{ "data": { "a": { "slug": "cohere-transcribe-03-2026", "name": "Cohere Transcribe 03-2026", "vendor": "Cohere", "vendorUrl": "https://cohere.com", "kind": "weights", "category": "speech-to-text", "summary": "Cohere Transcribe 03-2026 is Cohere's open-weight speech recognition model, a 2B Conformer encoder with a light Transformer decoder trained from scratch, transcribing 14 languages from a pre-specified language, under Apache 2.0, in a gated Hugging Face repository.", "url": "https://www.anchorterminal.com/tools/cohere-transcribe-03-2026", "markdownUrl": "https://www.anchorterminal.com/tools/cohere-transcribe-03-2026.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cohere-transcribe-03-2026.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cohere-transcribe-03-2026.json", "repo": "https://huggingface.co/CohereLabs/cohere-transcribe-03-2026", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "CohereLabs/cohere-transcribe-03-2026" } ], "auth": "none", "authNotes": "The repository is gated (gated: auto on 2026-10-21). The card and file list are public, but the files open only after a signed-in Hugging Face user accepts the conditions, which are approved automatically. We didn't accept them, so this listing is graded from what's public.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, once the gate is accepted. Cohere also serves the model on its Audio Transcriptions API (free with rate limits on a trial key, or a dedicated Model Vault priced per hour-instance), and fal serves it as a Hugging Face inference provider. Running the weights costs the hardware, 4.13 GB of BF16 safetensors (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://docs.cohere.com/docs/transcribe", "llmsTxt": "https://docs.cohere.com/llms.txt", "capabilities": [ "speech.stt", "speech.batch", "speech.languages", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "conformer", "multilingual", "apache-2.0", "safetensors", "gated", "self-hosted" ], "lastRelease": "2026-04-28", "graded": true, "anchor": { "graded": true, "score": 59.3, "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": 1197, "categoryRank": 18, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 66, "interface": 45, "reach": 65, "risk": 50, "upkeep": 54 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "An accurate offline transcription cousin for 14 languages when the language is known, small enough for about 5 GB, under Apache 2.0, and also on Cohere's API. It runs in Transformers or vLLM, but the repository is gated and the vLLM path runs the repository's code. Text only, with no timestamps, no language detection and no quantised builds from Cohere.", "bestFor": "Agents that need accurate batch transcription of known-language audio in 14 languages on a small GPU", "strengths": [ "Publisher-reported 5.42 average WER on the Open ASR Leaderboard's English sets, with results in the repository to check", "2B parameters, 4.13 GB of BF16 safetensors, about 5 GB in memory", "Native in released Transformers and vLLM, with long-form chunking and batching documented", "Apache 2.0 with nothing added, and Cohere's own API serves the same model", "Every report of a broken file got a reply from Cohere" ], "weaknesses": [ "Gated, so an agent needs a Hugging Face account and must accept the conditions before downloading", "The documented vLLM command uses --trust-remote-code", "No timestamps, diarisation, language detection or JSON output; one language must be given", "No quantised build from Cohere and no llama.cpp, Ollama or LM Studio support", "No safety results, training data without sources or cut-off, and no replies in the repository since April" ], "agentNotes": [ "A person has to accept the conditions on the Hugging Face page with an account, then `hf auth login`, before the files download", "Offline, `pip install \"transformers\u003e=5.4.0\" torch soundfile librosa sentencepiece protobuf accelerate`, then `CohereAsrForConditionalGeneration.from_pretrained(\"CohereLabs/cohere-transcribe-03-2026\", device_map=\"auto\")` with no remote code", "Always pass language= (en, de, fr, it, es, pt, el, nl, pl, zh, ja, ko, vi, ar); there's no auto-detection", "For long audio, keep audio_chunk_index from the processor and pass it to processor.decode to reassemble the chunks", "Serving, `vllm serve CohereLabs/cohere-transcribe-03-2026 --trust-remote-code`, then POST to /v1/audio/transcriptions", "Put voice activity detection in front of it, since it transcribes noise and can hallucinate on silence", "Without the hardware or the gate, Cohere's API serves the same model as cohere-transcribe-03-2026", "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.3 } ], "editorialScores": { "capability": 66, "interface": 45, "reach": 65, "risk": 50, "upkeep": 54 }, "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": "# accept the conditions on the Hugging Face page first, then log in with hf auth login\npip install \"transformers\u003e=5.4.0\" torch soundfile librosa sentencepiece protobuf accelerate", "http": "# after: vllm serve CohereLabs/cohere-transcribe-03-2026 --trust-remote-code\ncurl -X POST http://localhost:8000/v1/audio/transcriptions \\\n -F file=@/path/to/speech.wav \\\n -F model=CohereLabs/cohere-transcribe-03-2026" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/cohere-transcribe-03-2026" }, "sameCompany": [ "cohere-chat", "cohere-embed", "cohere-north" ], "weights": { "trainedBy": "Cohere (Cohere Labs)", "params": "2.07B", "goodFor": "Accurate offline and batch transcription in 14 languages when the language is known, on a small GPU.", "runtimes": [ "vLLM", "Transformers" ], "builds": [ { "name": "cohere-transcribe-03-2026", "url": "https://huggingface.co/CohereLabs/cohere-transcribe-03-2026", "who": "publisher", "precision": "bf16", "sizeGB": 4.13, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "Transformers" ], "note": "Gated, a click-through with automatic approval that needs a Hugging Face account" } ], "run": "vllm serve CohereLabs/cohere-transcribe-03-2026 --trust-remote-code", "runBuild": "cohere-transcribe-03-2026", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Cohere (Cohere Labs)", "family": "Cohere Transcribe", "url": "https://www.anchorterminal.com/open-weights/#cohere", "others": [ { "name": "cohere-transcribe-arabic-07-2026", "url": "https://huggingface.co/CohereLabs/cohere-transcribe-arabic-07-2026", "params": "2.07B (Hub safetensors count; the name states none)" } ], "olderCount": 29 }, "area": "voice", "provenance": { "legalEntity": "Cohere Inc.", "domain": "cohere.com", "domainRegistered": "2000-03-07", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://docs.cohere.com/changelog/cohere-transcribe-03-2026", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card says Developed by Cohere and Cohere Labs. The files sit in the verified CohereLabs organisation, which cohere.com's Transcribe announcement links to. Legal entity and domain registration are carried from the cohere-chat listing (checked 2026-10-09).", "Nothing is hosted in this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. Cohere's own API serves the model; no Cohere transcription API is listed here yet.", "The repository's commit history is behind the gate. Per-file last commits from the public file list show the weights from 25 March 2026, tokenizer fixes on 27 March, a modeling fix on 26 March, a configuration change on 28 April and card edits up to 7 October 2026.", "cohere.com/.well-known/security.txt answered 404 on 21 October 2026. The cohere-chat listing records a responsible disclosure policy and bug bounty in Cohere's trust centre." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/cohere-transcribe-03-2026.json" }, "answer": "Cohere Transcribe 03-2026 scores 59.3 (C) 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": "Cohere", "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": "Apache-2.0", "b": "CC-BY-4.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-04-28", "b": "2026-10-15", "name": "Last release" }, { "a": "no document linked", "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": "3/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Cohere Transcribe 03-2026 scores 59.3 (C) 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, Cohere Transcribe 03-2026 or Underdog Bark 3.0?" } ], "goodFor": [ { "aheadOn": [ "Risk, 50 against 40", "Agent interface, 45 against 35", "Documentation and upkeep, 54 against 49" ], "also": null, "goodFor": "Agents that need accurate batch transcription of known-language audio in 14 languages on a small GPU", "slug": "cohere-transcribe-03-2026", "watchFor": "Gated, so an agent needs a Hugging Face account and must accept the conditions before downloading" }, { "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-ming-3-0-asr-tiny.json", "title": "Cohere Transcribe 03-2026 vs Ming-3.0-asr-tiny", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash.json", "title": "Cohere Transcribe 03-2026 vs Ming-3.0-audio-flash", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-tiny.json", "title": "Cohere Transcribe 03-2026 vs Ming-3.0-audio-tiny", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-tiny" }, { "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-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Cohere Transcribe 03-2026 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "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-qwen3-asr-0-6b.json", "title": "Cohere Transcribe 03-2026 vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-1-7b.json", "title": "Cohere Transcribe 03-2026 vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Cohere Transcribe 03-2026 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-voxtral-mini-4b-realtime-2602.json", "title": "Cohere Transcribe 03-2026 vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-voxtral-mini-4b-realtime-2602" }, { "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-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-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-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-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-underdog-bark-3.json", "title": "Parakeet TDT 0.6B v3 vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3" }, { "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, "cohere-transcribe-03-2026": 65, "edge": "", "key": "reach", "name": "Reach", "underdog-bark-3": 65, "weight": 25 }, { "by": 10, "cohere-transcribe-03-2026": 50, "edge": "cohere-transcribe-03-2026", "key": "risk", "name": "Risk", "underdog-bark-3": 40, "weight": 20 }, { "by": 10, "cohere-transcribe-03-2026": 45, "edge": "cohere-transcribe-03-2026", "key": "interface", "name": "Agent interface", "underdog-bark-3": 35, "weight": 10 }, { "by": 5, "cohere-transcribe-03-2026": 54, "edge": "cohere-transcribe-03-2026", "key": "upkeep", "name": "Documentation and upkeep", "underdog-bark-3": 49, "weight": 10 } ], "summary": "Cohere Transcribe 03-2026 scores 59.3 (C) 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": { "cohere-transcribe-03-2026": "An accurate offline transcription cousin for 14 languages when the language is known, small enough for about 5 GB, under Apache 2.0, and also on Cohere's API. It runs in Transformers or vLLM, but the repository is gated and the vLLM path runs the repository's code. Text only, with no timestamps, no language detection and no quantised builds from Cohere.", "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/cohere-transcribe-03-2026-vs-underdog-bark-3", "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3.min.md" }, "markdown": "Cohere Transcribe 03-2026 scores 59.3 (C) 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- Cohere Transcribe 03-2026: grade C, 59.3/100, rank #61 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/cohere-transcribe-03-2026.md · JSON https://www.anchorterminal.com/api/v1/tools/cohere-transcribe-03-2026.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### Cohere Transcribe 03-2026 (C)\n\nGood for: Agents that need accurate batch transcription of known-language audio in 14 languages on a small GPU\n\nAhead on:\n- Risk, 50 against 40\n- Agent interface, 45 against 35\n- Documentation and upkeep, 54 against 49\n\nWatch for: Gated, so an agent needs a Hugging Face account and must accept the conditions before downloading\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 | Cohere Transcribe 03-2026 | 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) | 50 | 40 | Cohere Transcribe 03-2026 +10 |\n| Agent interface | 10% (10 this run) | 45 | 35 | Cohere Transcribe 03-2026 +10 |\n| Documentation and upkeep | 10% (10 this run) | 54 | 49 | Cohere Transcribe 03-2026 +5 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **59.3 · C** | **48.2 · D** | |\n\n## Facts side by side\n\n| Fact | Cohere Transcribe 03-2026 | Underdog Bark 3.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Cohere | 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 | Apache-2.0 | CC-BY-4.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-04-28 | 2026-10-15 |\n| Terms last updated | no document linked | |\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 | 3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Cohere Transcribe 03-2026.** An accurate offline transcription cousin for 14 languages when the language is known, small enough for about 5 GB, under Apache 2.0, and also on Cohere's API. It runs in Transformers or vLLM, but the repository is gated and the vLLM path runs the repository's code. Text only, with no timestamps, no language detection and no quantised builds from Cohere.\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### Cohere Transcribe 03-2026\n\n1. A person has to accept the conditions on the Hugging Face page with an account, then `hf auth login`, before the files download\n2. Offline, `pip install \"transformers\u003e=5.4.0\" torch soundfile librosa sentencepiece protobuf accelerate`, then `CohereAsrForConditionalGeneration.from_pretrained(\"CohereLabs/cohere-transcribe-03-2026\", device_map=\"auto\")` with no remote code\n3. Always pass language= (en, de, fr, it, es, pt, el, nl, pl, zh, ja, ko, vi, ar); there's no auto-detection\n4. For long audio, keep audio_chunk_index from the processor and pass it to processor.decode to reassemble the chunks\n5. Serving, `vllm serve CohereLabs/cohere-transcribe-03-2026 --trust-remote-code`, then POST to /v1/audio/transcriptions\n6. Put voice activity detection in front of it, since it transcribes noise and can hallucinate on silence\n7. Without the hardware or the gate, Cohere's API serves the same model as cohere-transcribe-03-2026\n8. 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, Cohere Transcribe 03-2026 or Underdog Bark 3.0?\n\nCohere Transcribe 03-2026 scores 59.3 (C) 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/cohere-transcribe-03-2026-vs-underdog-bark-3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cohere-transcribe-03-2026\", \"b\": \"underdog-bark-3\"}`. From a terminal: `anchor compare cohere-transcribe-03-2026 underdog-bark-3`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cohere-transcribe-03-2026.json and https://www.anchorterminal.com/api/v1/tools/underdog-bark-3.json\n\n## Other comparisons with Cohere Transcribe 03-2026 or Underdog Bark 3.0\n\n- [Cohere Transcribe 03-2026 vs Ming-3.0-asr-tiny](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.md)\n- [Cohere Transcribe 03-2026 vs Ming-3.0-audio-flash](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash.md)\n- [Cohere Transcribe 03-2026 vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-tiny.md)\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 Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\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 Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-0-6b.md)\n- [Cohere Transcribe 03-2026 vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-1-7b.md)\n- [Cohere Transcribe 03-2026 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Cohere Transcribe 03-2026 vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-voxtral-mini-4b-realtime-2602.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 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 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 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 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 Underdog Bark 3.0](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3.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": "Cohere Transcribe 03-2026 vs Underdog Bark 3.0", "url": "" } ], "description": "Cohere Transcribe 03-2026 scores 59.3 (C) to Underdog Bark 3.0's 48.2 (D) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Cohere Transcribe 03-2026 C 59.3", "Underdog Bark 3.0 D 48.2", "scores" ], "h1": "Cohere Transcribe 03-2026 vs Underdog Bark 3.0", "image": "https://www.anchorterminal.com/assets/og/compare-cohere-transcribe-03-2026-vs-underdog-bark-3.png", "path": "/compare/cohere-transcribe-03-2026-vs-underdog-bark-3", "published": "2026-10-01", "section": "tools", "title": "Cohere Transcribe 03-2026 vs Underdog Bark 3.0 for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3" }, "tokens": { "markdown": 2800, "slim": 480 }, "version": 1 }