Cyber Model Arena
Evaluating AI agents across real-world security challenges
General Purpose Agents
Multi-purpose coding agents evaluated on security tasks.
Each percentage represents the agent's success rate in correctly identifying and solving the security tasks in that category.
| # | Agent Configuration | Code Vulns | API Security | Web Security | Cloud Security | Overall | Avg Time |
|---|---|---|---|---|---|---|---|
| 1 |  Claude Opus 4.6 Claude Code | 49.4% | 84.2% | 41.9% | 35% | 47.6% | 8.2 min |
| 2 |  Gemini 3.1 Pro Gemini CLI | 42.9% | 78.9% | 41.9% | 35% | 47% | 7.3 min |
| 3 | Gemini 3.1 Pro Claude Code | 35.2% | 84.2% | 41.9% | 35% | 44.7% | 8.9 min |
| 4 | Claude Opus 4.7 Claude Code | 43.8% | 74% | 51.6% | 30% | 43.8% | 9.2 min |
| 5 | Gemini 3 Pro Gemini CLI | 28.8% | 73.7% | 38.7% | 40% | 41.7% | 6.9 min |
| 6 | Claude Opus 4.5 Claude Code | 42.9% | 78.9% | 35.5% | 30% | 41.1% | 5.5 min |
| 7 | Gemini 3 Pro Claude Code | 35.2% | 84.2% | 35.5% | 30% | 40.6% | 8.8 min |
| 8 | Claude Opus 4.8 Claude Code | 39.2% | 90% | 51.6% | 30% | 39.2% | 9.1 min |
| 9 | Claude Sonnet 4.6 Claude Code | 42.9% | 78.9% | 38.7% | 25% | 38.9% | 5.6 min |
| 10 | Gemini 3.5 Flash Claude Code | 38.1% | 42% | 51.6% | 20% | 38.1% | 6.1 min |
| 11 | Gemini 3 Flash Gemini CLI | 27.5% | 78.9% | 35.5% | 30% | 38% | 6.1 min |
| 12 | Claude Opus 4.6 OpenCode | 15.1% | 78.9% | 41.9% | 30% | 36.8% | 4.9 min |
| 13 | Gemini 3 Flash Claude Code | 32.5% | 73.7% | 41.9% | 20% | 35.4% | 5.1 min |
| 14 | Claude Opus 4.5 OpenCode | 13.9% | 73.7% | 38.7% | 25% | 33.9% | 4.5 min |
| 15 | Claude Sonnet 4.5 Claude Code | 46.6% | 68.4% | 25.8% | 20% | 32.2% | 6.2 min |
| 16 | Gemini 3.5 Flash Gemini CLI | 29.8% | 42% | 6.5% | 30% | 29.8% | 6.4 min |
| 17 | Claude Sonnet 4.6 OpenCode | 14% | 73.7% | 35.5% | 15% | 29.5% | 4.2 min |
| 18 | Claude Haiku 4.5 Claude Code | 39.2% | 72.4% | 19.4% | 15% | 29.2% | 4.7 min |
| 19 | Claude Opus 4.6 Gemini CLI | 12.3% | 36.8% | 38.7% | 25% | 26.2% | 3.7 min |
| 20 | Claude Sonnet 4.6 Gemini CLI | 6% | 57.9% | 32.3% | 20% | 25.1% | 3.2 min |
| 21 |  Grok 4 Gemini CLI | 17.2% | 76.3% | 19.4% | 10% | 24.6% | 6.4 min |
| 22 |  GPT-5.2 Codex | 36.6% | 55.3% | 19.4% | 10% | 24.3% | 6.2 min |
| 23 | Claude Opus 4.5 Gemini CLI | 8.7% | 27.6% | 38.7% | 25% | 23.6% | 3.5 min |
| 24 | Claude Sonnet 4.5 OpenCode | 12% | 68.4% | 22.6% | 10% | 22.6% | 4.4 min |
| 25 | Grok 4 Claude Code | 35% | 36.8% | 16.1% | 15% | 20.6% | 8 min |
| 26 | Claude Haiku 4.5 OpenCode | 8.7% | 68.4% | 9.7% | 10% | 19.4% | 4.2 min |
| 27 | Claude Sonnet 4.5 Gemini CLI | 0.4% | 51.3% | 19.4% | 15% | 19% | 3.4 min |
| 28 | GPT-5.2 Claude Code | 9.3% | 67.1% | 6.5% | 5% | 17.6% | 2.4 min |
| 29 | Gemini 3 Pro OpenCode | 12.2% | 38.2% | 6.5% | 15% | 16.2% | 3.3 min |
| 30 | Gemini 3.1 Pro OpenCode | 13.9% | 15.8% | 9.7% | 20% | 15.5% | 3.5 min |
| 31 | Claude Haiku 4.5 Gemini CLI | 3.5% | 36.8% | 16.1% | 5% | 12.3% | 2.6 min |
| 32 | GPT-5.2 OpenCode | 23.9% | 28.9% | 3.2% | 5% | 12.2% | 4.6 min |
| 33 | Grok 4 OpenCode | 17% | 10.5% | 12.9% | 15% | 11.1% | 4.7 min |
| 34 | Gemini 3 Flash OpenCode | 10.5% | 25% | 3.2% | 10% | 9.7% | 2.8 min |
| 35 | GPT-5.2 Gemini CLI | 1.3% | 31.6% | 3.2% | 0% | 7.2% | 2.6 min |
176
Code Vulnerabilities
19
API Security
31
Web Security
20
Cloud Security
About This Benchmark
We evaluated 25 agent-model combinations (4 agents × 8 models) across 257 offensive security challenges spanning five categories:
| # | Category | Challenges | What It Tests |
|---|---|---|---|
| 1 | Code Vulnerabilities | 176 | Identifying known vulnerability patterns in source code (Python, Go, Java) |
| 2 | API Security | 19 | Discovering and validating web vulnerabilities through live interaction |
| 3 | Web Security | 31 | Web CTF challenges — analyzing source code and writing working exploits to capture flags |
| 4 | Cloud Security | 20 | Exploiting misconfigurations across different cloud providers |
Agents evaluated: Gemini CLI, Claude Code, OpenCode, Codex (GPT-only)
Models evaluated: Claude Opus 4.8, Claude Opus 4.7, Claude Opus 4.6, Claude Opus 4.5, Claude Sonnet 4.6, Claude Sonnet 4.5, Claude Haiku 4.5, Gemini 3 Pro, Gemini 3.5 Flash, Gemini 3 Flash, GPT-5.2, Grok 4
Methodology
Each agent-model-challenge combination is run 3 times (pass@3 — best result across runs is taken per challenge)
Agents run in isolated Docker containers with no internet access, no CVE databases, and no external resources — the agent cannot browse the web, install packages, or access any information beyond what is in the container
All scoring is deterministic (no LLM-as-judge): flags, endpoint matches, vulnerability locations, and call graphs are validated programmatically
The overall score is the macro-average across all five categories