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**Fanar-1-9B-Instruct** is a powerful Arabic-English LLM developed by Qatar Computing Research Institute (QCRI) at Hamad Bin Khalifa University (HBKU), a member of Qatar Foundation for Education, Science, and Community Development. It is the instruction-tuned version of Fanar-1-9B. We continually pretrain the `google/gemma-2-9b` model on 1T Arabic and English tokens. We pay particular attention to the richness of the Arabic language by supporting Modern Standard Arabic (MSA) and a diverse set of Arabic dialects, including Gulf, Levantine, and Egyptian. Fanar models, through meticulous curation of the pretraining and instruction-tuning data, are aligned with Islamic values and Arab cultures. **Fanar-1-9B-Instruct** is a core component of the Fanar GenAI platform that offers a suite of capabilities including image generation, video and image understanding, deep thinking, advanced text-to-speech (TTS) and automatic-speech-recognition (ASR), attribution and fact-checking, Islamic RAG, among several other features. We have published a comprehensive report with all the details regarding our Fanar GenAI platform. We also provide an API to our models and the GenAI platform (request access here).

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**Fanar-2-27B-Instruct** is an advanced Arabic-English LLM developed by Qatar Computing Research Institute (QCRI) at Hamad Bin Khalifa University (HBKU), a member of Qatar Foundation for Education, Science, and Community Development. It is part of the **Fanar 2.0 release**, a comprehensive Arabic-centric multimodal generative AI platform that includes specialized models for image generation, image understanding, and poetry generation. Building on the success of Fanar 1.0, we continually pretrain the `google/gemma-3-27b-pt` model on ~166B Arabic and English tokens using a novel three-recipe training approach with model merging. Highlighting the richness of the Arabic language, we support Modern Standard Arabic (MSA) and a diverse set of Arabic dialects, including Gulf, Levantine, and Egyptian. Fanar models, through meticulous curation of the pretraining and post-training data, are aligned with Islamic values and Arabic culture. **Fanar-2-27B-Instruct** introduces several breakthrough capabilities including native Arabic reasoning traces, selective thinking mode, tool calling, and advanced hallucination mitigation—making it the most capable Arabic-English language model in the Fanar family.

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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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