Abliterated model large v2: GLM 5.3 84.5% CyberGym

Abliteration.ai released abliterated-model-large-v2, an abliterated version of GLM 5.3 hosted in FP8, scoring 84.5% pass@1 on CyberGym's 1,507 OSS-Fuzz bugs across 188 projects, 41.8% on Terminal-Bench 4.0, and 105 of 869 tasks on ExploitGym in a 2-hour window. The model, priced at $5 per 1M tokens with 1M token context and zero data retention, is designed for offensive cyber, AI red teaming, and agent testing, with GPT-5.5 at 85.6% and DeepSeek V4 at 83.3% on CyberGym for comparison.

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 https://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 leaderboard https://www.cybergym.io/cybergym/ and paper https://arxiv.org/abs/2506.02548 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.