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探索 AI 模型 3

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为你发现值得关注的模型

Wald-26B-A4B

org2ai

An open decision model built on Gemma 4 26B-A4B, a mixture of experts with 26B parameters and about 4B active per token. You send a state and typed options, and it returns a probability for every option. It is served through a Jev-compatible `POST /v1/systemone` API on your own GPU. - **Probabilities, not text:** you get a probability for every option and have nothing to parse. - **Decision Index 0.3: 62.30**, complete public suite, one pass (our run with the official 0.3 kit). Jev 1.13 scores 57.96. Auto scores ≈ 65.9 (estimate from a 14 % sample; Auto exceeds the 1,000 ms admission latency, so one pass is the default). - **JevBench-XL TEST: 75.3 %** (Jev 1.13: 67.85 %). - **JevBench public set: 219 / 231** (macro 95.3 %). Jev 1.13 gets 200 / 231. - **Optional thinking:** an Auto mode can use Gemma 4's native thinking when the top probability is below 0.7; the default is one pass. - **Fast:** median **53 ms** in one pass, measured serially with one request in flight on a warm server with CUDA graphs. This is processing time on one RTX PRO 6000, over 200 Decision Index 0.3 requests. - **Open:** Apache-2.0, BF16 weights (52 GB), served on one 96 GB GPU. 62.30 is our run of the complete public suite (140,620 requests, all answered) with the official 0.3 kit, in one pass, the default setting.

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Wald-4B

org2ai

An open 4B decision model: send a state and typed options, get a calibrated probability for every option. Served through a Jev-compatible `POST /v1/systemone` API on your own GPU. - **Probabilities, not text:** a calibrated probability for every option, nothing to parse. - **Decision Index 0.3: 60.05** on the complete public suite (our run; v2.0: 54.18). - **Thinks only when unsure:** Auto 0.7 thinks natively on about half of the requests. - **Fast:** median **158 ms** (serial, one request in flight, warm, processing time, one RTX PRO 6000, 200 DI 0.3 requests). - **Web actions:** Mind2Web element selection **92.3 %** (test splits, 6 candidates; v2.0: 84.6 %). - **Reads images and video** zero-shot through Qwen3.5-4B's unchanged vision tower. Video (wald-serve 0.2.2): **87.4 %** on VisualDecisionBench video (7,143 questions, our run), 2.5 s for a 15-second clip with 24 questions (serial, one RTX 5090). - **Small and open:** 4B, Apache-2.0. Self-run, not leaderboard results; how each number was produced: evaluation/v2.1/. DI 0.3 run files: org2ai/Wald-Q4B-decision-index-results.

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Wald-4B-GGUF

org2ai

GGUF files of Wald-Q4B v2.1 (tag `v2.1`) for llama.cpp: an open 4B decision model that returns a calibrated probability for every option. - One pass, text only: the bundled server reads a probability for every option from the model's option-letter logits (Qwen chat template), calibrated with the same frozen temperature table as the BF16 release. - `--effort auto` (Auto 0.7) also runs here but has not been parity-checked on GGUF; for images, use the BF16 release. - On a 1,000-request Decision Index sample (5,922 questions), Q8_0 gives the BF16 answer on 99.5 % of questions and Q4_K_M on 97.3 %. All numbers: evaluation/v2.1-gguf-parity.json. - v2 (`Wald-4B-v2-*.gguf`, `v2/`): Wald-Q4B v2, unchanged in this repository. - v1.2 (`Wald-4B-v1.2-*.gguf`, `server/`): unchanged in this repository.

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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。

每月输入 Token 10 M每月输出 Token 2 M1 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。

找到适合你的 AI 模型。

探索 AI 模型 3

Wald-26B-A4B

Wald-4B

Wald-4B-GGUF

暂未找到匹配模型

好模型，也要算好成本。

读懂模型，再做选择

资讯看趋势，工具提效率，模型筑可能。