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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 16
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 ↗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 ↗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 ↗gliguard-LLMGuardrails-300M
fastinoGLiGuard is a compact, encoder-based guardrail model for LLM safety moderation built on the `GLiNER2` interface. Instead of generating moderation verdicts autoregressively, it treats safety as structured classification: you provide task names and candidate labels at inference time, and the model scores all requested moderation tasks in a single bidirectional encoder pass. This model is CPU-first and can be used for effective protection against prompt hack injection, jailbreaking, and identifying harmful content. *GLiGuard’s accuracy remains competitive with guardrail models that are 23 to 90 times its size while running up to 16 times faster with 17 times lower latency.* The released checkpoint, `fastino/gliguard-LLMGuardrails-300M`, is a 0.3B-parameter model designed for fast local inference.
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查看详情 ↗gliner2.5-base-v1
fastino> *Extract entities, classify text, parse structured records, score span attributes, and extract relations — all in one boundary architecture.* GLiNER2.5 Base is the English boundary checkpoint at DeBERTa-v3-base scale. It is the everyday English model for schema-driven NER, classification, records, and relations. Load it with `AutoExtractor`: the checkpoint's `architecture` field selects `BoundaryExtractor` automatically. Fine-tune via Fastino. Join discussions on Reddit.
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查看详情 ↗GLiNER2.5-Decide
fastino**The 340M English classification model in the GLiNER2.5 family.** Pass any label set at call time: intent, routing, sentiment, priority, policy, and multi-label tags, in a single forward pass. No prompt template. No generated tokens. Load it with `AutoExtractor` and ship it locally. A single call can score several heads at once. Single-label tasks return one string. Multi-label tasks return every label above the threshold. Exact-match accuracy on `fastino/fast-decisions`: 17 domains, 300 held-out examples each, with the same text and candidate labels for every model.
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查看详情 ↗GLiNER2.5-Decide-1B
fastino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 ↗GLiNER2.5-multi-Decide
fastino**The 287M multilingual classification model in the GLiNER2.5 family.** Pass any label set at call time: intent, routing, sentiment, priority, policy, and multi-label tags, in a single forward pass. No prompt template. No generated tokens. Load it with `AutoExtractor` and ship it locally. Use it when the text is not English. A single call can score several heads at once. Single-label tasks return one string. Multi-label tasks return every label above the threshold. Exact-match accuracy on `fastino/fast-decisions`: 17 domains, 300 held-out examples each, with the same text and candidate labels for every model.
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查看详情 ↗gliner2.5-multi-v1
fastino> *Extract entities, classify text, parse structured records, score span attributes, and extract relations — all in one boundary architecture.* GLiNER2.5 Multi is the multilingual boundary checkpoint. It is built on mDeBERTa-v3-base and is the default choice when you need entities, classification, records, and relations in one model across languages. Load it with `AutoExtractor`: the checkpoint's `architecture` field selects `BoundaryExtractor` automatically. Fine-tune via Fastino. Join discussions on Reddit.
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查看详情 ↗gliner2.5-small-v1
fastino> *Extract entities, classify text, parse structured records, score span attributes, and extract relations — all in one boundary architecture.* GLiNER2.5 Small is the compact English boundary checkpoint. It keeps the same public API as the larger 2.5 models while running faster on CPU and edge-style deployments. Load it with `AutoExtractor`: the checkpoint's `architecture` field selects `BoundaryExtractor` automatically. Fine-tune via Fastino. Join discussions Reddit.
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查看详情 ↗gliner2-base-v1
fastino> *Extract entities, classify text, parse structured data, and extract relations—all in one efficient model.* GLiNER2 unifies **Named Entity Recognition**, **Text Classification**, **Structured Data Extraction**, and **Relation Extraction** into a single 205M parameter model. It provides efficient CPU-based inference without requiring complex pipelines or external API dependencies. Fine-tune via Fastino. Join discussions on Discord and Reddit.
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查看详情 ↗GLiNER2-Guardrails-PII-Multi
fastino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 ↗gliner2-large-v1
fastino> *Extract entities, classify text, parse structured data, and extract relations—all in one efficient model.* GLiNER2 unifies **Named Entity Recognition**, **Text Classification**, **Structured Data Extraction**, and **Relation Extraction** into a single 205M parameter model. It provides efficient CPU-based inference without requiring complex pipelines or external API dependencies. Fine-tune via Fastino. Join discussions on Discord and Reddit.
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查看详情 ↗gliner2-multi-v1
fastino> *Extract entities, classify text, parse structured data, and extract relations—all in one efficient model.* GLiNER2 unifies **Named Entity Recognition**, **Text Classification**, **Structured Data Extraction**, and **Relation Extraction** into a single 205M parameter model. It provides efficient CPU-based inference without requiring complex pipelines or external API dependencies. Fine-tune via Fastino. Join discussions on Discord and Reddit.
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查看详情 ↗**GLiNER2-PII** is a fine-tune of the GLiNER2 model (205M parameters) for detecting and masking personally identifiable information across **42 entity types** and **7 languages**. Trained entirely on a constraint-driven synthetic corpus of 4,910 annotated texts, it achieves the **highest span-level F1 (0.477)** on the SPY benchmark among four compared systems — including OpenAI Privacy Filter, NVIDIA GLiNER-PII, and urchade/gliner\_multi\_pii-v1. 📄 **Technical Report** 🔗 **GitHub**
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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。