Best LLM for Cybersecurity: AI SOC Benchmark Leaderboard
AI SOC LLM Leaderboard
The first benchmark to comprehensively measure LLM performance for Security Operations — and the foundation of Simbian's Reasoning Engine, hardened through millions of cycles in the war lab.
LLMs performance on AI SOC LLM Leaderboard
Best LLM for Cybersecurity: Full Benchmark Results
Across 100 full-kill-chain SOC scenarios, the 7 frontier LLMs tested on the Simbian AI SOC Benchmark scored between 61.4% and 67.6%. Sonnet 3.5 (Anthropic) led at 67.6%, ahead of Gemini 2.5 Pro Preview (Google) at 66.4% and Opus 4 (Anthropic) at 65.7%. No model tested cleared 70%, which is why autonomous alert triage needs an orchestration layer around the model rather than a model alone.
| Rank | Model | Provider | Benchmark score |
|---|---|---|---|
| 1 | Sonnet 3.5 | Anthropic | 67.6% |
| 2 | Gemini 2.5 Pro Preview | 66.4% | |
| 3 | Opus 4 | Anthropic | 65.7% |
| 4 | GPT 4.1 | OpenAI | 63.0% |
| 5 | Sonnet 4 | Anthropic | 62.1% |
| 6 | Sonnet 3.7 | Anthropic | 61.9% |
| 7 | DeepSeek R1 | DeepSeek | 61.4% |
Simbian AI SOC Benchmark: frontier LLM performance on the automated investigation of 100 full-kill-chain security scenarios with known ground truth. Higher is better.
Read more technical details in the blog
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Sample of Benchmark Scenarios
Our benchmark is built on the automated investigation of 100 full-kill chain scenarios that realistically mirror what human SOC analysts face every day. The created attack scenarios have known ground truth of malicious activity, allowing AI agents to investigate and be assessed against a clear baseline. The used scenarios are even based on historical behavior of well-known APT groups and cybercriminal organizations covering a wide range of MITRE ATT&CK™ Tactics and Techniques, with a focus on prevalent threats like ransomware and phishing.