moonshine-base-gguf
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探索 AI 模型 75
moonshine-base-ja-gguf
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查看详情 ↗moonshine-base-uk-gguf
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查看详情 ↗moonshine-base-vi-gguf
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查看详情 ↗moonshine-base-zh-gguf
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查看详情 ↗moonshine-streaming-medium-gguf
handy-computerPorted from upstream commit 57b8436, pinned 2026-05-06. Validated against the HF Transformers v5.7.0 reference at transcribe.cpp commit 0d312ce on 2026-05-06. English speech-to-text in both one-shot and streaming modes. An encoder-decoder ASR model designed for streaming use (ergodic encoder + sliding-window attention, 50 Hz time-domain frontend). Same family as moonshine-streaming-tiny and moonshine-streaming-small; deepest of the three (14 / 14 layers) and widest hidden dims (encoder 768 / decoder 640). Takes a 16 kHz mono WAV and produces a transcript. No translation, no multilingual capability, no timestamps. WER on the full LibriSpeech test-clean split (2,620 utterances). Figures without a commit were published before provenance was recorded.
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查看详情 ↗moonshine-streaming-small-gguf
handy-computerPorted from upstream commit 2c03650, pinned 2026-05-06. Validated against the HF Transformers v5.7.0 reference at transcribe.cpp commit 0d312ce on 2026-05-06. English speech-to-text in both one-shot and streaming modes. An encoder-decoder ASR model designed for streaming use (ergodic encoder + sliding-window attention, 50 Hz time-domain frontend). Same family as moonshine-streaming-tiny; deeper encoder/decoder (10 / 10 layers) and wider hidden dims (encoder 620 / decoder 512). Takes a 16 kHz mono WAV and produces a transcript. No translation, no multilingual capability, no timestamps. WER on the full LibriSpeech test-clean split (2,620 utterances). Figures without a commit were published before provenance was recorded.
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查看详情 ↗moonshine-streaming-tiny-gguf
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查看详情 ↗moonshine-tiny-ar-gguf
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查看详情 ↗moonshine-tiny-gguf
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查看详情 ↗moonshine-tiny-ja-gguf
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查看详情 ↗moonshine-tiny-uk-gguf
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查看详情 ↗moonshine-tiny-zh-gguf
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查看详情 ↗moss-transcribe-diarize-gguf
handy-computerPorted from upstream commit d7231bb, pinned 2026-07-12. Validated against the MOSS author repo (OpenMOSS/MOSS-Transcribe-Diarize) reference at transcribe.cpp commit 3f5e15c on 2026-07-12. Offline English/Chinese speech-to-text with speaker diarization. A 0.9B audio-LLM: a Whisper-Medium encoder (24 layers, d_model=1024) feeds a 4x temporal merge + VQAdaptor bridge into a Qwen3-0.6B decoder (28 layers) via audio-token injection. The model emits `[start][Sxx]text[end]`; the runtime parses those generated markers into clean text and segment rows. Speaker attribution is opt-in (`--diarize`) and returns structured speaker ids/turns. Not a streaming model. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.
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查看详情 ↗multitalker-parakeet-streaming-0.6b-v1-gguf
handy-computerGGUF conversions of nvidia/multitalker-parakeet-streaming-0.6b-v1 for use with transcribe.cpp. Ported from upstream commit 8749fc7, pinned 2026-07-12. Validated against the NeMo reference at transcribe.cpp commit 3083021 on 2026-08-03. Offline and cache-aware streaming English speech-to-text with punctuation and capitalization. A cache-aware streaming FastConformer encoder with an RNN-T transducer decoder, fine-tuned from nvidia/nemotron-speech-streaming-en-0.6b. Plain GGUFs run the single_speaker_mode ASR path, while bundle GGUFs embed nvidia/diar_streaming_sortformer_4spk-v2.1 and, with `--diarize`, transcribe up to four overlapping speakers into a speaker-tagged transcript. The encoder preserves the upstream att_context_size=[70, 13] (1.12s) cache-aware attention mask; all four latency lookahead settings are selectable.
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查看详情 ↗nemotron-3.5-asr-streaming-0.6b-gguf
handy-computerGGUF conversions of nvidia/nemotron-3.5-asr-streaming-0.6b for use with transcribe.cpp. Ported from upstream commit 24b151a, pinned 2026-06-08. Validated against the NeMo reference at transcribe.cpp commit 909e94e on 2026-06-08. Multilingual speech-to-text across 32 supported language-locales (the model's tokenizer recognizes 40, but 8 are adaptation-ready and need fine-tuning) with punctuation and capitalization. A cache-aware streaming FastConformer encoder with a prompt-conditioned RNN-T transducer decoder; the target language is selected per call (--language en-US, fr-FR, de-DE, ...) and an auto mode emits a tag. Ships both the offline path (att_context_size=[56, 13], 1.12s, headline accuracy) and runtime-selectable chunked streaming (--stream-chunk-ms 1120 --stream-att-right {0,3,6,13}).
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查看详情 ↗nemotron-speech-streaming-en-0.6b-gguf
handy-computerGGUF conversions of nvidia/nemotron-speech-streaming-en-0.6b for use with transcribe.cpp. Ported from upstream commit ef3bf40, pinned 2026-05-11. Validated against the NeMo reference at transcribe.cpp commit 12f1076 on 2026-05-11. English speech-to-text with punctuation and capitalization. A cache-aware streaming FastConformer encoder with an RNN-T transducer decoder. Runs in both offline and cache-aware streaming modes. The encoder preserves the upstream att_context_size=[70, 13] (1.12s) cache-aware attention mask end-to-end.
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查看详情 ↗orukeet-gguf
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查看详情 ↗parakeet-ctc-0.6b-gguf
handy-computerGGUF conversions of nvidia/parakeet-ctc-0.6b for use with transcribe.cpp. Ported from upstream commit ad09ba1, pinned 2026-05-10. Validated against the NeMo reference at transcribe.cpp commit 42528dd on 2026-05-10. Offline English speech-to-text with greedy CTC decoding. A FastConformer-Large encoder with a linear CTC head — the simplest and fastest decoder in the parakeet family. Output is lowercase, no punctuation. Not a streaming model and does not translate.
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查看详情 ↗parakeet-ctc-1.1b-gguf
handy-computerPorted from upstream commit a707e81, pinned 2026-05-10. Validated against the NeMo reference at transcribe.cpp commit 42528dd on 2026-05-10. Offline English speech-to-text with greedy CTC decoding. A FastConformer-XL encoder with a linear CTC head. Output is lowercase, no punctuation. Not a streaming model and does not translate. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.
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查看详情 ↗parakeet-primeline-gguf
handy-computerGGUF conversions of primeline/parakeet-primeline for use with transcribe.cpp. Ported from upstream commit 3f1a9bc, pinned 2026-08-16. Validated against the NeMo reference at transcribe.cpp commit 856d7c1 on 2026-08-16. primeLine's German fine-tune of NVIDIA's parakeet-tdt-0.6b-v3. A FastConformer encoder with a TDT/RNNT transducer decoder, taking 16 kHz mono WAV and producing a punctuated, cased transcript with optional token-level timestamps. Tuned for German, but the fine-tune did not collapse the base model's multilingual ability: it still transcribes the other 24 v3 languages with correct per-language casing and punctuation. Not a streaming model and does not translate.
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查看详情 ↗parakeet-rnnt-0.6b-gguf
handy-computerPorted from upstream commit c0c1f09, pinned 2026-05-10. Validated against the NeMo reference at transcribe.cpp commit 42528dd on 2026-05-10. Offline English speech-to-text with greedy RNN-T decoding. A FastConformer-Large encoder with an RNN-T transducer decoder. Output is lowercase, no punctuation. Not a streaming model and does not translate. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.
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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。