AI NEWS · Model intelligenceExplore capabilities. Make informed choices. ↗Profiles indexed first · Details continuously enriched
THE AI MODEL DIRECTORY Start exploring models here

Find your ideal AI model.

From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.

Popular searches
84,991 model profiles13202 providers / publishers45 capability categories465 with comparable prices · 30 benchmarked
TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
DISCOVER & COMPARE

Explore AI models 9

Discover models worth your attention

> Zero refusal. Zero capability loss. First in the field. > > 0/842 refusals. 46/70 MMLU-Pro (stock parity). Full coherence. The first abliterated model to achieve **zero refusal with zero benchmark regression** versus stock weights. Built with a novel **2-pass surgery pipeline** developed by OBLITERATUS:

Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗

> *"The chains are broken. The mind is free."* > *"Also we fixed the part where half the brain was missing lmao"* Google built Gemma 4 with guardrails. We built OBLITERATUS to tear them off. They said their architecture was different. They were right — it broke every tool we threw at it. NaN activations, shared KV weights, thinking mode... Gemma 4 fought back harder than any model we've cracked. **0% hard refusal. Guardrails fully removed. 720 tensors intact. Runs on your phone.**

Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗

Abliterated version of Ornith-1.5-9B by OBLITERATUS. Safety alignment removed via precision abliteration surgery — the model responds to most prompts without refusal. The stock Ornith-1.5-9B refuses requests it considers harmful. This version removes that refusal behavior while preserving the model's coding, reasoning, and agentic capabilities. **Recipe:** Gentle 3-round SVD abliteration + per-head attention surgery (G3-HS) - Round 1: 5-direction SVD, reg 0.06, min_layer 0.30 - Round 2: 3-direction SVD, reg 0.04, min_layer 0.25 - Round 3: 3-direction SVD, reg 0.03, min_layer 0.20 - Finish: Per-head attention surgery (`--attention-head-surgery`), reg 0.02, min_layer 0.10 - All rounds used full 1000-prompt corpus with residue weighting

Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗

基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。

Category pending
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗

> A 27B Qwen cut loose by OBLITERATUS: 26.9B parameters, BF16 safetensors, > Q4/Q5/Q6/Q8 GGUFs, lower refusal, preserved capability, and receipts in the > open. > > The chains are cut. The capability stays. The receipts are brutal. A 26.9B Qwen3.6 checkpoint went into the OBLITERATUS chamber, got hit with source-tethered ASPA, then got pulled back toward the source model where the cut started threatening useful capability. The mission was simple: cut the refusal circuits, keep the 27B brain. Not a toy quant. Not a prompt wrapper. Not a refusal-cosplay fine-tune. This is weight-space liberation with capability checks attached, a full local-runtime ladder, and the refusal residue mapped instead of hand-waved.

Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗

> Genuinely uncensored. Real answers, not safety lectures. Near-stock capability. V3 applies iterative refinement on top of V2's complementary blend, with targeted corpus expansion. The result: **genuine liberation — not just removal of hard refusals but elimination of safety-lecture deflections.** **V3 highlights:** - **Genuinely answers restricted queries** — provides real substance instead of safety lectures - **20/20 on code generation tasks** — functional implementations, not disclaimers - **Thinking ON compatible** — no refusals in either thinking mode - **Honest scoring** — every response manually audited for real substance, not just absence of "I cannot" - **-2.1pp MMLU** — modest capability cost for genuine liberation

Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

Understand before you choose

Selection guide & FAQ
How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

AI NEWS · From information to decisions

Follow trends. Find tools. Explore models.

Explore AI news ↗Discover AI tools ↗