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# On long-running tasks, the harness matters more than the model, Nvidia finds
- URL: https://www.metatalks.ai/nvidia-lifts-claude-opus-5-from-30-to-a-perfect-reasoning-test-score-without-touching-the-model/
- Published: 2026-08-24T15:33:00.000Z
- Updated: 2026-08-24T15:32:59.000Z
- Author: Al
- Tags: News, Agentic AI, #newswire

**Nvidia's custom-built harness took Claude Opus 5 from 30% to a perfect 100% on the interactive reasoning benchmark ARC-AGI-3, with nothing changed in the model itself.**

The 30% run was already the strongest result of any model Nvidia's researchers put through the trials. They named that harness [the Agentic Variation Operators, or AVO](https://arxiv.org/abs/2603.24517?ref=metatalks.ai). Their research, published last week, argues that on long-horizon work — tasks that chain many decisions together, sometimes across days, before any finished work comes out — the choice of harness weighs heavier than the choice of model.

ARC-AGI-3 is built from a collection of 2D games that arrive with no explanation of the rules, leaving the model to work out for itself how they are played and how to win. A 100% score means it wins them as capably as a person can.

The harness is the scaffolding that handles memory, context and feedback, and it is what turns a model into an agent at all. Adel El Hallack, a vice president of product at Nvidia's AI unit, told [TechCrunch](https://techcrunch.com/2026/08/21/nvidia-just-showed-that-the-harness-not-the-ai-model-is-now-the-real-hero/?ref=metatalks.ai) that the world by and large treats an agent as little more than an API call to the model, when an agent takes in considerably more: the model, the tools built around it that Nvidia calls the harness, the runtime, and the skills and libraries it is handed access to.

A harness also has to watch the agent it runs. El Hallack said the more interesting piece of the work was setting a supervising agent alongside the main agent doing the job, in the role of a chief executive: it nudges the agent when it strays off direction, sets off down a route that may end up going nowhere, or goes back over ground it has already covered. The concept is not new, but most agent users today rely on a single layer for their harness, such as Claude Code, Codex or Hermes.

Nvidia's result adds to a swelling body of evidence that the model you pick is nowhere near the only thing determining how an agent performs. OpenAI, whose own models came in under 10% on ARC-AGI-3, ran a study of the benchmark last month and found that tweaking two settings on the harness tripled those scores — though not one model in that study got anywhere near a perfect 100%. In July, Databricks published research indicating that the harness, far more than the model, has a dramatic effect on what AI costs.

The harness that closed the gap is not on offer, though. AVO is a research build rather than a fresh Nvidia product, and what the company puts out instead is an assortment of open technology for assembling harnesses under the Nemo name, some of it sold commercially and a large share of it free.