Tencent WorkBuddy Bench
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.
- Made by
- Tencent
- Released
- July 2026
- Kind
- Fixing real repositories
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
- Claude Opus 5.5 #4 on the capability index
- Last model added
- October 2026
- Scores refreshed
- November 12th, 2026
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.
Claude Opus 5.5 leads with 81.6%
Best score per entry from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.
- 1 Claude Opus 5.5 81.6%
- 2 Hy4-dev 79.3%
- 3 Kimi K3.1 Pro 78.7%
- 4
DeepSeek V4.1 Flash 1017 78.3%
- 5 GLM-5.3 77.4%
- 6 Kimi K3 76.7%
- 7
Tencent Hy4 preview 76.6%
- 8
DeepSeek-V4-Pro 75.6%
- 9
Step 5 Preview 75.6%
- 10
MiniMax-M3.1-Flash 75.6%
- 11
DeepSeek-V4-Flash 74.9%
- 12 GLM-5.2 72.9%
- 13 MiMo-V2.6 Pro 72.9%
- 14
Tencent Hy3 70.9%
- 15
MiniMax-M3 65.5%
Source: Official Tencent WorkBuddy Bench leaderboard
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 5.5 | 81.6% | CodeBuddy Code | Sep 2026 | Official leaderboard |
| 2 | Hy4-dev | 79.3% | CodeBuddy Code | Official leaderboard | |
| 3 | Kimi K3.1 Pro | 78.7% | CodeBuddy Code | Oct 2026 | Official leaderboard |
| 4 | | 78.3% | CodeBuddy Code | Oct 2026 | Official leaderboard |
| 5 | GLM-5.3 | 77.4% | CodeBuddy Code | Aug 2026 | Official leaderboard |
| 6 | Kimi K3 | 76.7% | CodeBuddy Code | Jul 2026 | Official leaderboard |
| 7 | | 76.6% | CodeBuddy Code | Aug 2026 | Official leaderboard |
| 8 | | 75.6% | CodeBuddy Code | Apr 2026 | Official leaderboard |
| 9 | | 75.6% | CodeBuddy Code | Sep 2026 | Official leaderboard |
| 10 | | 75.6% | CodeBuddy Code | Oct 2026 | Official leaderboard |
| 11 | | 74.9% | CodeBuddy Code | Apr 2026 | Official leaderboard |
| 12 | GLM-5.2 | 72.9% | CodeBuddy Code | Jun 2026 | Official leaderboard |
| 13 | MiMo-V2.6 Pro | 72.9% | CodeBuddy Code | Sep 2026 | Official leaderboard |
| 14 | | 70.9% | CodeBuddy Code | Jul 2026 | Official leaderboard |
| 15 | | 65.5% | CodeBuddy Code | Jun 2026 | Official leaderboard |
4 models have held number one on the official leaderboard since its release in July 2026
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official Tencent WorkBuddy Bench leaderboard
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Official Tencent WorkBuddy Bench leaderboard
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
More in Coding
- SWE-bench Verified In use 500 human-validated GitHub issues from 12 Python repos. Agent writes a patch that must pass hidden tests. Claude Opus 4.7 83.5%
- LiveCodeBench v6 In use Competitive programming problems collected after model cutoffs from LeetCode, AtCoder, Codeforces. Scored pass@1 on hidden tests.
DeepSeek-V4-Pro 93.5%
- SWE-bench Multilingual In use 300 GitHub-issue tasks across nine programming languages in SWE-bench format. Patch graded by repository tests. Claude Fable 5.2 96.6%
- DeepSWE In use 113 original long-horizon engineering tasks across 91 repos and five languages, written from scratch. Hand-written verifiers accept alternative solutions. GPT-6 Astra 74.1%
- SciCode In use Scientist-curated research coding problems split into subproblems across natural sciences. Code checked against test cases.
Seed2.1 Pro 59.8%
- Codeforces rating In use Model solves recent Codeforces contest problems. An Elo-style rating is computed from simulated contest performance.
DeepSeek V4.1 Flash 3,471