Tencent Leaderboard Splits Same Model's Score by 13.5 Points

Tencent's WorkBuddy Bench scored GPT-5.5 at 77.91 on its Security subset under one agent harness, and 64.39 under another, per the leaderboard. That's a 13.5-point swing on the same model weights, enough to flip which model looks best to anyone comparing scores.

That gap showed up the same day Anthropic reported Opus 5 more than doubling Opus 4.8's Frontier-Bench score. Anthropic also cited 30.2% on ARC-AGI 3, versus a prior field best of 7.8%, without naming which harness ran either test.

The underlying paper, from Tencent Youtu Lab, Keen Security Lab and Yunding Security Lab, builds 260 tasks from real commits, PRs and business scenarios. Each one gets rewritten as a colloquial role-played request, so the original issue thread can't be found by search.

The suite also refuses one cross-subset average, since each subset uses a different scoring instrument. Token cost varies just as much as accuracy. MiniMax-M3 averaged roughly 11.1 million input tokens per Security run, while GPT-5.5 ran the smallest output budget in the field.

A model that scores three points higher while burning four times the tokens isn't the better pick for a production loop. Rank alone won't tell you that. The smart move: benchmark your own harness on your own tasks before you pick a model.

A related paper found that strong scores on fixed benchmarks don't survive once a user's request shifts mid-conversation, per Tack, Laban and Neville.