relweave-4b-base
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84991
fare2-clip
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查看详情 ↗fare4-clip
chs20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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗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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查看详情 ↗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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查看详情 ↗tecoa2-clip
chs20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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查看详情 ↗tecoa4-clip
chs20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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查看详情 ↗> **On NVIDIA GPUs, use llama.cpp b11390 (4 October 2026) or newer.** Older > CUDA builds crash on prompts of certain lengths (230, 344 and 457 tokens > in our tests) with "CUDA error: an illegal memory access was encountered". > Other lengths work normally, and CPU-only builds are not affected. > Upstream fix: #29941. This is a GGUF conversion of `ISTA-DASLab/Qwen3.6-35B-A3B-2Bit-GSQ`. It is essentially the DASLab GSQ checkpoint repackaged into stock llama.cpp types, with the small conversion errors documented below. It is not a new quantization, fine-tune, or model release. It is distributed as a text-model GGUF plus a separate BF16/F32 vision-projector GGUF. The routed experts use Q2_0 (source: 2-bit GSQ), while attention, linear attention, shared-expert, and LM-head weights use Q8_0 (source: INT8). Remaining text-model tensors use BF16 or F32 (source: BF16) as required by llama.cpp's canonical `QWEN35MOE` layout. The companion projector retains the source BF16 vision tower as BF16 or exact F32 promotions. MTP weights remain omitted.
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查看详情 ↗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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查看详情 ↗chua-dl-models
chtk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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查看详情 ↗Llama-3.2-3B-Instruct-uncensored
chuanli11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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查看详情 ↗ML-Agents-SoccerTwos-New
chubbyk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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查看详情 ↗BiomedCLIP-vit-bert-hf
chuhac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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查看详情 ↗gemma-4-E4B-it-uncensored-vision
ChuHo1994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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗internlm2-chat-20b-ExPO
chujiezhengThe extrapolated (ExPO) model based on `internlm2-chat-20b` and `internlm/internlm2-chat-20b-sft`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.5)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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查看详情 ↗internlm2-chat-7b-ExPO
chujiezhengThe extrapolated (ExPO) model based on `internlm2-chat-7b` and `internlm/internlm2-chat-7b-sft`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.5)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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查看详情 ↗Llama3-70B-Chinese-Chat-ExPO
chujiezhengThe extrapolated (ExPO) model based on `shenzhi-wang/Llama3-70B-Chinese-Chat` and `meta-llama/Meta-Llama-3-70B-Instruct`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. **Note:** This is an experimental model, as I have not comprehensively evaluated its Chinese ability. **Unexpected issues may occur when we apply extrapolation to the DPO/RLHF alignment training for new languages (e.g., Chinese).**
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查看详情 ↗Llama3-8B-Chinese-Chat-ExPO
chujiezhengThe extrapolated (ExPO) model based on `shenzhi-wang/Llama3-8B-Chinese-Chat` and `meta-llama/Meta-Llama-3-8B-Instruct`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. **Note:** This is an experimental model, as I have not comprehensively evaluated its Chinese ability. **Unexpected issues may occur when we apply extrapolation to the DPO/RLHF alignment training for new languages (e.g., Chinese).**
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查看详情 ↗LLaMA3-iterative-DPO-final-ExPO
chujiezhengThe extrapolated (ExPO) model based on `RLHFlow/LLaMA3-iterative-DPO-final` and `RLHFlow/LLaMA3-SFT`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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查看详情 ↗Mistral7B-PairRM-SPPO-ExPO
chujiezhengThe extrapolated (ExPO) model based on `UCLA-AGI/Mistral7B-PairRM-SPPO` and `mistralai/Mistral-7B-Instruct-v0.2`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. This extrapolated model achieves the **35.4%** win rate and **31.8%** LC win rate on **AlpacaEval 2.0**, outperforming the original `Mistral7B-PairRM-SPPO`'s 32.2% and 30.5%, respectively.
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查看详情 ↗Smaug-34B-v0.1-ExPO
chujiezhengThe extrapolated (ExPO) model based on `abacusai/Smaug-34B-v0.1` and `jondurbin/bagel-34b-v0.2`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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查看详情 ↗Smaug-Llama-3-70B-Instruct-ExPO
chujiezhengThe extrapolated (ExPO) model based on `abacusai/Smaug-Llama-3-70B-Instruct` and `meta-llama/Meta-Llama-3-70B-Instruct`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.3)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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查看详情 ↗Starling-LM-7B-alpha-ExPO
chujiezhengThe extrapolated (ExPO) model based on `berkeley-nest/Starling-LM-7B-alpha` and `openchat/openchat_3.5`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.2)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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查看详情 ↗Starling-LM-7B-beta-ExPO
chujiezhengThe extrapolated (ExPO) model based on `Nexusflow/Starling-LM-7B-beta` and `openchat/openchat-3.5-0106`, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper. Specifically, we obtain this model by extrapolating **(alpha = 0.5)** from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. Evaluation results on the **AlpacaEval 2.0** benchmark (you can find the evaluation outputs on the official GitHub repo):
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
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
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
读懂模型，再做选择
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。