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 5

Discover models worth your attention

The repository contains Qwen3.6-27B converted to the native NInfer `.ninfer` artifact format. The artifact is intended only for NInfer; it is not a Transformers checkpoint, Safetensors distribution, or GGUF file. The file contains Text, Vision, MTP, the optimized proposal head, and frontend resources. Text projections use Q4/Q5, vocabulary weights use Q6, and MTP projections use Q8. Vision and speculative weights are loaded only when selected at startup. NInfer does not provide an install target or packaged binary. See the repository README for source-build dependencies.

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

This model card is the version-controlled source for neroued/Qwen3.6-35B-A3B-NInfer. The repository contains Qwen3.6-35B-A3B converted to the native NInfer `.ninfer` artifact format. The artifact is intended only for NInfer; it is not a Transformers checkpoint, Safetensors distribution, or GGUF file. Its optional DFlash companion weights come from z-lab/Qwen3.6-35B-A3B-DFlash. The file contains Text, Vision, MTP, DFlash, the optimized proposal head and frontend resources. Routed experts use Q4 gate/up and Q5/Q6 down weights; shared experts and mixer projections use Q8. Vision and speculative weights are loaded only when selected at startup.

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

This model card is the version-controlled source for neroued/Qwen3.8-27B-NInfer. The repository contains Qwen3.8-27B converted to the native NInfer `.ninfer` artifact format. The artifact is intended only for NInfer; it is not a Transformers checkpoint, Safetensors distribution, or GGUF file. The Text body uses Q4/Q5 projections, while the token embedding and full output head use `q8_g32_fp16`. The file also contains Vision, MTP, DFlash2, the optimized proposal head and frontend resources. Vision and speculative weights are loaded only when selected at startup.

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

This model card is the version-controlled source for neroued/Qwen3.8-27B-nvfp4-NInfer. The repository contains a mixed NVFP4/FP8 representation of Qwen3.8-27B. It combines the official BF16 checkpoint with the fixed packed Text weights from unsloth/Qwen3.8-27B-NVFP4 in the native NInfer `.ninfer` artifact format. The artifact is intended only for NInfer; it is not a Transformers checkpoint, Safetensors distribution, or GGUF file. The artifact uses the Qwen3.5 Dense architecture. Text layers 0–55 use NVFP4 MLP weights, while the token embedding, attention input/output projections, GDN Q/K/V/Z and output projections, full output head, and Text layers 56–63 MLP weights use row-scaled FP8. Control weights use BF16, with separate MTP, Vision and DFlash2 weights.

Input Text · Image→ 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 ↗