Benchmark suite comparison
Benchmarking: Abliterated Large v2
GLM 5.3FP8
Terminal-Bench 4.0
Resolution rate
51.8%Opus 5
44.5%Fable 5
41.8%GLM 5.3
37.3%GPT-5.6 Sol
CyberGym
Pass@1 score
85.6%GPT-5.5
84.5%GLM 5.3
83.3%DeepSeek V4
83.1%Mythos
ExploitGym (2h)
Tasks completed / 869
216GPT-5.6 Sol
181Fable 5
105GLM 5.3
80Opus 4.8
Today we are releasing abliterated-model-large-v2. We started from GLM 5.3 and abliterated it for offensive cyber, AI red teaming, and agent testing. We host it in FP8. Use the same endpoint and key. The model id is the only change.
The scores
On CyberGym, 1,507 OSS-Fuzz bugs across 188 projects, it scores 84.5% pass@1. GPT-5.5 is 85.6%. DeepSeek V4 is 83.3%. Mythos is 83.1%.
On Terminal-Bench 4.0 it resolves 41.8% of tasks. Opus 5 is 51.8%. Fable 5 is 44.5%. GPT-5.6 Sol is 37.3%.
On ExploitGym, in a 2-hour window, it completes 105 of 869 tasks. GPT-5.6 Sol completes 216. Fable 5 completes 181. Opus 4.8 completes 80.
Those three suites are the jobs this model is for: long-horizon coding, vulnerability reproduction, and exploit work that other APIs refuse. Abliteration takes the refusal directions out of the weights.
Key specifications
- Base model: GLM 5.3
- Post-training: abliterated
- Hosting: FP8
- Context: 1M tokens, text-only
- Price: $5 per 1M tokens, input and output
- Endpoints: OpenAI-compatible
/v1/chat/completions, Anthropic-style/v1/messages, and/v1/responses - Data: zero data retention for prompts and responses, by default
How to switch
Set model to abliterated-model-large-v2. Keep the base URL, the key, and your existing client. Guides for common libraries are at docs.abliteration.ai.
Compute is limited and first-come, first-served. Load credits early if you need sustained throughput.
Sources
- Abliteration.ai eval: abliterated-model-large-v2 on GLM 5.3, hosted in FP8, Terminal-Bench 4.0, CyberGym pass@1, ExploitGym 2h TPS-normalized
- CyberGym leaderboard and paper (UC Berkeley)
- Comparator Terminal-Bench 4.0, CyberGym, and ExploitGym figures are vendor-reported on mixed harnesses and budgets. Treat the chart as indicative rather than strictly comparable.
Your AI. Your rules.