a computer-use model family and an open Desktop/Web agent harness from Shanghai Qiji Zhifeng. Its November 1 technical report describes training agents to finish software tasks, while updated November 3 documentation confirms public model resources. The useful distinction is what developers can download versus what the research evaluated.
NorthCUA Nano, Mini and Pro
The NorthCUA technical report describes three Qwen3.5-based sizes: Nano at 9 billion parameters, Mini at 35 billion total and 3 billion active, and Pro at 397 billion total and 17 billion active.
In the authors' OSWorld-Verified evaluation, Nano reaches 78.9%, Mini 85.3% and Pro 88.6% task success. That evaluation uses 361 tasks with a 100-step limit. These are reported research results, rather than independent measurements of the current public download.
The more demanding OSWorld 2.0 result gives a useful counterweight. Pro records 30.6% complete-task success and 66.8% partial credit across 108 tasks. Making progress and finishing every requirement remain very different outcomes.
What can developers download?
The maintained NorthCUA repository provides an Apache-2.0 harness built on NexAU. It combines visual context, working notes, outcome checks and recoverable batches. The earlier NorthCUA-Harness repository is now a migration notice pointing to this maintained location.
The harness is model-agnostic. Developers can connect a vision model with structured tool calls, so evaluating the runtime does not require loading Pro. Its local no-key tutorial checks integration with a deterministic model; passing that tutorial does not demonstrate autonomous task-solving ability.
The project's model and endpoint documentation records public Nano, Mini and Pro model resources under Apache-2.0. It also says its November 3 availability check supersedes an earlier HTTP 401 observation for Pro. This is a concrete improvement for anyone who previously encountered an inaccessible checkpoint.
The Pro checkpoint has an important benchmark caveat
The NorthCUA-Pro model card identifies the export as post-training checkpoint 975 and explicitly says the report's research scores have not been tied to that exact export. Available weights do not yet establish benchmark reproduction.
Pro's files occupy approximately 793.63 GB before runtime caches, across 122 weight shards. Its 17 billion active parameters describe computation through the mixture-of-experts model, not the memory needed to hold all the weights. That difference matters when estimating whether a deployment is practical.
The card gives a configured context length of 262,144 tokens and an illustrative loading example. It does not present that example as a benchmarked serving configuration. Teams still need to check image handling, tool calls and memory overhead with their chosen engine.
Why the harness is worth evaluating separately
NorthCUA's research combines verified task trajectories, long-horizon supervision and desktop–web reinforcement learning. Hard tasks feed reusable skills and verified intermediate milestones back into later attempts.
For a product team, the practical question is whether its own workflows benefit from that continuity. A browser agent that remembers why it rejected an earlier option may avoid repeating work. An agent that checks the final artifact can catch a different class of failure from one that only checks whether each click succeeded.
A sensible evaluation separates three questions: can the model choose useful actions, can the runtime preserve the task over a long session, and does the final output meet every requirement? NorthCUA supplies pieces to investigate all three. The next meaningful result will be a reproducible connection between a named checkpoint, its harness settings and complete task outcomes.
I’m Paras. I work at the intersection of AI, engineering and distribution.
I help technical founders turn AI launches and engineering work into clear stories that developers and founders read and share.