StartLux-Decision-v1-0.8B
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探索 AI 模型 37
StartLux-Decision-v1-0.8B-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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查看详情 ↗StartLux-Decision-v1-0.8B-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。
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查看详情 ↗StartLux-Decision-v1-0.8B-Q8_0-GGUF
startlux-modelsStartLux-Decision-v1-0.8B 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. At this size Q4_K_M changes noticeably more answers (93.1% the same), so Q8_0 is the better choice. `-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-v1.1-0.8B-Instant
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-v1.1-0.8B-Instant-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-v1.1-27B-AutoThink
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-v1.1-27B-AutoThink-GGUF
startlux-modelsFormats: BF16 · **GGUF** · MLX 8-bit · MLX 4-bit · Code: StartLuxLabs/StartLux-Decision · Paper: arXiv 2610.17309 Decision Index 0.3, public subset, as of 11 October 2026: the StartLux-Decision Instant models against other decision models of each size. Figure 1 of the technical report . The DecisionPivot column is the mean chance-corrected accuracy over the seven layers of the benchmark's scorer on its 7,800 core items, from the report's measurements: the GGUF files on llama.cpp (release b11549), the BF16 weights on vLLM. For an AutoThink model the rows therefore also compare two engines. The Q8_0 file scored 2.7 points above BF16 (95% interval +1.3 to +4.1), but as the two runs used different engines, the difference does not isolate the format. Q8_0 is the recommended file; Q4_K_M lost up to 3.8 points across the sizes.
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查看详情 ↗StartLux-Decision-v1.1-27B-Instant
startlux-modelsStartLux-Decision-v1.1-27B-Instant is a 27B decision model. You give it a state (text or JSON, with up to 8 images) and typed questions about it: pick one of several options (`choice`), yes or no (`noul`), or a level on a scale of 2 to 10 (`score`). It returns a probability for every option of every question from one forward pass, without generating text. The request and response format is compatible with the System One `/v1/systemone` API. Formats: **BF16** · GGUF · MLX 8-bit · MLX 4-bit · Code: StartLuxLabs/StartLux-Decision · Paper: arXiv 2610.17309 Family: 0.8B Instant · 2B Instant · 4B Instant · 4B AutoThink · 9B Instant · 9B AutoThink · **27B Instant** · 27B AutoThink
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查看详情 ↗StartLux-Decision-v1.1-27B-Instant-GGUF
startlux-modelsFormats: BF16 · **GGUF** · Code: StartLuxLabs/StartLux-Decision · Paper: arXiv 2610.17309 Decision Index 0.3, public subset, as of 11 October 2026: the StartLux-Decision Instant models against other decision models of each size. Figure 1 of the technical report . The DecisionPivot column is the mean chance-corrected accuracy over the seven layers of the benchmark's scorer on its 7,800 core items, from the report's measurements: the GGUF files on llama.cpp (release b11549), the BF16 weights on vLLM. Q8_0 is the recommended file; Q4_K_M lost up to 3.8 points across the sizes.
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查看详情 ↗StartLux-Decision-v1.1-2B-Instant
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-v1.1-2B-Instant-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-v1.1-4B-AutoThink
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-v1.1-4B-AutoThink-GGUF
startlux-modelsStartLux-Decision-v1.1-4B-AutoThink as GGUF files for llama.cpp, in Q8_0 (8-bit) and Q4_K_M (4-bit). StartLux-Decision-v1.1-4B-AutoThink is a 4B decision model that thinks only where its one-pass answer is uncertain. Given a state (text or JSON) and typed questions about it (pick an option, yes or no, or a level on a scale of 2 to 10), it reads a probability for every option from the option letters in one pass; where the top probability is below 0.90 it writes a thought of at most 4,096 tokens and reads the answer again, and the default answer is 0.25 × the first read-out + 0.75 × the second. Requests and responses use the TypeSafe `/v1/systemone` format. Formats: BF16 · **GGUF** · MLX 8-bit · MLX 4-bit · Code: StartLuxLabs/StartLux-Decision · Paper: arXiv 2610.17309 Decision Index 0.3, public subset, as of 11 October 2026: the StartLux-Decision Instant models against other decision models of each size. Figure 1 of the technical report .
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查看详情 ↗StartLux-Decision-v1.1-4B-Instant
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-v1.1-4B-Instant-GGUF
startlux-modelsFormats: BF16 · **GGUF** · MLX 8-bit · MLX 4-bit · Code: StartLuxLabs/StartLux-Decision · Paper: arXiv 2610.17309 Decision Index 0.3, public subset, as of 11 October 2026: the StartLux-Decision Instant models against other decision models of each size. Figure 1 of the technical report . The DecisionPivot column is the mean chance-corrected accuracy over the seven layers of the benchmark's scorer on its 7,800 core items, from the report's measurements: the GGUF files on llama.cpp (release b11549), the BF16 weights on vLLM. Q8_0 is the recommended file; Q4_K_M lost up to 3.8 points across the sizes.
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查看详情 ↗StartLux-Decision-v1.1-9B-AutoThink
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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗StartLux-Decision-v1.1-9B-AutoThink-GGUF
startlux-modelsStartLux-Decision-v1.1-9B-AutoThink as GGUF files for llama.cpp, in Q8_0 (8-bit) and Q4_K_M (4-bit). StartLux-Decision-v1.1-9B-AutoThink is a 9B decision model that thinks only where its one-pass answer is uncertain. Given a state (text or JSON) and typed questions about it (pick an option, yes or no, or a level on a scale of 2 to 10), it reads a probability for every option from the option letters in one pass; where the top probability is below 0.90 it writes a thought of at most 4,096 tokens and reads the answer again, and the default answer is 0.25 × the first read-out + 0.75 × the second. Requests and responses use the TypeSafe `/v1/systemone` format. Formats: BF16 · **GGUF** · MLX 8-bit · MLX 4-bit · Code: StartLuxLabs/StartLux-Decision · Paper: arXiv 2610.17309 Decision Index 0.3, public subset, as of 11 October 2026: the StartLux-Decision Instant models against other decision models of each size. Figure 1 of the technical report .
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查看详情 ↗StartLux-Decision-v1.1-9B-Instant
startlux-modelsStartLux-Decision-v1.1-9B-Instant is a 9B decision model. You give it a state (text or JSON, with up to 8 images) and typed questions about it: pick one of several options (`choice`), yes or no (`noul`), or a level on a scale of 2 to 10 (`score`). It returns a probability for every option of every question from one forward pass, without generating text. The request and response format is compatible with the System One `/v1/systemone` API. Formats: **BF16** · GGUF · MLX 8-bit · MLX 4-bit · Code: StartLuxLabs/StartLux-Decision · Paper: arXiv 2610.17309 Family: 0.8B Instant · 2B Instant · 4B Instant · 4B AutoThink · **9B Instant** · 9B AutoThink · 27B Instant · 27B AutoThink
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查看详情 ↗StartLux-Decision-v1.1-9B-Instant-GGUF
startlux-modelsStartLux-Decision-v1.1-9B-Instant as GGUF files for llama.cpp, in Q8_0 (8-bit) and Q4_K_M (4-bit). StartLux-Decision-v1.1-9B-Instant is a 9B decision model. Given a state (text or JSON) and typed questions about it (pick an option, yes or no, or a level on a scale of 2 to 10), it returns a probability for every option of every question from one forward pass, without generating text. Requests and responses use the TypeSafe `/v1/systemone` format. Formats: BF16 · **GGUF** · MLX 8-bit · MLX 4-bit · Code: StartLuxLabs/StartLux-Decision · Paper: arXiv 2610.17309 Decision Index 0.3, public subset, as of 11 October 2026: the StartLux-Decision Instant models against other decision models of each size. Figure 1 of the technical report .
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查看详情 ↗StartLux-Decision-v1-27B
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-v1-27B-BF16-GGUF
startlux-modelsStartLux-Decision-v1-27B in BF16 (16-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-v1-27B-Q4_K_M-GGUF
startlux-modelsStartLux-Decision-v1-27B 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-v1-27B-Q8_0-GGUF
startlux-modelsStartLux-Decision-v1-27B 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 ↗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。