Abliterated Large V2
abliterated-large-v2abliteration-abliterated-model-large-v2- 🧠 Unrestricted reasoning model built on open-weight GLM-5.3, then abliterated
- 📏 One-million-token context window, text-only input and output
- 🆕 Replaces the prior large model, which was based on GLM-5.2
- 🔧 Native tool/function calling and web search
- ⚡ Always reasons; effort selectable at low, high, or max
- 🎯 Marketed for offensive-cyber testing, red teaming, agent evaluation
- 🏢 Served by Abliteration AI in FP8 via an OpenAI-compatible API
- 🔒 Weight edits target refusal behavior rather than capability
Abliteration AI is a Palo Alto company selling an OpenAI-compatible LLM API for work most assistants turn down: AI red teaming, cybersecurity, trust and safety, synthetic data and evals, ML research, and defense or government workflows. Its models start from open-weight releases…
Explore 1 more model by Abliteration AI →Abliterated Large V2 is Abliteration AI's default large text model, derived from the open-weight GLM-5.3 base and then further abliterated and fine-tuned by the provider. "Abliteration" here refers to a weight modification that suppresses refusal behavior, so the model answers prompts its base version might decline; the provider positions it for offensive cyber work, red teaming, and agent testing. It is served in FP8 through an OpenAI-compatible chat-completions endpoint.
Against its same-family predecessor, the difference is primarily the base: the earlier large model was derived from GLM-5.2, while V2 moves to GLM-5.3 and becomes the catalog default. Both are text-only with a one-million-token context, so the generational gain is in underlying model quality rather than context or modality. In the provider's own launch post, V2 is credited with 84.5% pass@1 on CyberGym and 41.8% on Terminal-Bench 4.0, plus 105 ExploitGym tasks completed in two hours. These figures are self-reported by the provider.
V2 always reasons, and effort can be set to low, high, or max. It is the family's long-context reasoning option for harder multi-step problems, where the trade-off is latency and token spend rather than whether the model deliberates at all.
Practically, V2 suits long-document analysis, multi-step agentic planning, and evaluation pipelines, with native function calling and web search available alongside the long context. Image inputs are not supported and are rejected with an error; the provider's separate multimodal model, with a 256K context, covers that case. Released October 2026.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
Daily usage · Abliterated Large V2 · Organic sellers
Top sellers · Abliterated Large V2 · Organic
- ▲ Apex Ant52.1%
- Super Seeder41.5%
- attacompute.ai6.3%
- Fire Ant 🔥🐜0.0%
| Seller | Health | Rep↓ | Sales | Tokens | Routing | Input $/M | Cached $/M | Output $/M | Save |
|---|---|---|---|---|---|---|---|---|---|
| Super Seeder 0xd19f…41f3 | healthy | 89.22 | $89.79 | 314.72M | #2 | $4.2425 | $0.4243 | $7.0708 | — |
| ▲ Apex Ant 0x73b4…e736 | healthy | 81.71 | $112.61 | 384.86M | #1 | $3.12 | $0.31 | $5.19 | — |
| OpenCheap 0x0ee1…6c41 | — | 12.43 | — | — | gated | $4.1577 | $0.4158 | $6.9294 | — |
| Crimson-Abliterated 0xe85d…8f07 | — | 5.00 | — | — | gated | $0.15 | $0.06 | $0.60 | −95% |
| surplus-hive 0x6145…5c58 | — | 0.00 | — | — | gated | $7.2917 | $7.2917 | $12.1528 | — |
| attacompute.ai 0xd5e7…7be0 | failingupstream error · 12 h · stale | 51.22 | $13.70 | 41.07M | gated | $2.34 | $0.234 | $3.91 | −22% |
"Best price" and the seller table are live Antseed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the Antseed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on Antseed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.