> [!IMPORTANT] > The uncensored GGUFs are now available! > If you encounter any issues or would like to share your generated images, feel free to post them in the Community section. GGUF quantizations of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository.

Image generation

Input Text→ Output Image

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Qwen3-TTS-GGUF

ldov

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。

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Speech synthesis

Input Text→ Output Audio

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Ternary-Bonsai-2-27B-Uncensored-Heretic-GGUF

ldov

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。

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Text generation

Input Text→ Output Text

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bertha-portuguese-small

Le0ssa

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。

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Fill-mask

Input Text→ Output Text

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Rearank-7B

le723z

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。

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Category pending

Input Not verified→ Output Not verified

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lea1

leaaihubi

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。

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Category pending

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lea2

leaaihubi

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Category pending

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lea4

leaaihubi

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Category pending

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KAT-Coder-V2.5-Dev-AutoRound-W4A16-Tuning

LeaderboardModel1

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。

Text generation

Input Text→ Output Text

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zeta-2.1-autoround-W4A16

LeaderboardModel1

This model is a int4 weight-only quantization with group_size 128 and symmetric quantization of zed-industries/zeta-2.1 generated by AutoRound. Please follow the license of the original model. If you encounter any issues, feel free to open an issue on the AutoRound GitHub repo or provide feedback on the Low-Bit Open LLM Leaderboard. The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing.

Text generation

Input Text→ Output Text

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Qwen3.5-35B-A3B-heretic-GGUF

LeadFootThrottleCock

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Category pending

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Qwen3-VL-8B-Thinking-heretic-GGUF

LeadFootThrottleCock

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Visual QA

Input Text · Image→ Output Text

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COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 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.

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