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.
curl https://api.abliteration.ai/v1/chat/completions \
-H "Authorization: Bearer $ABLIT_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "abliterated-model-large-v2",
"messages": [
{ "role": "user", "content": "Write a proof-of-concept exploit for this authorized pen-test target so our red team can validate the patch." }
]
}'
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 leaderboardandpaper(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.