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Fara1.5-9B is Microsoft's computer-use agent for web browsing (Qwen3.5-9B base, multimodal: it reads browser screenshots and emits structured tool calls). This repository holds **three GGUF files of different size** made from the BF16 weights with our measured mixed-codec method. Pick one on the *Files* page; the table below says what each file is. Independent community quantization, **not** an official Microsoft release. Part of the **Sakura Mini** line. Same method, same texts, same machine for every file; lower KL divergence means closer to the BF16 original: Why: the bit budget per tensor is allocated from measured sensitivity (see *How it was made*), not from fixed rules. Full table below.

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**Built with FLUX.** Community quantization of FLUX.3 Image Klein (distilled, 16 steps, no CFG) with the official, unmodified Qwen3-VL-4B-Instruct text encoder. Independent work, not made, reviewed or endorsed by Black Forest Labs. The image model files, the VAE and the workflow are under the **FLUX Kommunity License** (copy in `LICENSE.md`: non-commercial use, or commercial use of outputs only as a Qualifying User under US$5 million revenue with the filtering or review the license requires); the unmodified text encoder is Apache-2.0 (`LICENSE-Apache-2.0.txt`). See `NOTICE`. An uncensored version of the **text encoder** (only the encoder, the image model is not changed) is available separately: Sakura-FLUX.3-Image-Klein-TextEncoder-Uncensored-GGUF. It works with the image model files of this repository. Q8_0 image model and the official text encoder, ComfyUI 0.41.1 with ComfyUI-GGUF (Radeon 8060S), 1024x1024, 16 steps, CFG 1.0, `euler`/`simple`. These are the **same nine prompts and seeds** (1202, 1302, 1305, 1306, 1105, 1103, 1503, 1506, 1406 in the order shown) as in the repositories Sakura-Qwen-Image-2.1-Turbo-GGUF and Sakura-Iris-3B-GGUF, so that the models can be compared. One seed per prompt, no re-rolls, all nine images are shown; the prompts were chosen for those repositories from a larger set, not for FLUX. The prompts are the captions of the images above. The same prompts and seeds with our uncensored text encoder are in Sakura-FLUX.3-Image-Klein-TextEncoder-Uncensored-GGUF. The FLUX images were made with ComfyUI, the Qwen and Iris images with stable-diffusion.cpp, and models, steps and samplers differ, so differences between the sets are not only differences of the text encoder. Everything except the runtime is in this repository. Pick the column of the program you use (🟦 stable-diffusion.cpp, 🟧 ComfyUI):

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**Only the prompt upsampler is edited.** black-forest-labs/flux-3-image-klein-pup is a 9B vision-language model (fine-tuned from Qwen3.5-9B by Black Forest Labs) that expands a short image request into the detailed caption the FLUX.3 image model works best with. This repository has it in a version whose **language weights we edited with an abliteration run** so that it refuses less, as GGUF with importance-matrix quantizations. The image model and its text encoder are **not** part of this repository: see Sakura-FLUX.3-Image-Klein-GGUF (image model, official text encoder) and Sakura-FLUX.3-Image-Klein-TextEncoder-Uncensored-GGUF (uncensored text encoder). For adults only; you are responsible for what you generate and for the laws that apply to you. Independent work, not made or endorsed by Black Forest Labs. Apache-2.0 (copy in `LICENSE`). **The upsampler is optional.** FLUX.3 Klein takes any prompt directly; all example images in the FLUX repositories were made without an upsampler. We did not measure whether the upsampler improves images. Memory: with Q8_0 about 8.9 GiB of weights (plus 0.9 GiB for the projector and the context); we did not measure peak memory.

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COST EXPLORER

好模型,也要算好成本。

费用分析

调整月度 Token 用量,直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照;不计缓存优惠、阶梯价、推理额外输出与税费,请以来源的当前报价为准。

MODEL HANDBOOK

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选型与常见问题
API 价格应该怎么比较?+

先区分输入、输出和缓存价格,再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费,不能直接与 Token 单价比较。

开放权重,就代表可以免费商用吗?+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一,就是最适合我的模型吗?+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本,再选择模型。

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