AI NEWS.ccAI is happening. The future is updating.
♡ Saved tools 0
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 22

Discover models worth your attention

The full NVFP4 build of the LTX-2.5 22B distilled transformer, stamped so ComfyUI actually loads it. Smallest working 22B on the board — **18.7GB, ~2.8GB lighter than the official comfy-int8** — and the same-seed output holds up frame for frame. Top row: text-to-video. Bottom row: **image-to-video** from a single keyframe — same start image, same seed, both quants. Native synced audio on all four (yes, the disco clips come with their own disco track). Lightricks ships an NVFP4 export of the 2.5 distilled DiT, but it's a raw TensorRT-ModelOpt export. Load it through ComfyUI's UNETLoader and you get:

Video generation
Input Text→ Output Video
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

**This is an independent NVFP4 quantization of Qwen Image 2.1. The Qwen team created the original model.** The original model and architecture are Qwen's work. I converted the official Comfy-Org/Qwen-Image-2.1 BF16 checkpoint package and packaged the quantized weights, workflows, and validation here. This is not a new base model or an official Qwen release. I converted Qwen Image 2.1's image transformer and Qwen3-VL 8B language encoder directly from the official BF16 weights into native ComfyUI NVFP4. The vision tower, embeddings, output head, critical image-model tensors, and VAE stay BF16. Real Blackwell FP4 kernels in both denoising and text conditioning, with an included small runtime patch for the latter. No new nodes. **RTX 5090: 6.746 s median for a complete 1024×1024, 40-step run** versus 15.629 s with BF16 and 7.553 s with official INT8. Three warm runs per variant, every node executed, including text encoding. This one-prompt benchmark used the encoder patch; full method and limits.

Image generation
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

Native ComfyUI weights, a selectively quantized image transformer and encoder, and seven ready-to-load workflows. Fashion, casual photography, editing, multiple references, typography, illustration, and transparent PNGs. **No custom nodes or core patches required.** All seven workflows were tested on unmodified ComfyUI 0.39.0 with custom nodes disabled. Update ComfyUI and its normal dependencies, then place these files in the matching folders: **15.70 GB total — 51.6% smaller than the original BF16 package.** These are file sizes. Model checksums are included.

Image generation
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

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 ↗
Source / docs ↗

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。

Basic profile indexed · More details to follow

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

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。

Basic profile indexed · More details to follow

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

V4 still rocks as the original Qwen3 prompt-engineer build. The new V6 release is the native Z-Image Turbo text-encoder build, rebuilt with SMART DoRA and trained locally for ComfyUI drop-in use plus LM Studio prompt enhancement. This is Z-Engineer V4, the culmination of extensive research into what makes an AI prompt engineer actually *good* at its job. Built on the Qwen 3 architecture and trained using a novel **SMART Training** methodology, this 4B parameter model doesn't just describe scenes—it understands the craft of visual storytelling down to the lens flare. Z-Engineer V4 is a fully fine-tuned (not LoRA, we went *all in*) version of the text encoder from Tongyi-MAI/Z-Image-Turbo. It's been specifically trained to understand the nuances of AI Image Generation workflows.

Text generation
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

This is the **abliterated** version of the Z-Image-Turbo text encoder. I took the **p-e-w heretic method** and hammered it through **1000 trials**, specifically targeting **BOTH** image generation refusals and those peskier general refusals. The result? - **KL Divergence:** A tiny `0.0004` (basically no lobotomy! 🧠✨) - **Refusal Rate:** Only `4/100` in my torture tests. Brought to you by the same chaotic good energy behind: - Z-Image-Engineer-V2.5 - The original Z-Image-Engineer

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

The main repo contains the merged HF safetensors. This repo contains the quant ladder for the ComfyUI-Z-Engineer node, LM Studio, ComfyUI `CLIPLoaderGGUF`, llama.cpp-style loaders, and local prompt-enhancement workflows. **Z-Image-Engineer V6** is a SMART DoRA fine-tuned 4B Qwen text encoder from `Tongyi-MAI/Z-Image-Turbo`. The comparison examples were generated from direct LM Studio user requests like this, with no separate system prompt. `V6_SYSTEM_PROMPT.md` is included only as an optional preset for people who want a stricter prompt-only chat setup.

Text generation
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗
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 ↗