Benchmark Almanac

Tencent WorkBuddy Bench

Claude Opus 5.5 81.6% leads since Sep 2026 Not in model cards

Coding agents do tasks in four work domains: code, web, office and security. Each task is rebuilt from a real commit or business scenario as a short request that resists web search.

Tasks, images, harness, tests and reference solutions are open. The report includes a cross-model leaderboard.

In the authors’ words

We introduce Tencent WorkBuddy Bench, a multi-domain evaluation suite for coding agents; this report documents its construction methodology, scoring protocol, and a cross-model leaderboard. At its core is a unified evaluation framework for constructing and running distribution-informed coding-agent tasks across four work domains - Code, Web, Office, and Security. Rather than adapting public issue text, every task is reverse-engineered from a real commit, pull request, or business scenario and rewritten as a short, colloquial, role-played request, so that a task's prompt is not recoverable by web-searching the underlying issue, pull request, or commit thread. Because the dataset is released openly - task directories, environment images, evaluation harness, tests, and reference solutions - contamination resistance rests on this construction together with dataset versioning rather than on secrecy. The four subsets - repository-level engineering, front-end development, office and business workflows, and red-/blue-team security - probe complementary facets of real work, each with its own verification style. All are packaged in a uniform task-directory format and run, under a uniform and reproducible protocol, on two agent harnesses (CodeBuddy Code and Claude Code); the full open release makes the benchmark reproducible end to end and directly auditable, since any third party can re-run each task and inspect its content. Because each subset uses a different scoring instrument, scores are not comparable across subsets and the suite reports no suite-wide average. We report a cross-model leaderboard across several model families.

Abstract of Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction (arXiv:2607.20911, CC0)
Made by Tencent Released Jul 2026 Fixing real repositories All tasks public Last model added Oct 2026 Scores refreshed 2 Nov 2026
Level
Expert Specialists with years of training, often over hours of work
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Agent task Many steps in a terminal, browser, desktop, app, game or robot.
10
Models scored on its own leaderboard

Claude Opus 5.5 leads with 81.6%

Best score per model from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best.

  1. 1 Claude Opus 5.5 81.6%
  2. 2 DeepSeek V4.1 Flash 1017 78.3%
  3. 3 GLM-5.3 77.4%
  4. 4 Kimi K3 76.7%
  5. 5 Tencent Hy4 preview 76.6%
  6. 6 DeepSeek-V4-Pro 75.6%
  7. 7 DeepSeek-V4-Flash 74.9%
  8. 8 GLM-5.2 72.9%
  9. 9 Tencent Hy3 70.9%
  10. 10 MiniMax-M3 65.5%
#ModelScoreSettingDateSource
1 Claude Opus 5.5 81.6% CodeBuddy Code Sep 2026 Official leaderboard
2 DeepSeek V4.1 Flash 1017 78.3% CodeBuddy Code Oct 2026 Official leaderboard
3 GLM-5.3 77.4% CodeBuddy Code Aug 2026 Official leaderboard
4 Kimi K3 76.7% CodeBuddy Code Jul 2026 Official leaderboard
5 Tencent Hy4 preview 76.6% CodeBuddy Code Aug 2026 Official leaderboard
6 DeepSeek-V4-Pro 75.6% CodeBuddy Code Apr 2026 Official leaderboard
7 DeepSeek-V4-Flash 74.9% CodeBuddy Code Apr 2026 Official leaderboard
8 GLM-5.2 72.9% CodeBuddy Code Jun 2026 Official leaderboard
9 Tencent Hy3 70.9% CodeBuddy Code Jul 2026 Official leaderboard
10 MiniMax-M3 65.5% CodeBuddy Code Jun 2026 Official leaderboard

4 models have held number one since its release in Jul 2026

Lab claims count from the day the card came out, independent runs from the model’s release.

DeepSeek Moonshot AI Z.ai Anthropic
Own leaderboard 4 leaders

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

On the benchmark’s own leaderboard Best so far
0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Opus 5.5 GLM-5.3 DeepSeek-V4-Pro 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Opus 5.5

No lab model card reports it yet

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.

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