StartLux-Decision-v1-2B
startlux-models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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探索 AI 模型 37
StartLux-Decision-v1-2B-BF16-GGUF
startlux-models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。
基础资料已收录 · 缺失资料后续补充
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查看详情 ↗StartLux-Decision-v1-2B-Q4_K_M-GGUF
startlux-models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。
基础资料已收录 · 缺失资料后续补充
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查看详情 ↗StartLux-Decision-v1-2B-Q8_0-GGUF
startlux-models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 ↗StartLux-Decision-v1-35B-A3B
startlux-modelsStartLux-Decision-v1-35B-A3B answers typed questions about a state: pick one of several options, yes or no, or a rating on a scale. The state can be text, JSON or images, up to 262,144 tokens (256K). Every question comes back with a probability for each option. Requests and responses use the TypeSafe `/v1/systemone` format, so clients written for Jev work unchanged. It is a mixture of experts: about 35B parameters, of which about 3B are active for each token. It answers in half the time of StartLux-Decision-v1-27B, has the highest JevBench and Intern-Decision numbers of the family, and is 2.3 points below the 27B on Decision Index 0.2.1. JevBench public counts the correct answers on the 231 public items in the Intern-Decision bundle; Intern avg is the average accuracy over its seven suites; DI is the Decision Index under both editions, the public board's values for the other systems. A blank cell means the number is not published. StartLux-Decision latencies are for one H200, with the three questions answered in one forward pass. ¹ Intern-Decision's own measurement on an RTX 4090. ² The server time the TypeSafe API gateway reports for the same request, mean of 100, network left out as in ours; Intern-Decision reports 109.7 ms end to end.
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查看详情 ↗StartLux-Decision-v1-4B
startlux-models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 ↗StartLux-Decision-v1-4B-BF16-GGUF
startlux-models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 ↗StartLux-Decision-v1-4B-Q4_K_M-GGUF
startlux-modelsStartLux-Decision-v1-4B in Q4_K_M (4-bit) as a GGUF file for llama.cpp. The decision server in this repository turns it into the same typed decisions, with a probability for every option, as the original model. "Same decision" is the share of the 231 public JevBench items on which the file picks the same answer as the original weights run through the `startlux_decision` package, with the same prompts, readout and temperatures. Q8_0 and Q4_K_M are llama.cpp's standard quantizations of BF16, without an importance matrix. `-ngl 99` puts every layer on the GPU (CUDA or Metal); leave it out on a CPU-only machine. llama.cpp has to be recent enough for this model (build b10454 or newer).
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查看详情 ↗StartLux-Decision-v1-4B-Q8_0-GGUF
startlux-modelsStartLux-Decision-v1-4B in Q8_0 (8-bit) as a GGUF file for llama.cpp. The decision server in this repository turns it into the same typed decisions, with a probability for every option, as the original model. "Same decision" is the share of the 231 public JevBench items on which the file picks the same answer as the original weights run through the `startlux_decision` package, with the same prompts, readout and temperatures. Q8_0 and Q4_K_M are llama.cpp's standard quantizations of BF16, without an importance matrix. `-ngl 99` puts every layer on the GPU (CUDA or Metal); leave it out on a CPU-only machine. llama.cpp has to be recent enough for this model (build b10454 or newer).
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查看详情 ↗StartLux-Decision-v1-9B
startlux-models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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗StartLux-Decision-v1-9B-BF16-GGUF
startlux-models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。
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗StartLux-Decision-v1-9B-Q4_K_M-GGUF
startlux-modelsStartLux-Decision-v1-9B in Q4_K_M (4-bit) as a GGUF file for llama.cpp. The decision server in this repository turns it into the same typed decisions, with a probability for every option, as the original model. "Same decision" is the share of the 231 public JevBench items on which the file picks the same answer as the original weights run through the `startlux_decision` package, with the same prompts, readout and temperatures. Q8_0 and Q4_K_M are llama.cpp's standard quantizations of BF16, without an importance matrix. `-ngl 99` puts every layer on the GPU (CUDA or Metal); leave it out on a CPU-only machine. llama.cpp has to be recent enough for this model (build b10454 or newer).
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查看详情 ↗StartLux-Decision-v1-9B-Q8_0-GGUF
startlux-models"Same decision" is the share of the 231 public JevBench items on which the file picks the same answer as the original weights run through the `startlux_decision` package, with the same prompts, readout and temperatures. Q8_0 and Q4_K_M are llama.cpp's standard quantizations of BF16, without an importance matrix. `-ngl 99` puts every layer on the GPU (CUDA or Metal); leave it out on a CPU-only machine. llama.cpp has to be recent enough for this model (build b10454 or newer). Plain chat with a GGUF file does not give these decisions: the option-letter readout and the per-type temperatures live in `gguf_server.py`, not in the weights. The file holds the text decoder only.
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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。