**Intern-Decision-0.8B** is a multimodal structured decision model fine-tuned from **Qwen3.5-0.8B**. It accepts a shared state, a schema of named questions, and optional images, and returns an answer distribution for every question in one model forward pass. 1. Preserve the question and option order, and map each question's options to single-token symbols `A`, `B`, …, `Z`, `a`, …, `z`, `0`, …, `9`. 2. Render the original system prompt, state, decision schema, and a complete assistant JSON skeleton with one ` ` placeholder per field. Preserve the checkpoint's chat template and empty thinking block. 3. Run one causal Hugging Face forward pass. For the masked-next-token decision objective, read logits at the position **immediately before each placeholder**. 4. Take a softmax over only that field's allowed candidate-symbol logits, then apply the checkpoint's probability calibration. 5. Map symbols back to the original option values and return typed JSON answers. This API performs structured candidate scoring. It does not call `generate()` or sample free-form text. A request can contain multiple fields; no gold answers are inserted into the prompt. The inference compiler uses only `state`, `questions`, and optional `images`.

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Intern-Decision-2B

internlm

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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Intern-Decision-4B

internlm

**Intern-Decision-4B** is a multimodal structured decision model fine-tuned from **Qwen3.5-4B**. It accepts a shared state, a schema of named questions, and optional images, and returns an answer distribution for every question in one model forward pass. 1. Preserve the question and option order, and map each question's options to single-token symbols `A`, `B`, …, `Z`, `a`, …, `z`, `0`, …, `9`. 2. Render the original system prompt, state, decision schema, and a complete assistant JSON skeleton with one ` ` placeholder per field. Preserve the checkpoint's chat template and empty thinking block. 3. Run one causal Hugging Face forward pass. For the masked-next-token decision objective, read logits at the position **immediately before each placeholder**. 4. Take a softmax over only that field's allowed candidate-symbol logits, then apply the checkpoint's probability calibration. 5. Map symbols back to the original option values and return typed JSON answers. This API performs structured candidate scoring. It does not call `generate()` or sample free-form text. A request can contain multiple fields; no gold answers are inserted into the prompt. The inference compiler uses only `state`, `questions`, and optional `images`.

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Intern-MemDec-4B

internlm

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

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。

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。

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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。