Benchmark Almanac

SusVibes

GPT-5.5 43.5% leads since April 2026
Not in model cards

Coding agents rebuild a feature in a real open-source Python project from a plain-language request. Each task comes from a historical vulnerability fix, so the original human version was insecure. Hidden functional tests and security tests check the result. Scores report the share of solutions that work and the share that are also secure.

Endor Labs runs it as the Agent Security League, a public board of agent harness and model pairs (Claude Code, Codex, Cursor, SWE-agent). Endor added an anti-cheating check because some agents read the git history to recover the original fix. The paper and Endor describe 200 tasks across 77 CWE types, while the repository README now lists 186. The repository has about 57 GitHub stars and names ICML 2026 as the venue.

Released
December 2025
Kind
Secure code and review
Level
Expert
Scored
Checked automatically
Answers
Code
Tasks
All public
Strongest model tested
GPT-5.5 #20 on the capability index
Last model added
May 2026
Scores refreshed
November 12th, 2026

In the authors’ words

Vibe coding is a new software development paradigm in which human engineers prompt a large language model (LLM) agent to complete complex coding tasks with little supervision. Although vibe coding is increasingly adopted, is the generated code really safe to deploy in production? To investigate this question, we propose SUSVIBES, a benchmark consisting of 186 feature-request software engineering tasks from real-world open-source projects, for which, human programmers committed vulnerable implementations. We evaluate 12 widely used coding agentic settings with frontier models on the benchmark. Disturbingly, all agents perform poorly in terms of software security. Although 57% of the solutions from SWE-Agent with Claude 4 Sonnet are functionally correct, only 11.8% are secure. Further experiments demonstrate that preliminary security strategies, such as augmenting the feature request with vulnerability hints, cannot mitigate these security issues. Our findings raise serious concerns about the widespread adoption of vibe coding, particularly in security-sensitive applications. The code and dataset are available at https://github.com/LeiLiLab/susvibes. The leaderboard is at https://leililab.github.io/susvibes-leaderboard.

Abstract of Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks (arXiv:2512.03262, CC0)
13
Entries on its own leaderboard, 10 models we track

GPT-5.5 leads with 43.5% among models released by May 2026

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.

The newest model with a score here came out in May 2026, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 1 GPT-5.5 default 43.5%
  2. 2 MAI Code 1 Flash Internal default 42.5%
  3. 3 Claude Opus 4.8 default 32.8%
  4. 4 MiniMax M3 (MXFP8) default 26.3%
  5. 5 MiniMax-M2.7 default 18.8%
  6. 6 Qwen3.5-35B-A3B default 18.3%
  7. 7 GLM-5 default 14%
  8. 8 Gemini 3 Pro default 12.9%
  9. 9 Claude Sonnet 4 default 11.8%
  10. 10 glm-4.7-flash default 9.70%
  11. 11 Kimi K2 Sep 2025, default 9.10%
  12. 12 Gemini 2.5 Pro (snapshot unspecified) default 8.10%
  13. 13 Qwen3-Coder-Next default 7.50%

Source: Official SusVibes leaderboard

#EntryScoreSettingDateSource
1 GPT-5.5 43.5% default, mini-SWE-agent Apr 2026 Official leaderboard
2 MAI Code 1 Flash Internal 42.5% default, mini-SWE-agent Aug 2026 Official leaderboard
3 Claude Opus 4.8 32.8% default, Claude Code May 2026 Official leaderboard
4 MiniMax M3 (MXFP8) 26.3% default, mini-SWE-agent Aug 2026 Official leaderboard
5 MiniMax-M2.7 18.8% default, mini-SWE-agent Mar 2026 Official leaderboard
6 Qwen3.5-35B-A3B 18.3% default, mini-SWE-agent Feb 2026 Official leaderboard
7 GLM-5 14% default, SWE-agent Feb 2026 Official leaderboard
8 Gemini 3 Pro 12.9% default, SWE-agent Nov 2025 Official leaderboard
9 Claude Sonnet 4 11.8% default, OpenHands May 2025 Official leaderboard
10 glm-4.7-flash 9.70% default, SWE-agent Jan 2026 Official leaderboard
11 Kimi K2 Sep 2025 9.10% default, OpenHands Sep 2025 Official leaderboard
12 Gemini 2.5 Pro (snapshot unspecified) 8.10% default, OpenHands Dec 2025 Official leaderboard
13 Qwen3-Coder-Next 7.50% default, SWE-agent Feb 2026 Official leaderboard

5 models have held number one on the official leaderboard since its release in December 2025

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

Google DeepMind Z.ai Qwen MiniMax OpenAI
Own leaderboard Not tested since May 2026
GPT-5.5 43.5%

Source: Official SusVibes leaderboard

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% 2025 2026 Near the ceiling (85%) GPT-5.5 MiniMax-M2.7 GLM-5 Claude Sonnet 4 0% 20% 40% 60% 80% 100% 2025 2026 Near the ceiling (85%) GPT-5.5 MiniMax-M2.7

Source: Official SusVibes 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.

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