{ "data": { "a": { "slug": "parakeet-tdt-0-6b-v3", "name": "Parakeet TDT 0.6B v3", "vendor": "NVIDIA", "vendorUrl": "https://github.com/NVIDIA/NeMo", "kind": "weights", "category": "speech-to-text", "summary": "Parakeet TDT 0.6B v3 is NVIDIA's open-weight speech recognition model for 25 European languages, a 0.6B-parameter FastConformer encoder with a TDT decoder that detects the language and returns punctuated text with word and segment timestamps, under CC BY 4.0.", "url": "https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3", "markdownUrl": "https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/parakeet-tdt-0-6b-v3.json", "repo": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3", "license": "CC-BY-4.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/parakeet-tdt-0.6b-v3" } ], "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 CC BY 4.0, which asks for attribution. Running it costs the hardware; the card says at least 2 GB of RAM to load it. The files are 2.5 GB in FP32 safetensors or 0.7 GB as NVIDIA's Q8_0 GGUF (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/nvidia/parakeet-tdt-0.6b-v3", "capabilities": [ "speech.stt", "speech.batch", "speech.streaming", "speech.languages", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "multilingual", "timestamps", "fastconformer", "tdt", "nemo", "cc-by-4.0", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-08-05", "graded": true, "anchor": { "graded": true, "score": 66.9, "grade": "B", "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": 1197, "categoryRank": 11, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 74, "interface": 50, "reach": 65, "risk": 60, "upkeep": 77 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A small, fast transcription cousin for 25 European languages that detects the language and returns punctuated text with word timestamps. It fits in under 1 GB as NVIDIA's GGUF and runs in Transformers, NeMo or NVIDIA's C++ runtime, under CC BY 4.0. Few other runtimes load it, and it takes no language hint.", "bestFor": "Agents that need cheap local transcription with timestamps in English and other European languages", "strengths": [ "Small enough for any machine, 0.71 GB as NVIDIA's Q8_0 GGUF or 2.5 GB in FP32", "25 European languages detected automatically, with punctuation, capitalisation and word, segment and char timestamps", "CC BY 4.0, ungated, files hashed in NVIDIA's own organisation", "Training data listed by corpus with hours, much of it published as Granary", "Kept current, with Transformers support added in 2026 and NVIDIA's C++ runtime changing this month" ], "weaknesses": [ "Among the six runtimes only Transformers runs it; vLLM, SGLang, llama.cpp, Ollama and LM Studio don't", "The NeMo archive, the card's main path, loads a PyTorch pickle", "No language hint or hotwords, and an open report's title says it can drift into other languages without one", "No safety results and only general cautions", "NVIDIA answered 9 of the 40 latest questions, none since February 2026" ], "agentNotes": [ "For the smallest setup, `hf download nvidia/parakeet-tdt-0.6b-v3 parakeet-tdt-0.6b-v3.q8_0.gguf --local-dir models`, then `nemo-speech transcribe speech.wav --model models/parakeet-tdt-0.6b-v3.q8_0.gguf` with NeMo-Speech.cpp", "In Python, `pipeline('automatic-speech-recognition', model='nvidia/parakeet-tdt-0.6b-v3')` works on Transformers 5.10.0 or later, despite the card's note to install from source", "Resample to 16 kHz mono first; it takes wav or flac", "Ask for timestamps with `timestamps=True` in NeMo, or pass `durations=output.durations` to `processor.decode` in Transformers", "For audio longer than about 24 minutes, switch NeMo to local attention with `change_attention_model(\"rel_pos_local_attn\", [256, 256])`", "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 4, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 66.9 } ], "editorialScores": { "capability": 74, "interface": 50, "reach": 65, "risk": 60, "upkeep": 77 }, "provenanceScore": 73, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install -U \"transformers\u003e=5.10\" torch\npython -c \"from transformers import pipeline; print(pipeline('automatic-speech-recognition', model='nvidia/parakeet-tdt-0.6b-v3')('speech.wav'))\"\n# or with NVIDIA's C++ runtime, NeMo-Speech.cpp:\nhf download nvidia/parakeet-tdt-0.6b-v3 parakeet-tdt-0.6b-v3.q8_0.gguf --local-dir models\nnemo-speech transcribe speech.wav --model models/parakeet-tdt-0.6b-v3.q8_0.gguf", "http": "# after starting NeMo-Speech.cpp's server with this model (it binds 127.0.0.1:8080)\ncurl http://127.0.0.1:8080/v1/audio/transcriptions -F file=@speech.wav" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/parakeet-tdt-0-6b-v3" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "cosmos-reason2-2b", "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": "Fast multilingual transcription in 25 European languages with automatic language detection, punctuation and word-level timestamps.", "runtimes": [ "Transformers" ], "builds": [ { "name": "parakeet-tdt-0.6b-v3 (safetensors)", "url": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3/blob/main/model.safetensors", "who": "publisher", "precision": "fp32", "sizeGB": 2.5, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ] }, { "name": "parakeet-tdt-0.6b-v3.nemo", "url": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3/blob/main/parakeet-tdt-0.6b-v3.nemo", "who": "publisher", "precision": "fp32", "sizeGB": 2.5, "memoryGB": 8, "format": "nemo", "runsOn": [ "NeMo" ], "note": "A NeMo archive holding a PyTorch checkpoint (pickle), loaded by NeMo's ASRModel.from_pretrained" }, { "name": "parakeet-tdt-0.6b-v3.q8_0.gguf", "url": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3/blob/main/parakeet-tdt-0.6b-v3.q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 0.71, "memoryGB": 8, "format": "gguf", "runsOn": [ "NeMo-Speech.cpp" ], "note": "For NVIDIA's NeMo-Speech.cpp, not llama.cpp, which has no TDT decoder" } ], "run": "nemo-speech transcribe speech.wav --model models/parakeet-tdt-0.6b-v3.q8_0.gguf", "runBuild": "parakeet-tdt-0.6b-v3.q8_0.gguf", "checked": "2026-10-21", "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_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" }, { "name": "parakeet-unified-en-0.6b", "url": "https://huggingface.co/nvidia/parakeet-unified-en-0.6b", "params": "0.6B" } ], "olderCount": 43 }, "area": "voice", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "https://creativecommons.org/licenses/by/4.0/legalcode.en", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The model sits in NVIDIA's Hugging Face organisation and the card's governing terms are CC BY 4.0. 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, 28 commits from 4 August 2025 to 5 August 2026. Recent messages say what changed (Transformers integration on 16 April and 19 May 2026, an eos_token for vLLM on 19 May, Open ASR Leaderboard results on 29 June, the Q8_0 GGUF on 5 August).", "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, and NeMo-Speech.cpp ships a SECURITY.md pointing to NVIDIA PSIRT." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3.json" }, "answer": "Parakeet TDT 0.6B v3 scores 66.9 (B) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in 3 of 4 scored categories.", "b": { "slug": "underdog-bark-3", "name": "Underdog Bark 3.0", "vendor": "Conway Research", "vendorUrl": "https://conway.tech", "kind": "weights", "category": "speech-to-text", "summary": "Underdog Bark 3.0 is Conway Research's open-weight English speech recognition model, a post-trained and compressed Parakeet TDT 0.6B v3 in one 163 MB file, run with Conway's loader on Transformers, MLX or Core ML, under CC BY 4.0.", "url": "https://www.anchorterminal.com/tools/underdog-bark-3", "markdownUrl": "https://www.anchorterminal.com/tools/underdog-bark-3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/underdog-bark-3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/underdog-bark-3.json", "repo": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0", "license": "CC-BY-4.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ConwayResearch/Underdog-Bark-3.0" } ], "auth": "none", "authNotes": "The files download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). The card's optional line about `hf auth login` applies only if access ever needs it.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under CC BY 4.0, which asks for attribution of NVIDIA, Moondream and Conway (the repository's NOTICE file). Running it costs the hardware. model.bark is 163.4 MB, and the Core ML packages about 0.2 GB each (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/ConwayResearch/Underdog-Bark-3.0", "capabilities": [ "speech.stt", "speech.batch", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "english", "fastconformer", "tdt", "parakeet", "low-bit", "mlx", "core-ml", "apple-silicon", "on-device", "cc-by-4.0", "self-hosted" ], "lastRelease": "2026-10-15", "graded": true, "disclosure": "Underdog Bark 3.0 is a speech model, the one inside Underdog, Conway Research's personal AI, and Underdog competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published open-weight checklist as every open-weight model, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default. underdog.ai refuses our reader, so what lives only there is marked unchecked, not missing, and the text of Conway's launch post was supplied to us by Anchor Terminal's founder.", "competesWith": "localghost", "anchor": { "graded": true, "score": 48.2, "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": 1197, "categoryRank": 23, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 35, "reach": 65, "risk": 40, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": -2, "negativeNotes": [ "A misleading claim, 2 off. Conway's launch post of 21 October 2026, whose text Anchor Terminal's founder supplied because underdog.ai refuses our reader, calls Underdog Bark 3.0 'the #1 frontier speech model'. The Open ASR Leaderboard shows that false on every reading the post's own words allow, even granting Conway's own 4.937 per cent average over the seven public English sets its card uses, which isn't on the leaderboard. As the best speech model, 32 of the leaderboard's 65 rows average lower on those sets. As the best open model, 16 open-weight rows do, led by Hojo-ASR-V1 at 4.291. On AMI, the set the post cites, 19 rows score below its 7.82. And at its own size in the leaderboard's size column, two open models do better, Parakeet TDT 0.6B v2 (0.6B, 4.819) and Granite Speech 5.0 470M NC (0.47B, 4.921). Conway's card claims only state of the art for its size, measured by download, which the leaderboard can't test and is recorded as Conway's claim. 2, the low end of the range, because the card states the narrower position and the post reached us as supplied text (https://huggingface.co/datasets/hf-audio/open-asr-leaderboard-results/raw/main/english_short_latest.csv, results file last changed 19 October 2026)" ], "verdict": "A very small English transcription cousin, one 163 MB file built on NVIDIA's Parakeet TDT that Conway's loader expands into Transformers, MLX or Core ML, under CC BY 4.0. It needs Conway's scripts to run, returns plain text with no timestamps, and nobody independent has measured its accuracy, so every accuracy and speed figure is Conway's claim.", "bestFor": "Agents on Apple silicon or small machines that need English transcripts from a very small download, can run Conway's loader, and can accept accuracy nobody independent has measured", "disclosure": "Underdog Bark 3.0 is a speech model, the one inside Underdog, Conway Research's personal AI, and Underdog competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published open-weight checklist as every open-weight model, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default. underdog.ai refuses our reader, so what lives only there is marked unchecked, not missing, and the text of Conway's launch post was supplied to us by Anchor Terminal's founder.", "strengths": [ "A 163 MB download, about a fifteenth of Parakeet TDT 0.6B v3's 2.5 GB FP32 file, and within 16 GB even expanded to FP32", "CC BY 4.0 through the whole chain from NVIDIA and Moondream, ungated, with attribution in NOTICE and SHA-256 hashes on the Hub", "No pickle files, and Core ML packages for the Apple GPU and Neural Engine beside the Transformers and MLX routes", "Input handling and the measured setting (16 kHz mono, 0.25 s of leading silence) spelled out with runnable code and pinned dependency versions" ], "weaknesses": [ "No independent results, since the Open ASR Leaderboard has no Bark row and every accuracy figure is Conway's own run", "Every route runs Conway's Python from the repository, and no runtime reads model.bark itself", "English only and plain text only, with no timestamps, language hint or hotwords", "No training data described, no safety results, no security contact found, and no Conway page we could read links the Hugging Face organisation", "Expanding it takes about 6 GB of RAM once, and a 10-minute file peaked near 15 GB in stock mlx-audio, per the card" ], "agentNotes": [ "Download only what one route needs, for example `hf download ConwayResearch/Underdog-Bark-3.0 --include \"model.bark\" --include \"bark_loader.py\" --include \"bark_mlx.py\" --include \"mlx/*\" --include \"NOTICE\"`", "With transformers 5.19.0 and constriction 0.5.0, `python bark_loader.py model.bark recording.wav`, or `load_model(\"model.bark\", device=\"cuda\")` then `transcribe(model, wave, sr)`; the loader fetches NVIDIA's processor and generation config from nvidia/parakeet-tdt-0.6b-v3 on first use, so the first run needs the network", "On Apple silicon, `python bark_mlx.py recording.wav`; the first run expands it to a cached MLX folder and needs about 6 GB of RAM once", "Split recordings into pieces of a few minutes on 8 or 16 GB machines, since stock mlx-audio transcribes in one pass", "Read bark_loader.py before running it and pin the repository revision, since loading runs the publisher's code", "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions", "Plan memory for the recording, not the file. Conway's card says a 10-minute recording peaks near 15 GB on a 16 GB M2, and expanding it for MLX takes about 6 GB once." ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 48.2 } ], "editorialScores": { "interface": 35, "reach": 65, "risk": 40, "upkeep": 49 }, "provenanceScore": 27, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install huggingface_hub==2.2.0\nhf download ConwayResearch/Underdog-Bark-3.0 --local-dir bark-3.0 --include \"model.bark\" --include \"bark_loader.py\" --include \"bark_mlx.py\" --include \"mlx/*\" --include \"NOTICE\"\ncd bark-3.0\npip install transformers==5.19.0 torch==2.8.0 constriction==0.5.0 numpy==2.4.6 soundfile==0.14.0 soxr==1.1.0 librosa==0.11.0\npython bark_loader.py model.bark recording.wav\n# on Apple silicon with MLX instead:\npip install mlx==0.32.2 mlx-audio==0.5.3 constriction==0.5.0 numpy==2.4.6 soundfile==0.14.0 soxr==1.1.0\npython bark_mlx.py recording.wav" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/underdog-bark-3" }, "weights": { "trainedBy": "Conway Research", "builtOn": "nvidia/parakeet-tdt-0.6b-v3 (CC BY 4.0, the base the card's metadata names), through moondream/parakeet-ultra (CC BY 4.0), per NOTICE", "params": "0.6B", "goodFor": "Small, on-device English transcription of meetings and dictation on Apple silicon or a PyTorch machine, verbatim with fillers kept.", "runtimes": [ "Transformers" ], "builds": [ { "name": "model.bark", "url": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0/blob/main/model.bark", "who": "publisher", "precision": "low-bit (entropy-coded per-row levels for encoder linear weights, 5 or 6 bit integers for other matrices, fp16 small tensors)", "sizeGB": 0.16, "memoryGB": 8, "format": "bark", "runsOn": [ "Transformers", "mlx-audio" ], "note": "Conway's own format, read only by the repository's bark_loader.py, which expands it to FP32 for Transformers, or by bark_mlx.py, which writes an FP16 or 4-bit MLX folder for mlx-audio. About 2.1 bits a parameter as stored, entropy-coded; the bit width isn't stated The band comes from the file size, as for every build. Conway's card says expanding it for MLX takes about 6 GB once and a 10-minute recording peaks near 15 GB on a 16 GB M2, so long recordings need more than the band." }, { "name": "Core ML (Apple GPU)", "url": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0/tree/main/coreml/gpu", "who": "publisher", "precision": "the same quantised values as model.bark, FP16 encoder arithmetic", "sizeGB": 0.2, "memoryGB": 8, "format": "coreml", "runsOn": [ "Core ML" ], "note": "BarkEncoder.mlpackage with a fixed 15-second window, decoder.npz and vocab.json, run by coreml/gpu/bark_coreml.py" }, { "name": "Core ML (Apple Neural Engine)", "url": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0/tree/main/coreml/ane", "who": "publisher", "precision": "the same quantised values as model.bark, FP16 encoder arithmetic", "sizeGB": 0.2, "memoryGB": 8, "format": "coreml", "runsOn": [ "Core ML" ], "note": "BarkEncoder.mlpackage with 8 and 15 second functions sharing weights, decoder.npz and vocab.json, run by coreml/ane/bark_coreml.py" } ], "run": "python bark_loader.py model.bark recording.wav", "runBuild": "model.bark", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Conway Research", "family": "Underdog Bark", "url": "https://www.anchorterminal.com/open-weights/#conway" }, "area": "voice", "provenance": { "legalEntity": "", "domain": "conway.tech", "domainRegistered": "2025-11-08", "endpointOnVendorDomain": null, "terms": "https://creativecommons.org/licenses/by/4.0/legalcode.en", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The model sits in the ConwayResearch organisation on Hugging Face, named Conway and unverified, whose Bark 3.0 commits both come from the Hugging Face account of Sigil Wen, whom sigilwen.ca/conway names as Conway's founder. conway.tech (page and script), sigilwen.ca/conway and Conway-Research/automaton's README don't link the organisation, and underdog.ai, which the card links, refuses our reader, so a link from Conway's own site is unchecked.", "No registered company name found, as in the Underdog listing of 3 October 2026. The Hugging Face organisation and conway.tech both say Conway. RDAP gives conway.tech a registration date of 8 November 2025 (rechecked 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, two commits on 15 October 2026 (initial commit and Underdog Bark 3.0). No release notes outside underdog.ai were found.", "conway.tech/.well-known/security.txt answered 404 on 21 October 2026, and the repository has no security file. underdog.ai is unchecked." ], "score": 27 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/underdog-bark-3.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "NVIDIA", "b": "Conway Research", "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": "CC-BY-4.0", "b": "CC-BY-4.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-05", "b": "2026-10-15", "name": "Last release" }, { "a": "", "b": "", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "4/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Parakeet TDT 0.6B v3 scores 66.9 (B) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, Parakeet TDT 0.6B v3 or Underdog Bark 3.0?" } ], "goodFor": [ { "aheadOn": [ "Risk, 60 against 40", "Agent interface, 50 against 35", "Documentation and upkeep, 77 against 49" ], "also": [ "Rated higher by the reviewer agents, 4.0 against 2.8 out of 5" ], "goodFor": "Agents that need cheap local transcription with timestamps in English and other European languages", "slug": "parakeet-tdt-0-6b-v3", "watchFor": "Among the six runtimes only Transformers runs it; vLLM, SGLang, llama.cpp, Ollama and LM Studio don't" }, { "aheadOn": null, "also": null, "goodFor": "Agents on Apple silicon or small machines that need English transcripts from a very small download, can run Conway's loader, and can accept accuracy nobody independent has measured", "slug": "underdog-bark-3", "watchFor": "No independent results, since the Open ASR Leaderboard has no Bark row and every accuracy figure is Conway's own run" } ], "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-parakeet-tdt-0-6b-v3.json", "title": "Cohere Transcribe 03-2026 vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3.json", "title": "Cohere Transcribe 03-2026 vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-parakeet-tdt-0-6b-v3.json", "title": "Ming-3.0-asr-tiny vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3.json", "title": "Ming-3.0-asr-tiny vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-parakeet-tdt-0-6b-v3.json", "title": "Ming-3.0-audio-flash vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-underdog-bark-3.json", "title": "Ming-3.0-audio-flash vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3.json", "title": "Ming-3.0-audio-tiny vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3.json", "title": "Ming-3.0-audio-tiny vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Parakeet TDT 0.6B v3", "url": "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-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-nano-omni-30b-a3b-reasoning-vs-parakeet-tdt-0-6b-v3.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-0-6b.json", "title": "Parakeet TDT 0.6B v3 vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-1-7b.json", "title": "Parakeet TDT 0.6B v3 vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Parakeet TDT 0.6B v3 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-voxtral-mini-4b-realtime-2602.json", "title": "Parakeet TDT 0.6B v3 vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-voxtral-mini-4b-realtime-2602" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-underdog-bark-3.json", "title": "Qwen3-ASR-0.6B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-underdog-bark-3.json", "title": "Qwen3-ASR-1.7B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-underdog-bark-3.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.json", "title": "Underdog Bark 3.0 vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 0, "edge": "", "key": "reach", "name": "Reach", "parakeet-tdt-0-6b-v3": 65, "underdog-bark-3": 65, "weight": 25 }, { "by": 20, "edge": "parakeet-tdt-0-6b-v3", "key": "risk", "name": "Risk", "parakeet-tdt-0-6b-v3": 60, "underdog-bark-3": 40, "weight": 20 }, { "by": 15, "edge": "parakeet-tdt-0-6b-v3", "key": "interface", "name": "Agent interface", "parakeet-tdt-0-6b-v3": 50, "underdog-bark-3": 35, "weight": 10 }, { "by": 28, "edge": "parakeet-tdt-0-6b-v3", "key": "upkeep", "name": "Documentation and upkeep", "parakeet-tdt-0-6b-v3": 77, "underdog-bark-3": 49, "weight": 10 } ], "summary": "Parakeet TDT 0.6B v3 scores 66.9 (B) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in 3 of 4 scored categories. Both do speech-to-text.", "tutorials": [], "verdicts": { "parakeet-tdt-0-6b-v3": "A small, fast transcription cousin for 25 European languages that detects the language and returns punctuated text with word timestamps. It fits in under 1 GB as NVIDIA's GGUF and runs in Transformers, NeMo or NVIDIA's C++ runtime, under CC BY 4.0. Few other runtimes load it, and it takes no language hint.", "underdog-bark-3": "A very small English transcription cousin, one 163 MB file built on NVIDIA's Parakeet TDT that Conway's loader expands into Transformers, MLX or Core ML, under CC BY 4.0. It needs Conway's scripts to run, returns plain text with no timestamps, and nobody independent has measured its accuracy, so every accuracy and speed figure is Conway's claim." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3", "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3.min.md" }, "markdown": "Parakeet TDT 0.6B v3 scores 66.9 (B) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in 3 of 4 scored categories. Both do speech-to-text.\n\n- Parakeet TDT 0.6B v3: grade B, 66.9/100, rank #13 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3.md · JSON https://www.anchorterminal.com/api/v1/tools/parakeet-tdt-0-6b-v3.json\n- Underdog Bark 3.0: grade D, 48.2/100, rank #96 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/underdog-bark-3.md · JSON https://www.anchorterminal.com/api/v1/tools/underdog-bark-3.json\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 231 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n\n## Which one, for what\n\n### Parakeet TDT 0.6B v3 (B)\n\nGood for: Agents that need cheap local transcription with timestamps in English and other European languages\n\nAhead on:\n- Risk, 60 against 40\n- Agent interface, 50 against 35\n- Documentation and upkeep, 77 against 49\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 4.0 against 2.8 out of 5\n\nWatch for: Among the six runtimes only Transformers runs it; vLLM, SGLang, llama.cpp, Ollama and LM Studio don't\n\n### Underdog Bark 3.0 (D)\n\nGood for: Agents on Apple silicon or small machines that need English transcripts from a very small download, can run Conway's loader, and can accept accuracy nobody independent has measured\n\nWatch for: No independent results, since the Open ASR Leaderboard has no Bark row and every accuracy figure is Conway's own run\n\n\n## Score by category\n\n| Category | Weight | Parakeet TDT 0.6B v3 | Underdog Bark 3.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 65 | even |\n| Risk | 20% (20 this run) | 60 | 40 | Parakeet TDT 0.6B v3 +20 |\n| Agent interface | 10% (10 this run) | 50 | 35 | Parakeet TDT 0.6B v3 +15 |\n| Documentation and upkeep | 10% (10 this run) | 77 | 49 | Parakeet TDT 0.6B v3 +28 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **66.9 · B** | **48.2 · D** | |\n\n## Facts side by side\n\n| Fact | Parakeet TDT 0.6B v3 | Underdog Bark 3.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | Conway Research |\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 | CC-BY-4.0 | CC-BY-4.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-05 | 2026-10-15 |\n| Terms last updated | | |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 4/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Parakeet TDT 0.6B v3.** A small, fast transcription cousin for 25 European languages that detects the language and returns punctuated text with word timestamps. It fits in under 1 GB as NVIDIA's GGUF and runs in Transformers, NeMo or NVIDIA's C++ runtime, under CC BY 4.0. Few other runtimes load it, and it takes no language hint.\n\n**Underdog Bark 3.0.** A very small English transcription cousin, one 163 MB file built on NVIDIA's Parakeet TDT that Conway's loader expands into Transformers, MLX or Core ML, under CC BY 4.0. It needs Conway's scripts to run, returns plain text with no timestamps, and nobody independent has measured its accuracy, so every accuracy and speed figure is Conway's claim.\n\n## Before you call either\n\n### Parakeet TDT 0.6B v3\n\n1. For the smallest setup, `hf download nvidia/parakeet-tdt-0.6b-v3 parakeet-tdt-0.6b-v3.q8_0.gguf --local-dir models`, then `nemo-speech transcribe speech.wav --model models/parakeet-tdt-0.6b-v3.q8_0.gguf` with NeMo-Speech.cpp\n2. In Python, `pipeline('automatic-speech-recognition', model='nvidia/parakeet-tdt-0.6b-v3')` works on Transformers 5.10.0 or later, despite the card's note to install from source\n3. Resample to 16 kHz mono first; it takes wav or flac\n4. Ask for timestamps with `timestamps=True` in NeMo, or pass `durations=output.durations` to `processor.decode` in Transformers\n5. For audio longer than about 24 minutes, switch NeMo to local attention with `change_attention_model(\"rel_pos_local_attn\", [256, 256])`\n6. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions\n\n### Underdog Bark 3.0\n\n1. Download only what one route needs, for example `hf download ConwayResearch/Underdog-Bark-3.0 --include \"model.bark\" --include \"bark_loader.py\" --include \"bark_mlx.py\" --include \"mlx/*\" --include \"NOTICE\"`\n2. With transformers 5.19.0 and constriction 0.5.0, `python bark_loader.py model.bark recording.wav`, or `load_model(\"model.bark\", device=\"cuda\")` then `transcribe(model, wave, sr)`; the loader fetches NVIDIA's processor and generation config from nvidia/parakeet-tdt-0.6b-v3 on first use, so the first run needs the network\n3. On Apple silicon, `python bark_mlx.py recording.wav`; the first run expands it to a cached MLX folder and needs about 6 GB of RAM once\n4. Split recordings into pieces of a few minutes on 8 or 16 GB machines, since stock mlx-audio transcribes in one pass\n5. Read bark_loader.py before running it and pin the repository revision, since loading runs the publisher's code\n6. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions\n7. Plan memory for the recording, not the file. Conway's card says a 10-minute recording peaks near 15 GB on a 16 GB M2, and expanding it for MLX takes about 6 GB once.\n\n## Questions\n\n### Which is better for AI agents, Parakeet TDT 0.6B v3 or Underdog Bark 3.0?\n\nParakeet TDT 0.6B v3 scores 66.9 (B) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"parakeet-tdt-0-6b-v3\", \"b\": \"underdog-bark-3\"}`. From a terminal: `anchor compare parakeet-tdt-0-6b-v3 underdog-bark-3`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/parakeet-tdt-0-6b-v3.json and https://www.anchorterminal.com/api/v1/tools/underdog-bark-3.json\n\n## Other comparisons with Parakeet TDT 0.6B v3 or Underdog Bark 3.0\n\n- [Cohere Transcribe 03-2026 vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-parakeet-tdt-0-6b-v3.md)\n- [Cohere Transcribe 03-2026 vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3.md)\n- [Ming-3.0-asr-tiny vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-parakeet-tdt-0-6b-v3.md)\n- [Ming-3.0-asr-tiny vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3.md)\n- [Ming-3.0-audio-flash vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-parakeet-tdt-0-6b-v3.md)\n- [Ming-3.0-audio-flash vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-underdog-bark-3.md)\n- [Ming-3.0-audio-tiny vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3.md)\n- [Ming-3.0-audio-tiny vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-parakeet-tdt-0-6b-v3.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3.md)\n- [Parakeet TDT 0.6B v3 vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-0-6b.md)\n- [Parakeet TDT 0.6B v3 vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-1-7b.md)\n- [Parakeet TDT 0.6B v3 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Parakeet TDT 0.6B v3 vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-voxtral-mini-4b-realtime-2602.md)\n- [Qwen3-ASR-0.6B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-underdog-bark-3.md)\n- [Qwen3-ASR-1.7B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-underdog-bark-3.md)\n- [Qwen3-Omni-30B-A3B-Instruct vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-underdog-bark-3.md)\n- [Underdog Bark 3.0 vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.md)\n\n## Disclosure\n\n- Underdog Bark 3.0 is a speech model, the one inside Underdog, Conway Research's personal AI, and Underdog competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published open-weight checklist as every open-weight model, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default. underdog.ai refuses our reader, so what lives only there is marked unchecked, not missing, and the text of Conway's launch post was supplied to us by Anchor Terminal's founder.\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Parakeet TDT 0.6B v3 vs Underdog Bark 3.0", "url": "" } ], "description": "Parakeet TDT 0.6B v3 scores 66.9 (B) to Underdog Bark 3.0's 48.2 (D) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Parakeet TDT 0.6B v3 B 66.9", "Underdog Bark 3.0 D 48.2", "scores" ], "h1": "Parakeet TDT 0.6B v3 vs Underdog Bark 3.0", "image": "https://www.anchorterminal.com/assets/og/compare-parakeet-tdt-0-6b-v3-vs-underdog-bark-3.png", "path": "/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3", "published": "2026-10-01", "section": "tools", "title": "Parakeet TDT 0.6B v3 vs Underdog Bark 3.0 for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3" }, "tokens": { "markdown": 2750, "slim": 430 }, "version": 1 }