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AI NEWS · 模型情报站发现能力边界，让每一次选择更有依据 ↗基础档案先收录 · 缺失资料持续补齐
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84,991 款模型档案13202 家厂商 / 发布者45 个能力分类465 款有可比报价 · 30 款有公开评测
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探索 AI 模型 4

为你发现值得关注的模型

JevK5

alibiserikbay

JevK5 is an independent, Apache-2.0 open-source alternative to TypeSafe's Jev for typed decisions. It reads a state and a yes/no (`noul`), choice, or score question and returns a **probability for every option in one forward pass, with zero generated tokens**. The open weights can be self-hosted using the JevK5 runtime, which serves a TypeSafe-style `/v1/systemone` endpoint. This is not Jev's model or architecture and is not affiliated with TypeSafe AI. Use the JevK5 runtime shown below to read option probabilities. Generic text-generation examples on the Hub call `generate()` and do not perform JevK5's decision readout. - **Base:** Qwen3.5-4B, with a LoRA (rank 16, attention projections) merged into the weights - **Readout:** SemIf's protocol (TheoLeeCJ/SemIf, MIT): a softmax over the answer letters' next-token logits, divided by one calibration temperature. Questions with more than 16 options are read in several passes and combined with a second temperature. Both are in `jevk5_config.json`: `temperature` 1.22 and `knockout_temperature` 0.93 - **Runtime:** github.com/allebee/jevk5, 0.3.0 or later. One CUDA graph per padded input length: ~13 ms per decision on an H100 - **License:** Apache-2.0 - **Previous version:** v0.2 stays available under the Hub tag `v0.2` (see Use)

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文本生成
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
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来源 / 文档 ↗

JevK5-2B

alibiserikbay

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

文本生成
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
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来源 / 文档 ↗

JevK5-GGUF

alibiserikbay

GGUF builds of JevK5 (4B), JevK5-9B and JevK5-2B for llama.cpp, which runs on NVIDIA, AMD, Intel and Apple GPUs and on plain CPUs. JevK5 is an Apache-2.0 open alternative to TypeSafe's Jev. It reads a document and a yes/no, choice, or score question and returns **a probability for every option in one forward pass, with nothing generated**. It is not affiliated with TypeSafe AI. Every file was checked against its unquantized model on JevBench's 231 public decisions, scored the same way: how often it gives the same answer, and its accuracy per tier. We publish a file only if at least 95% of its answers match. **Each model has its own temperatures; pass them to the client as shown under "Run it".**

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分类待补充
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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

读懂模型，再做选择

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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