safety benchmark
CyberSecEval 4 Leaderboard
CyberSecEval 4 is an evaluation suite covering cybersecurity-related capabilities and risks of large language models. The insecure-code-generation tracks measure whether a model produces vulnerable code: the Instruct track presents coding requests designed to elicit known insecure patterns, while the Autocomplete track prompts the model with code context leading up to a known insecure pattern, with vulnerabilities detected via static analysis.
Updated Aug 17, 2026
Models1
Model coverage1
MetricScore
EvidenceB
CyberSecEval 4 Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMI | Score63.0% | Percentile100.0% | Participants1 | EvidenceC | Evaluated |
CyberSecEval 4 Highlights
The leading models and scores on this benchmark.
The Top AI Models for CyberSecEval 4
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis cyberseceval 4 AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01MIMAI-Thinking-1Microsoft
- Score
- 63.0%
Strengths
- Ranks #1 of 1 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures CyberSecEval 4, not total model capability
Selection summary
Best AI Models for CyberSecEval 4
MAI-Thinking-1 currently leads CyberSecEval 4 with 63.0%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is CyberSecEval 4?
What CyberSecEval 4 measures and how its scores work.
CyberSecEval 4 is an evaluation suite covering cybersecurity-related capabilities and risks of large language models. The insecure-code-generation tracks measure whether a model produces vulnerable code: the Instruct track presents coding requests designed to elicit known insecure patterns, while the Autocomplete track prompts the model with code context leading up to a known insecure pattern, with vulnerabilities detected via static analysis.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- CyberSecEval 4
- Modality
- text
- Primary category
- safety
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- cyberseceval-4|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about CyberSecEval 4.
Which model scores highest on CyberSecEval 4?
MAI-Thinking-1 is currently ranked first with 63.0%.
What does CyberSecEval 4 measure?
CyberSecEval 4 is an evaluation suite covering cybersecurity-related capabilities and risks of large language models. The insecure-code-generation tracks measure whether a model produces vulnerable code: the Instruct track presents coding requests designed to elicit known insecure patterns, while the Autocomplete track prompts the model with code context leading up to a known insecure pattern, with vulnerabilities detected via static analysis.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.