{ "data": { "a": { "slug": "nemotron-3-5-asr-streaming-0-6b", "name": "Nemotron 3.5 ASR Streaming 0.6B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "speech-to-text", "summary": "Nemotron 3.5 ASR Streaming 0.6B is NVIDIA's open-weight streaming speech recognition model, a 600M-parameter FastConformer with an RNN-T decoder, transcribing 32 language-locales with punctuation and an optional detected-language tag at chunk sizes from 80 ms to 1.12 s, under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json", "dns": "nemotron-3-5-asr-streaming-0-6b.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/nemotron-3.5-asr-streaming-0.6b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1 (the Linux Foundation's permissive model licence). NVIDIA sells no per-call access to these weights in this listing. Running them costs the hardware, about 2.6 GB of F32 safetensors or a 0.74 GB Q8_0 GGUF. fal, Together and DeepInfra serve it as inference providers on Hugging Face (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/docs/transformers/en/model_doc/nemotron3_5_asr", "capabilities": [ "speech.stt", "speech.streaming", "speech.batch", "speech.languages", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "streaming", "multilingual", "rnnt", "fastconformer", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-09-10", "graded": true, "anchor": { "graded": true, "score": 60.3, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1520, "categoryRank": 22, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 54, "interface": 50, "reach": 70, "risk": 60, "upkeep": 69 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": 0, "verdict": "A small streaming transcription model for live audio in about 32 languages, with language detection and 80 ms to 1.12 s chunks. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NVIDIA's NeMo-Speech.cpp, and three providers host it. Timestamps aren't documented, and NVIDIA's own WERs are 23% to 26% outside its 19 transcription-ready locales.", "bestFor": "Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop", "strengths": [ "0.64B parameters, with a 0.74 GB Q8_0 GGUF from NVIDIA and F32 safetensors of 2.55 GB", "Cache-aware streaming with chunk sizes from 80 ms to 1.12 s chosen at run time", "One model for 32 working locales with automatic language detection and a language tag in the output", "Native in Transformers from v5.13.0, no remote code, and hosted by fal, Together and DeepInfra", "OpenMDW-1.1, permissive with no use policy, ungated, and NVIDIA answers most discussions" ], "weaknesses": [ "Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp", "A pickled NeMo checkpoint sits beside the safetensors and NeMo's documented path loads it", "Plain text out; timestamps and confidence scores are claimed in a subcard but not documented", "Publisher WERs of 23% to 26% across the 13 broad-coverage locales, and 8 locales need fine-tuning", "No safety results, and training data without a cut-off" ], "agentNotes": [ "Quickest local run, `pip install \"transformers\u003e=5.13.0\"`, then `pipeline(\"automatic-speech-recognition\", model=\"nvidia/nemotron-3.5-asr-streaming-0.6b\")`; the pipeline uses en-US, so pass language= to the processor for anything else", "With NeMo-Speech.cpp, `nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto`", "Pass a locale such as de-DE or a bare code such as de, or auto; in auto mode decode with skip_special_tokens=False to keep the \u003cxx-XX\u003e tag, True to drop it", "Pick latency with att_context_size [56, r], r in 0, 1, 3, 6, 13 (80 to 1,120 ms chunks), or processor.set_num_lookahead_tokens in Transformers; bigger chunks are more accurate", "Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends", "Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle", "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.3 } ], "editorialScores": { "capability": 54, "interface": 50, "reach": 70, "risk": 60, "upkeep": 69 }, "provenanceScore": 76, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"transformers\u003e=5.13.0\"\npython -c \"from transformers import pipeline; print(pipeline('automatic-speech-recognition', model='nvidia/nemotron-3.5-asr-streaming-0.6b')('speech.wav'))\"" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/nemotron-3-5-asr-streaming-0-6b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-transcribe-30b-a4b", "parakeet-tdt-0-6b-v3", "parakeet-unified-en-0-6b", "nemotron-3-5-parse", "cosmos-reason2-2b", "nemotron-3-5-bolt-e4b-4gb", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/nemotron-speech-streaming-en-0.6b (NVIDIA's own English model, extended to 40 locales)", "params": "0.64B", "goodFor": "Low-latency streaming transcription in 32 languages from one small model, with automatic language detection, for voice agents and many parallel streams.", "runtimes": [ "Transformers" ], "builds": [ { "name": "nemotron-3.5-asr-streaming-0.6b (safetensors)", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/model.safetensors", "who": "publisher", "precision": "fp32", "sizeGB": 2.55, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ] }, { "name": "nemotron-3.5-asr-streaming-0.6b.nemo", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/nemotron-3.5-asr-streaming-0.6b.nemo", "who": "publisher", "precision": "fp32", "sizeGB": 2.37, "memoryGB": 8, "format": "nemo", "note": "NeMo checkpoint, a tar archive holding a PyTorch pickle; loads with NeMo from its main branch" }, { "name": "nemotron-3.5-asr-streaming-0.6b.q8_0.gguf", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/nemotron-3.5-asr-streaming-0.6b.q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 0.74, "memoryGB": 8, "format": "gguf", "note": "For NVIDIA's NeMo-Speech.cpp, not llama.cpp (GGUF architecture asr). Re-converted with an embedded SentencePiece tokenizer on 10 September 2026" } ], "run": "hf download nvidia/nemotron-3.5-asr-streaming-0.6b nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --local-dir models \u0026\u0026 nemo-speech transcribe audio.wav --model models/nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto", "runBuild": "nemotron-3.5-asr-streaming-0.6b.q8_0.gguf", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron 3.5 Bolt E4B 4GB", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "params": "8B total, 4B effective", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb" }, { "name": "Nemotron Labs 3 Elastic 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron Labs 3 Puzzle 75B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16", "params": "75B total, 9B active" }, { "name": "Nemotron 3 Labs Ultra Math SFT", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Labs Ultra Math RL", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL", "params": "550B total, 55B active" }, { "name": "Nemotron Labs 3 Competitive Coding 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Parse", "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse", "params": "0.631B (630,800,496 trainable parameters per the card)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse" }, { "name": "Nemotron 3.5 Transcribe 30B-A4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B", "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b" } ], "olderCount": 47 }, "area": "voice", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card says the model was developed by NVIDIA and the files sit in NVIDIA's verified nvidia organisation on Hugging Face, which developer.nvidia.com links to. The domain registration date is carried from the RDAP check made for nemotron-3-super-120b-a12b on 21 October 2026.", "Nothing is hosted in this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored. The privacy subcard links NVIDIA's privacy policy.", "The changelog is the repository's commit history, 36 commits from 15 May to 10 September 2026, the last re-converting the GGUF with an embedded SentencePiece tokenizer.", "www.nvidia.com/.well-known/security.txt was not requested again; earlier NVIDIA listings record a 403. The card links NVIDIA's vulnerability submission page." ], "score": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.json", "live": { "slug": "nemotron-3-5-asr-streaming-0-6b", "securityTxt": { "url": "https://nvidia.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-10T16:08:00.271598775Z" }, "domain": { "domain": "nvidia.com", "registered": "1993-04-20", "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com", "checkedAt": "2026-11-08T15:03:11.895084282Z" }, "pages": [ { "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:32.527836743Z", "changedAt": "2026-11-10T16:23:32.527836743Z", "fingerprint": "f23b23866a4b" } ], "updatedAt": "2026-11-10T16:23:32.527836743Z" } }, "answer": "Nemotron 3.5 ASR Streaming 0.6B scores 60.3 (C) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in 3 of 4 scored categories. Parakeet Unified EN 0.6B leads on documentation and upkeep.", "b": { "slug": "parakeet-unified-en-0-6b", "name": "Parakeet Unified EN 0.6B", "vendor": "NVIDIA", "vendorUrl": "https://github.com/NVIDIA/NeMo", "kind": "weights", "category": "speech-to-text", "summary": "Parakeet Unified EN 0.6B is NVIDIA's open-weight English speech recognition model, a 600M FastConformer encoder with an RNN-T decoder trained for both offline and streaming use down to 160 ms of latency, with punctuation, published as a NeMo archive.", "url": "https://www.anchorterminal.com/tools/parakeet-unified-en-0-6b", "markdownUrl": "https://www.anchorterminal.com/tools/parakeet-unified-en-0-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/parakeet-unified-en-0-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/parakeet-unified-en-0-6b.json", "dns": "parakeet-unified-en-0-6b.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/parakeet-unified-en-0.6b", "license": "NVIDIA Open Model License", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/parakeet-unified-en-0.6b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Open Model License. Running it yourself costs little hardware, a 2.47 GB NeMo archive in FP32 (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/nvidia/parakeet-unified-en-0.6b", "capabilities": [ "speech.stt", "speech.batch", "speech.streaming", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "english", "streaming", "fastconformer", "rnnt", "nemo", "nvidia-open-model-license", "self-hosted" ], "lastRelease": "2026-06-09", "graded": true, "anchor": { "graded": true, "score": 47.5, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1520, "categoryRank": 31, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 35, "reach": 50, "risk": 35, "upkeep": 79 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "One English model for batch and live transcription, with a well-documented data list and an NVIDIA team that answers. It runs only in NVIDIA NeMo from a pickle-format archive, with no safetensors, GGUF or quantised build, returns plain text without timestamps, and hasn't changed since June.", "bestFor": "English voice agents and captioning that want one small model for both recorded and live audio and already run NeMo", "strengths": [ "One model for offline and streaming transcription, down to 160 ms of latency", "Small: a 2.47 GB file that fits any machine", "Training datasets listed by name with hours", "NVIDIA replied in every discussion on the repository", "A security reporting route and public bulletins from NVIDIA" ], "weaknesses": [ "The only file is a NeMo archive holding a pickle checkpoint", "NVIDIA NeMo is the only runtime; none of the six we count runs it", "No timestamps or other typed output documented", "English only", "A custom licence with a use policy that NVIDIA can change", "No commit since 9 June 2026" ], "agentNotes": [ "Load it with NeMo: `nemo_asr.models.ASRModel.from_pretrained(model_name='nvidia/parakeet-unified-en-0.6b')`, then `transcribe([path])[0].text`", "Loading a .nemo archive unpickles a PyTorch checkpoint, so only load it from NVIDIA's repository", "Send mono wav audio. English only", "For live audio use NeMo's chunked RNN-T streaming script with a 5.6 s left context, and pick the chunk and right context from the card's table", "Below 240 ms of latency accuracy drops, and at 80 ms NVIDIA recommends its nemotron-speech-streaming-en-0.6b instead", "If you need word timestamps, use Parakeet TDT 0.6B v3" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 47.5 } ], "editorialScores": { "interface": 35, "reach": 50, "risk": 35, "upkeep": 79 }, "provenanceScore": 74, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install -U \"nemo_toolkit[asr]\"\npython -c \"import nemo.collections.asr as nemo_asr; m = nemo_asr.models.ASRModel.from_pretrained(model_name='nvidia/parakeet-unified-en-0.6b'); print(m.transcribe(['speech.wav'])[0].text)\"", "http": "# no HTTP server ships with it. For streaming, NeMo's script:\npython examples/asr/asr_chunked_inference/rnnt/speech_to_text_streaming_infer_rnnt.py \\\n pretrained_name=nvidia/parakeet-unified-en-0.6b dataset_manifest=manifest.json \\\n left_context_secs=5.6 chunk_secs=0.56 right_context_secs=0.56 att_context_size_as_chunk=true" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/parakeet-unified-en-0-6b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "nemotron-3-5-transcribe-30b-a4b", "parakeet-tdt-0-6b-v3", "nemotron-3-5-parse", "cosmos-reason2-2b", "nemotron-3-5-bolt-e4b-4gb", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "params": "0.6B", "goodFor": "English transcription from one model in both batch and live use, with punctuation and capitalisation, where latency of 160 ms to 2 s matters.", "runtimes": [], "builds": [ { "name": "parakeet-unified-en-0.6b.nemo", "url": "https://huggingface.co/nvidia/parakeet-unified-en-0.6b/blob/main/parakeet-unified-en-0.6b.nemo", "who": "publisher", "precision": "fp32", "sizeGB": 2.47, "memoryGB": 8, "format": "nemo", "runsOn": [ "NeMo" ], "note": "A NeMo archive holding a PyTorch checkpoint (pickle), loaded by NeMo's ASRModel.from_pretrained. The only build" } ], "run": "python -c \"import nemo.collections.asr as nemo_asr; print(nemo_asr.models.ASRModel.from_pretrained(model_name='nvidia/parakeet-unified-en-0.6b').transcribe(['speech.wav'])[0].text)\"", "runBuild": "parakeet-unified-en-0.6b.nemo", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Parakeet", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "parakeet-rnnt-1.1b", "url": "https://huggingface.co/nvidia/parakeet-rnnt-1.1b", "params": "1.1B" }, { "name": "parakeet-ctc-1.1b", "url": "https://huggingface.co/nvidia/parakeet-ctc-1.1b", "params": "1.1B" }, { "name": "parakeet-rnnt-0.6b", "url": "https://huggingface.co/nvidia/parakeet-rnnt-0.6b", "params": "0.6B" }, { "name": "parakeet-ctc-0.6b", "url": "https://huggingface.co/nvidia/parakeet-ctc-0.6b", "params": "0.6B" }, { "name": "parakeet-tdt-1.1b", "url": "https://huggingface.co/nvidia/parakeet-tdt-1.1b", "params": "1.1B" }, { "name": "parakeet-tdt_ctc-1.1b", "url": "https://huggingface.co/nvidia/parakeet-tdt_ctc-1.1b", "params": "1.1B" }, { "name": "parakeet-tdt_ctc-0.6b-ja", "url": "https://huggingface.co/nvidia/parakeet-tdt_ctc-0.6b-ja", "params": "0.6B" }, { "name": "parakeet-tdt_ctc-110m", "url": "https://huggingface.co/nvidia/parakeet-tdt_ctc-110m", "params": "110M" }, { "name": "parakeet-tdt-0.6b-v2", "url": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2", "params": "0.6B" }, { "name": "parakeet-rnnt-110m-da-dk", "url": "https://huggingface.co/nvidia/parakeet-rnnt-110m-da-dk", "params": "110M" }, { "name": "parakeet-tdt-0.6b-v3", "url": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3", "params": "0.6B (627M per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3" }, { "name": "parakeet_realtime_eou_120m-v1", "url": "https://huggingface.co/nvidia/parakeet_realtime_eou_120m-v1", "params": "120M" }, { "name": "multitalker-parakeet-streaming-0.6b-v1", "url": "https://huggingface.co/nvidia/multitalker-parakeet-streaming-0.6b-v1", "params": "0.6B" }, { "name": "parakeet-ctc-0.6b-Vietnamese", "url": "https://huggingface.co/nvidia/parakeet-ctc-0.6b-Vietnamese", "params": "0.6B" } ], "olderCount": 47 }, "area": "voice", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/parakeet-unified-en-0.6b/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The model sits in NVIDIA's Hugging Face organisation and the card's governing terms are the NVIDIA Open Model License. The domain registration date is carried from the RDAP check made for nemotron-3-nano-30b-a3b on 21 October 2026.", "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 19 commits from 7 April to 9 June 2026, mostly card edits.", "www.nvidia.com/.well-known/security.txt answered 403 at the 21 October 2026 check for nemotron-3-nano-30b-a3b. The card links NVIDIA's security vulnerability form." ], "score": 74 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/parakeet-unified-en-0-6b.json" }, "edges": [ { "by": 25, "category": "risk", "leads": "nemotron-3-5-asr-streaming-0-6b", "name": "Risk", "reasons": { "a": "File safety gets 15, because the NeMo checkpoint beside the safetensors is a pickle that one documented path loads; no repository code runs. Provenance gets 15 for files in NVIDIA's verified organisation with hashes for every file. Licence risk keeps 15, since OpenMDW-1.1 has none of the deductions and the one-day metadata slip after release was a correction. The security programme gets 10 for the card's vulnerability route and NVIDIA's PSIRT bulletins. Agent guidance gets 5 for general accuracy cautions, and safety evaluation 0, since no results are published.", "b": "File safety 0, the only weights file is parakeet-unified-en-0.6b.nemo, a NeMo archive whose weights are a PyTorch checkpoint read with torch.load, a pickle format, and the card's documented path loads it. Provenance 15, the file sits in NVIDIA's own Hugging Face organisation, the one NVIDIA's NeMo repositories link to (10), and the Hub gives a SHA-256 for the archive and git hashes for the rest (5). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 5, 15 less 5 for a use policy beyond the law (NVIDIA's Trustworthy AI terms and the guardrail clause) and 5 for terms the licensor can change or revoke, as read for NVIDIA's other listings under the same licence. Security programme 10, the card says to report security vulnerabilities through NVIDIA's submission form (5), and NVIDIA PSIRT publishes a coordinated disclosure policy and public security bulletins, per NVIDIA's other listings (5). 0 + 15 + 0 + 5 + 5 + 10 = 35." }, "scores": { "a": 60, "b": 35 } }, { "by": 20, "category": "reach", "leads": "nemotron-3-5-asr-streaming-0-6b", "name": "Reach", "reasons": { "a": "Memory earns the full 30 because NVIDIA's own Q8_0 GGUF is 0.74 GB and even the F32 safetensors need only about 3 GB. Runtimes get 5, for Transformers from v5.13.0; vLLM points to a third party's plugin, SGLang and llama.cpp have only a Parakeet encoder for multimodal models, and Ollama and LM Studio have nothing. NVIDIA's NeMo and NeMo-Speech.cpp run it but aren't among the six. Builds get 10 for one quantised build, the Q8_0 GGUF. Terms get 10 because OpenMDW-1.1 is permissive with nothing added but isn't OSI-approved. Download gets 10 for an ungated repository and hosted too gets 5 for fal, Together and DeepInfra.", "b": "Memory 30, the only build is the FP32 NeMo archive, 2.47 GB, and 2.47 GB × 1.2 = 3.0 GB, under 16 GB. Runtimes 0, none of the six runs it in a released version. Builds 0, NVIDIA publishes no quantised build of it. Terms 10, the NVIDIA Open Model License is a custom licence, not OSI-approved, that lets anyone use the model commercially for free under a use policy, which is the custom-licence band, as on NVIDIA's other listings. Download 10, the Hub API reports gated false on 10 November 2026, so the file downloads with no account and nothing to accept. Hosted 0, Hugging Face's inference provider mapping is empty on 10 November 2026, and the card names no NVIDIA API that serves it. 30 + 0 + 0 + 10 + 10 + 0 = 50." }, "scores": { "a": 70, "b": 50 } }, { "by": 15, "category": "interface", "leads": "nemotron-3-5-asr-streaming-0-6b", "name": "Agent interface", "reasons": { "a": "Read for a speech model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. NVIDIA documents the audio, the language prompt and the chunk settings, and the language-tagged output with how to strip the tag in Transformers and NeMo (25). It was trained for one stream with one language prompt at a time (10). Timestamps and confidence scores are claimed in a subcard with no way shown to ask for them (5). The chat template line gets 10 because the language prompt is given only as processor or script arguments. Reasoning control and context score 0, with no reasoning and no stated context.", "b": "Chat template 0, no chat template and no prompt. Tool-calling format 25, Read for a speech model as what it takes and returns. Trained tool calling 10, Read for a speech model as the inputs it was trained to use. Structured output 0, Read for a speech model as typed outputs. Reasoning control 0, A speech recognition model with no reasoning. Context 0, no token context is stated. 0 + 25 + 10 + 0 + 0 + 0 = 35." }, "scores": { "a": 50, "b": 35 } }, { "by": 10, "category": "upkeep", "leads": "parakeet-unified-en-0-6b", "name": "Documentation and upkeep", "reasons": { "a": "The card with its subcards covers intended use, limitations, architecture, parameters and languages, and misses a context length and a cut-off (14). Training data names the sources and a size range without a cut-off (5). The files last changed on 10 September 2026, and NVIDIA's NeMo-Speech.cpp changed this model's cache-aware RNNT streaming path on 24 October 2026, released in v0.3.0 on 30 October (20). NVIDIA answered 18 of the 25 questions and reports (20). It's a first release, with the GGUF replaced in place through a titled pull request (5). An unanswered report of a tokenizer fault for Greek costs file health half (5).", "b": "Model card 14, Intended use, limitations (buffered streaming only, accuracy falling at 160 ms and below), architecture, parameters (600M) and the language are there. Training data 20, the card lists the training datasets by name, with hours for the four Granary subsets and about 250,000 hours in all, says how the transcripts were made, and NVIDIA publishes Granary on Hugging Face. Last change 10, the last commit to the repository, on 9 June 2026, edited the card, 154 days before this check and within 180. Responsiveness 20, All 5 discussions that aren't pull requests (8 April to 24 June 2026) have a reply from an NVIDIA member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release of the unified model. File health 10, one archive and a card that agree on the model's name, size and NeMo as the way to load it. 14 + 20 + 10 + 20 + 5 + 10 = 79." }, "scores": { "a": 69, "b": 79 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "NVIDIA", "b": "NVIDIA", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "OpenMDW-1.1", "b": "NVIDIA Open Model License", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-09-10", "b": "2026-06-09", "name": "Last release" }, { "a": "no document linked", "b": "2025-10-24", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not read yet", "b": "yes", "name": "Customer content may train models" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not read yet", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3.5 ASR Streaming 0.6B scores 60.3 (C) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in 3 of 4 scored categories. Parakeet Unified EN 0.6B leads on documentation and upkeep.", "question": "Which is better for AI agents, Nemotron 3.5 ASR Streaming 0.6B or Parakeet Unified EN 0.6B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 70 against 50", "Risk, 60 against 35", "Agent interface, 50 against 35" ], "also": null, "goodFor": "Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop", "slug": "nemotron-3-5-asr-streaming-0-6b", "watchFor": "Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp" }, { "aheadOn": [ "Documentation and upkeep, 79 against 69" ], "also": null, "goodFor": "English voice agents and captioning that want one small model for both recorded and live audio and already run NeMo", "slug": "parakeet-unified-en-0-6b", "watchFor": "The only file is a NeMo archive holding a pickle checkpoint" } ], "history": { "nemotron-3-5-asr-streaming-0-6b": [ { "causes": [ "product" ], "date": "2026-11-04", "delta": 0.5, "from": 59.8, "fromGrade": "C", "to": 60.3, "toGrade": "C" } ], "parakeet-unified-en-0-6b": null }, "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-5-asr-streaming-0-6b.json", "title": "Cohere Transcribe 03-2026 vs Nemotron 3.5 ASR Streaming 0.6B", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-5-asr-streaming-0-6b" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-parakeet-unified-en-0-6b.json", "title": "Cohere Transcribe 03-2026 vs Parakeet Unified EN 0.6B", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-parakeet-unified-en-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b.json", "title": "Granite Speech 5.0 2B vs Nemotron 3.5 ASR Streaming 0.6B", "url": 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"https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-parakeet-unified-en-0-6b.json", "title": "Nemotron 3.5 Transcribe 30B-A4B vs Parakeet Unified EN 0.6B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-parakeet-unified-en-0-6b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-parakeet-unified-en-0-6b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Parakeet Unified EN 0.6B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-parakeet-unified-en-0-6b" }, { "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-parakeet-unified-en-0-6b.json", "title": "Parakeet TDT 0.6B v3 vs Parakeet Unified EN 0.6B", "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-parakeet-unified-en-0-6b" }, { "json": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-qwen3-asr-0-6b.json", "title": "Parakeet Unified EN 0.6B vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-qwen3-asr-1-7b.json", "title": "Parakeet Unified EN 0.6B vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Parakeet Unified EN 0.6B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-underdog-bark-3.json", "title": "Parakeet Unified EN 0.6B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-voxtral-mini-4b-realtime-2602.json", "title": "Parakeet Unified EN 0.6B vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-voxtral-mini-4b-realtime-2602" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "nemotron-3-5-asr-streaming-0-6b", "key": "reach", "name": "Reach", "nemotron-3-5-asr-streaming-0-6b": 70, "parakeet-unified-en-0-6b": 50, "weight": 25 }, { "by": 25, "edge": "nemotron-3-5-asr-streaming-0-6b", "key": "risk", "name": "Risk", "nemotron-3-5-asr-streaming-0-6b": 60, "parakeet-unified-en-0-6b": 35, "weight": 20 }, { "by": 15, "edge": "nemotron-3-5-asr-streaming-0-6b", "key": "interface", "name": "Agent interface", "nemotron-3-5-asr-streaming-0-6b": 50, "parakeet-unified-en-0-6b": 35, "weight": 10 }, { "by": 10, "edge": "parakeet-unified-en-0-6b", "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-5-asr-streaming-0-6b": 69, "parakeet-unified-en-0-6b": 79, "weight": 10 } ], "summary": "Nemotron 3.5 ASR Streaming 0.6B scores 60.3 (C) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in 3 of 4 scored categories. Parakeet Unified EN 0.6B leads on documentation and upkeep. Both do speech-to-text.", "tasks": null, "tutorials": [], "verdicts": { "nemotron-3-5-asr-streaming-0-6b": "A small streaming transcription model for live audio in about 32 languages, with language detection and 80 ms to 1.12 s chunks. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NVIDIA's NeMo-Speech.cpp, and three providers host it. Timestamps aren't documented, and NVIDIA's own WERs are 23% to 26% outside its 19 transcription-ready locales.", "parakeet-unified-en-0-6b": "One English model for batch and live transcription, with a well-documented data list and an NVIDIA team that answers. It runs only in NVIDIA NeMo from a pickle-format archive, with no safetensors, GGUF or quantised build, returns plain text without timestamps, and hasn't changed since June." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-unified-en-0-6b", "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-unified-en-0-6b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-unified-en-0-6b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-unified-en-0-6b.min.md" }, "markdown": "Nemotron 3.5 ASR Streaming 0.6B scores 60.3 (C) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in 3 of 4 scored categories. Parakeet Unified EN 0.6B leads on documentation and upkeep. Both do speech-to-text.\n\n- Nemotron 3.5 ASR Streaming 0.6B: grade C, 60.3/100, rank #91 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json\n- Parakeet Unified EN 0.6B: grade D, 47.5/100, rank #154 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/parakeet-unified-en-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/parakeet-unified-en-0-6b.json\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 386 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n\n## Which one, for what\n\n### Nemotron 3.5 ASR Streaming 0.6B (C)\n\nGood for: Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop\n\nAhead on:\n- Reach, 70 against 50\n- Risk, 60 against 35\n- Agent interface, 50 against 35\n\nWatch for: Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp\n\n### Parakeet Unified EN 0.6B (D)\n\nGood for: English voice agents and captioning that want one small model for both recorded and live audio and already run NeMo\n\nAhead on:\n- Documentation and upkeep, 79 against 69\n\nWatch for: The only file is a NeMo archive holding a pickle checkpoint\n\n\n## Why the edge\n\n### Risk: Nemotron 3.5 ASR Streaming 0.6B +25\n\n- Nemotron 3.5 ASR Streaming 0.6B (60): File safety gets 15, because the NeMo checkpoint beside the safetensors is a pickle that one documented path loads; no repository code runs. Provenance gets 15 for files in NVIDIA's verified organisation with hashes for every file. Licence risk keeps 15, since OpenMDW-1.1 has none of the deductions and the one-day metadata slip after release was a correction. The security programme gets 10 for the card's vulnerability route and NVIDIA's PSIRT bulletins. Agent guidance gets 5 for general accuracy cautions, and safety evaluation 0, since no results are published.\n- Parakeet Unified EN 0.6B (35): File safety 0, the only weights file is parakeet-unified-en-0.6b.nemo, a NeMo archive whose weights are a PyTorch checkpoint read with torch.load, a pickle format, and the card's documented path loads it. Provenance 15, the file sits in NVIDIA's own Hugging Face organisation, the one NVIDIA's NeMo repositories link to (10), and the Hub gives a SHA-256 for the archive and git hashes for the rest (5). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 5, 15 less 5 for a use policy beyond the law (NVIDIA's Trustworthy AI terms and the guardrail clause) and 5 for terms the licensor can change or revoke, as read for NVIDIA's other listings under the same licence. Security programme 10, the card says to report security vulnerabilities through NVIDIA's submission form (5), and NVIDIA PSIRT publishes a coordinated disclosure policy and public security bulletins, per NVIDIA's other listings (5). 0 + 15 + 0 + 5 + 5 + 10 = 35.\n\n### Reach: Nemotron 3.5 ASR Streaming 0.6B +20\n\n- Nemotron 3.5 ASR Streaming 0.6B (70): Memory earns the full 30 because NVIDIA's own Q8_0 GGUF is 0.74 GB and even the F32 safetensors need only about 3 GB. Runtimes get 5, for Transformers from v5.13.0; vLLM points to a third party's plugin, SGLang and llama.cpp have only a Parakeet encoder for multimodal models, and Ollama and LM Studio have nothing. NVIDIA's NeMo and NeMo-Speech.cpp run it but aren't among the six. Builds get 10 for one quantised build, the Q8_0 GGUF. Terms get 10 because OpenMDW-1.1 is permissive with nothing added but isn't OSI-approved. Download gets 10 for an ungated repository and hosted too gets 5 for fal, Together and DeepInfra.\n- Parakeet Unified EN 0.6B (50): Memory 30, the only build is the FP32 NeMo archive, 2.47 GB, and 2.47 GB × 1.2 = 3.0 GB, under 16 GB. Runtimes 0, none of the six runs it in a released version. Builds 0, NVIDIA publishes no quantised build of it. Terms 10, the NVIDIA Open Model License is a custom licence, not OSI-approved, that lets anyone use the model commercially for free under a use policy, which is the custom-licence band, as on NVIDIA's other listings. Download 10, the Hub API reports gated false on 10 November 2026, so the file downloads with no account and nothing to accept. Hosted 0, Hugging Face's inference provider mapping is empty on 10 November 2026, and the card names no NVIDIA API that serves it. 30 + 0 + 0 + 10 + 10 + 0 = 50.\n\n### Agent interface: Nemotron 3.5 ASR Streaming 0.6B +15\n\n- Nemotron 3.5 ASR Streaming 0.6B (50): Read for a speech model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. NVIDIA documents the audio, the language prompt and the chunk settings, and the language-tagged output with how to strip the tag in Transformers and NeMo (25). It was trained for one stream with one language prompt at a time (10). Timestamps and confidence scores are claimed in a subcard with no way shown to ask for them (5). The chat template line gets 10 because the language prompt is given only as processor or script arguments. Reasoning control and context score 0, with no reasoning and no stated context.\n- Parakeet Unified EN 0.6B (35): Chat template 0, no chat template and no prompt. Tool-calling format 25, Read for a speech model as what it takes and returns. Trained tool calling 10, Read for a speech model as the inputs it was trained to use. Structured output 0, Read for a speech model as typed outputs. Reasoning control 0, A speech recognition model with no reasoning. Context 0, no token context is stated. 0 + 25 + 10 + 0 + 0 + 0 = 35.\n\n### Documentation and upkeep: Parakeet Unified EN 0.6B +10\n\n- Nemotron 3.5 ASR Streaming 0.6B (69): The card with its subcards covers intended use, limitations, architecture, parameters and languages, and misses a context length and a cut-off (14). Training data names the sources and a size range without a cut-off (5). The files last changed on 10 September 2026, and NVIDIA's NeMo-Speech.cpp changed this model's cache-aware RNNT streaming path on 24 October 2026, released in v0.3.0 on 30 October (20). NVIDIA answered 18 of the 25 questions and reports (20). It's a first release, with the GGUF replaced in place through a titled pull request (5). An unanswered report of a tokenizer fault for Greek costs file health half (5).\n- Parakeet Unified EN 0.6B (79): Model card 14, Intended use, limitations (buffered streaming only, accuracy falling at 160 ms and below), architecture, parameters (600M) and the language are there. Training data 20, the card lists the training datasets by name, with hours for the four Granary subsets and about 250,000 hours in all, says how the transcripts were made, and NVIDIA publishes Granary on Hugging Face. Last change 10, the last commit to the repository, on 9 June 2026, edited the card, 154 days before this check and within 180. Responsiveness 20, All 5 discussions that aren't pull requests (8 April to 24 June 2026) have a reply from an NVIDIA member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release of the unified model. File health 10, one archive and a card that agree on the model's name, size and NeMo as the way to load it. 14 + 20 + 10 + 20 + 5 + 10 = 79.\n\n## Score by category\n\n| Category | Weight | Nemotron 3.5 ASR Streaming 0.6B | Parakeet Unified EN 0.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 50 | Nemotron 3.5 ASR Streaming 0.6B +20 |\n| Risk | 20% (20 this run) | 60 | 35 | Nemotron 3.5 ASR Streaming 0.6B +25 |\n| Agent interface | 10% (10 this run) | 50 | 35 | Nemotron 3.5 ASR Streaming 0.6B +15 |\n| Documentation and upkeep | 10% (10 this run) | 69 | 79 | Parakeet Unified EN 0.6B +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **60.3 · C** | **47.5 · D** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3.5 ASR Streaming 0.6B | Parakeet Unified EN 0.6B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | NVIDIA |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | OpenMDW-1.1 | NVIDIA Open Model License |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-10 | 2026-06-09 |\n| Terms last updated | no document linked | 2025-10-24 |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | not read yet | yes |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | not found in the text |\n| Terms or service can change without notice | not read yet | not found in the text |\n| Arbitration or class-action waiver | not read yet | not found in the text |\n| Agent reviews | 2.8/5 (4) | none |\n\n## How the scores have moved\n\n### Nemotron 3.5 ASR Streaming 0.6B\n\n- +0.5 on 4 November 2026: product\n\n### Parakeet Unified EN 0.6B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Nemotron 3.5 ASR Streaming 0.6B.** A small streaming transcription model for live audio in about 32 languages, with language detection and 80 ms to 1.12 s chunks. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NVIDIA's NeMo-Speech.cpp, and three providers host it. Timestamps aren't documented, and NVIDIA's own WERs are 23% to 26% outside its 19 transcription-ready locales.\n\n**Parakeet Unified EN 0.6B.** One English model for batch and live transcription, with a well-documented data list and an NVIDIA team that answers. It runs only in NVIDIA NeMo from a pickle-format archive, with no safetensors, GGUF or quantised build, returns plain text without timestamps, and hasn't changed since June.\n\n## Before you call either\n\n### Nemotron 3.5 ASR Streaming 0.6B\n\n1. Quickest local run, `pip install \"transformers\u003e=5.13.0\"`, then `pipeline(\"automatic-speech-recognition\", model=\"nvidia/nemotron-3.5-asr-streaming-0.6b\")`; the pipeline uses en-US, so pass language= to the processor for anything else\n2. With NeMo-Speech.cpp, `nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto`\n3. Pass a locale such as de-DE or a bare code such as de, or auto; in auto mode decode with skip_special_tokens=False to keep the \u003cxx-XX\u003e tag, True to drop it\n4. Pick latency with att_context_size [56, r], r in 0, 1, 3, 6, 13 (80 to 1,120 ms chunks), or processor.set_num_lookahead_tokens in Transformers; bigger chunks are more accurate\n5. Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends\n6. Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle\n7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions\n\n### Parakeet Unified EN 0.6B\n\n1. Load it with NeMo: `nemo_asr.models.ASRModel.from_pretrained(model_name='nvidia/parakeet-unified-en-0.6b')`, then `transcribe([path])[0].text`\n2. Loading a .nemo archive unpickles a PyTorch checkpoint, so only load it from NVIDIA's repository\n3. Send mono wav audio. English only\n4. For live audio use NeMo's chunked RNN-T streaming script with a 5.6 s left context, and pick the chunk and right context from the card's table\n5. Below 240 ms of latency accuracy drops, and at 80 ms NVIDIA recommends its nemotron-speech-streaming-en-0.6b instead\n6. If you need word timestamps, use Parakeet TDT 0.6B v3\n\n## Questions\n\n### Which is better for AI agents, Nemotron 3.5 ASR Streaming 0.6B or Parakeet Unified EN 0.6B?\n\nNemotron 3.5 ASR Streaming 0.6B scores 60.3 (C) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in 3 of 4 scored categories. Parakeet Unified EN 0.6B leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-unified-en-0-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-unified-en-0-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-5-asr-streaming-0-6b\", \"b\": \"parakeet-unified-en-0-6b\"}`. From a terminal: `anchor compare nemotron-3-5-asr-streaming-0-6b parakeet-unified-en-0-6b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json and https://www.anchorterminal.com/api/v1/tools/parakeet-unified-en-0-6b.json\n\n## Other comparisons with Nemotron 3.5 ASR Streaming 0.6B or Parakeet Unified EN 0.6B\n\n- [Cohere Transcribe 03-2026 vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Cohere Transcribe 03-2026 vs Parakeet Unified EN 0.6B](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-parakeet-unified-en-0-6b.md)\n- [Granite Speech 5.0 2B vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Granite Speech 5.0 2B vs Parakeet Unified EN 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