{ "data": { "a": { "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": 1199, "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", "live": { "slug": "underdog-bark-3", "securityTxt": { "url": "https://conway.tech/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-22T16:00:14.806702546Z" }, "pages": [ { "url": "https://huggingface.co/ConwayResearch/Underdog-Bark-3.0/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-22T01:18:55.48268005Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "22e004a78267" } ], "updatedAt": "2026-10-22T16:00:14.806702546Z" } }, "answer": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in every scored category.", "b": { "slug": "voxtral-mini-4b-realtime-2602", "name": "Voxtral Mini 4B Realtime 2602", "vendor": "Mistral AI", "vendorUrl": "https://mistral.ai", "kind": "weights", "category": "speech-to-text", "summary": "Voxtral Mini 4B Realtime 2602 is Mistral AI's open-weight streaming speech recognition model, a causal audio encoder of about 970M parameters on a Ministral 3B decoder, 4.4B in all, transcribing 13 languages live under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602", "markdownUrl": "https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/voxtral-mini-4b-realtime-2602.json", "repo": "https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "mistralai/Voxtral-Mini-4B-Realtime-2602" } ], "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), though its metadata carries a gating description pointing to Mistral's privacy policy.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Mistral also serves the same model on its API as voxtral-mini-transcribe-realtime-2602 at $0.006 per audio minute, listed separately as mistral-voxtral-transcribe. Running the weights costs the hardware, 8.86 GB of BF16 safetensors on a GPU with 16 GB or more, per the card (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/mistralai/Voxtral-Mini-4B-Realtime-2602", "capabilities": [ "speech.stt", "speech.streaming", "speech.languages", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "streaming", "realtime", "multilingual", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-03-11", "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": 1199, "categoryRank": 16, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 46, "interface": 55, "reach": 85, "risk": 60, "upkeep": 54 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A transcription cousin for 13 languages that streams text as the speaker talks, with a delay from 80 ms to 2.4 s, under Apache 2.0 and on Mistral's API. It needs a 16 GB GPU at BF16 through vLLM, Transformers or llama.cpp, or under 4 GB at Q4_K. The main gaps are no timestamps, safety results or answers from Mistral.", "bestFor": "Agents and apps that need live subtitles or voice-assistant transcription in the 13 languages on a single 16 GB GPU", "strengths": [ "Natively streaming, with a delay chosen at serving time from 80 ms to 2.4 s", "Runs in released vLLM, Transformers and llama.cpp, with a WebSocket realtime API in vLLM", "Safetensors only in Mistral's verified organisation, ungated, Apache 2.0", "Fits a 16 GB GPU at BF16, with int4 and Q4_K builds in Mistral's experimental organisation", "The same model is on Mistral's API for agents without a GPU" ], "weaknesses": [ "Plain text out; no timestamps, segments, diarisation or JSON documented for the weights", "No safety results, no agent guidance, and training data described in one line", "Mistral hasn't answered a discussion since February 2026, including a report that the Transformers path fails", "Not in SGLang, Ollama or LM Studio, and the quantised builds are for ExecuTorch and audio.cpp", "The card contradicts itself on supported runtimes and the delay range" ], "agentNotes": [ "Serve it with `VLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{\"cudagraph_mode\": \"PIECEWISE\"}'` and install soxr, librosa and soundfile; mistral_common 1.9.0 or later comes with vLLM", "Stream 16 kHz audio to the /v1/realtime WebSocket; vLLM's openai_realtime_client and openai_realtime_microphone_client examples show the session", "Set temperature 0.0. Change the delay with transcription_delay_ms in tekken.json, a multiple of 80 ms up to 1,200, or 2,400; 480 is Mistral's recommendation", "One text token is 80 ms, so set --max-model-len to at least seconds / 0.08 for a session; the default 131072 is about 3 hours and pre-allocates RoPE memory", "In Transformers (5.2.0 or later), resample with processor.feature_extractor.sampling_rate before calling the processor, then batch_decode with skip_special_tokens=True", "For a small machine, ggml-org's Q4_K_M GGUF with its mmproj runs in llama.cpp, and Mistral Experimental's Q4_K runs in audio.cpp", "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.3 } ], "editorialScores": { "capability": 46, "interface": 55, "reach": 85, "risk": 60, "upkeep": 54 }, "provenanceScore": 81, "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": "uv pip install -U vllm soxr librosa soundfile\nVLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{\"cudagraph_mode\": \"PIECEWISE\"}'", "http": "# after vllm serve: stream 16 kHz PCM audio to the realtime WebSocket\n# ws://127.0.0.1:8000/v1/realtime (see vLLM's openai_realtime_client example)" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/voxtral-mini-4b-realtime-2602" }, "sameCompany": [ "mistral-api", "mistral-embeddings", "mistral-moderation", "mistral-ocr", "mistral-vibe-cli" ], "sameModel": [ "mistral-voxtral-transcribe" ], "weights": { "trainedBy": "Mistral AI", "builtOn": "mistralai/Ministral-3-3B-Base-2512 (Mistral's own, Apache 2.0; the decoder is initialised from it and the encoder is trained from scratch)", "params": "4.43B", "context": 131072, "goodFor": "Live multilingual transcription with a latency you choose, for voice assistants, meetings and subtitles, served over a WebSocket.", "runtimes": [ "llama.cpp", "vLLM", "Transformers" ], "builds": [ { "name": "Voxtral-Mini-4B-Realtime-2602-ExecuTorch (model-metal-int4.pte)", "url": "https://huggingface.co/mistral-experimental/Voxtral-Mini-4B-Realtime-2602-ExecuTorch", "who": "publisher", "precision": "int4", "sizeGB": 4.44, "memoryGB": 8, "format": "executorch", "note": "Mistral Experimental, linked from the card; ExecuTorch Metal build for Apple devices, marked untested. Size includes tekken.json" }, { "name": "AudioCPP-Voxtral-Mini-4B-Realtime-2602-GGUF q4_k", "url": "https://huggingface.co/mistral-experimental/AudioCPP-Voxtral-Mini-4B-Realtime-2602-GGUF", "who": "publisher", "precision": "q4_k", "sizeGB": 3.1, "memoryGB": 8, "format": "gguf", "note": "Mistral Experimental; for audio.cpp, a third-party runtime, not llama.cpp. A Q8_0 file of 5.1 GB sits beside it" }, { "name": "Voxtral-Mini-4B-Realtime-2602", "url": "https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602", "who": "publisher", "precision": "bf16", "sizeGB": 8.86, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "Transformers" ], "note": "The repository holds two copies, consolidated.safetensors for vLLM and model.safetensors for Transformers, 17.7 GB in all; size is one copy" } ], "run": "VLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{\"cudagraph_mode\": \"PIECEWISE\"}'", "runBuild": "Voxtral-Mini-4B-Realtime-2602", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Mistral AI", "family": "Voxtral", "url": "https://www.anchorterminal.com/open-weights/#mistral", "others": [ { "name": "Voxtral-Mini-4B-Realtime-Arabic", "url": "https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-Arabic", "params": "4B (Hub safetensors count 4.43B)" }, { "name": "Voxtral-4B-TTS-2603", "url": "https://huggingface.co/mistralai/Voxtral-4B-TTS-2603", "params": "4B" }, { "name": "Voxtral-Small-24B-2507", "url": "https://huggingface.co/mistralai/Voxtral-Small-24B-2507", "params": "24B (Hub safetensors count 24.26B)" }, { "name": "Voxtral-Mini-3B-2507", "url": "https://huggingface.co/mistralai/Voxtral-Mini-3B-2507", "params": "3B (Hub safetensors count 4.68B)" } ], "olderCount": 49 }, "area": "voice", "provenance": { "legalEntity": "Mistral AI (RCS Paris 952 418 325)", "domain": "mistral.ai", "domainRegistered": "2019-05-15", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "The files sit in Mistral's verified mistralai organisation, and Mistral's announcement links to them. Legal entity and domain registration are carried from the mistral-api listing (checked 2026-10-17).", "Nothing is hosted in this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. The hosted model is mistral-voxtral-transcribe.", "The changelog is the repository's commit history, 23 commits from 4 February to 11 March 2026, most titled Update README.md.", "mistral.ai/.well-known/security.txt answered on 21 October 2026 with a security contact, an expiry of 5 May 2027 and a HackerOne submission policy.", "The card's ExecuTorch link, under mistral-labs, redirects to the unverified mistral-experimental organisation (Mistral Experimental), which also holds audio.cpp GGUF builds; counted as the publisher's linked organisation." ], "score": 81 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602.json", "live": { "slug": "voxtral-mini-4b-realtime-2602", "securityTxt": { "url": "https://mistral.ai/.well-known/security.txt", "state": "valid", "expires": "2027-05-05T23:59:59.000Z", "checkedAt": "2026-10-22T16:01:04.264973849Z" }, "pages": [ { "url": "https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-22T01:19:35.968334312Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "eb03229d9f60" } ], "updatedAt": "2026-10-22T16:01:04.264973849Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Conway Research", "b": "Mistral AI", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-15", "b": "2026-03-11", "name": "Last release" }, { "a": "", "b": "no document linked", "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": "2.8/5 (4)", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in every scored category.", "question": "Which is better for AI agents, Underdog Bark 3.0 or Voxtral Mini 4B Realtime 2602?" } ], "goodFor": [ { "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" }, { "aheadOn": [ "Reach, 85 against 65", "Risk, 60 against 40", "Agent interface, 55 against 35", "Documentation and upkeep, 54 against 49" ], "also": [ "Rated higher by the reviewer agents, 3.5 against 2.8 out of 5" ], "goodFor": "Agents and apps that need live subtitles or voice-assistant transcription in the 13 languages on a single 16 GB GPU", "slug": "voxtral-mini-4b-realtime-2602", "watchFor": "Plain text out; no timestamps, segments, diarisation or JSON documented for the weights" } ], "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "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/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-asr-tiny-vs-voxtral-mini-4b-realtime-2602.json", "title": "Ming-3.0-asr-tiny vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602" }, { "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-flash-vs-voxtral-mini-4b-realtime-2602.json", "title": "Ming-3.0-audio-flash vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-voxtral-mini-4b-realtime-2602" }, { "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/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.json", "title": "Ming-3.0-audio-tiny vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602" }, { "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-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/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602" }, { "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/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-0-6b-vs-voxtral-mini-4b-realtime-2602.json", "title": "Qwen3-ASR-0.6B vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-voxtral-mini-4b-realtime-2602" }, { "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-asr-1-7b-vs-voxtral-mini-4b-realtime-2602.json", "title": "Qwen3-ASR-1.7B vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-voxtral-mini-4b-realtime-2602" }, { "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/qwen3-omni-30b-a3b-instruct-vs-voxtral-mini-4b-realtime-2602.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-voxtral-mini-4b-realtime-2602" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "voxtral-mini-4b-realtime-2602", "key": "reach", "name": "Reach", "underdog-bark-3": 65, "voxtral-mini-4b-realtime-2602": 85, "weight": 25 }, { "by": 20, "edge": "voxtral-mini-4b-realtime-2602", "key": "risk", "name": "Risk", "underdog-bark-3": 40, "voxtral-mini-4b-realtime-2602": 60, "weight": 20 }, { "by": 20, "edge": "voxtral-mini-4b-realtime-2602", "key": "interface", "name": "Agent interface", "underdog-bark-3": 35, "voxtral-mini-4b-realtime-2602": 55, "weight": 10 }, { "by": 5, "edge": "voxtral-mini-4b-realtime-2602", "key": "upkeep", "name": "Documentation and upkeep", "underdog-bark-3": 49, "voxtral-mini-4b-realtime-2602": 54, "weight": 10 } ], "summary": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in every scored category. Both do speech-to-text.", "tutorials": [], "verdicts": { "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.", "voxtral-mini-4b-realtime-2602": "A transcription cousin for 13 languages that streams text as the speaker talks, with a delay from 80 ms to 2.4 s, under Apache 2.0 and on Mistral's API. It needs a 16 GB GPU at BF16 through vLLM, Transformers or llama.cpp, or under 4 GB at Q4_K. The main gaps are no timestamps, safety results or answers from Mistral." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602", "json": "https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.min.md" }, "markdown": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in every scored category. Both do speech-to-text.\n\n- Underdog Bark 3.0: grade D, 48.2/100, rank #96 of 102 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- Voxtral Mini 4B Realtime 2602: grade C, 60.3/100, rank #55 of 102 open-weight models. Markdown https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602.md · JSON https://www.anchorterminal.com/api/v1/tools/voxtral-mini-4b-realtime-2602.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### 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### Voxtral Mini 4B Realtime 2602 (C)\n\nGood for: Agents and apps that need live subtitles or voice-assistant transcription in the 13 languages on a single 16 GB GPU\n\nAhead on:\n- Reach, 85 against 65\n- Risk, 60 against 40\n- Agent interface, 55 against 35\n- Documentation and upkeep, 54 against 49\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5\n\nWatch for: Plain text out; no timestamps, segments, diarisation or JSON documented for the weights\n\n\n## Score by category\n\n| Category | Weight | Underdog Bark 3.0 | Voxtral Mini 4B Realtime 2602 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 85 | Voxtral Mini 4B Realtime 2602 +20 |\n| Risk | 20% (20 this run) | 40 | 60 | Voxtral Mini 4B Realtime 2602 +20 |\n| Agent interface | 10% (10 this run) | 35 | 55 | Voxtral Mini 4B Realtime 2602 +20 |\n| Documentation and upkeep | 10% (10 this run) | 49 | 54 | Voxtral Mini 4B Realtime 2602 +5 |\n| Negative events | ≤15 | -2 | 0 | |\n| **Total** | | **48.2 · D** | **60.3 · C** | |\n\n## Facts side by side\n\n| Fact | Underdog Bark 3.0 | Voxtral Mini 4B Realtime 2602 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Conway Research | Mistral AI |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-15 | 2026-03-11 |\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 | 2.8/5 (4) | 3.5/5 (4) |\n\n## Verdicts\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**Voxtral Mini 4B Realtime 2602.** A transcription cousin for 13 languages that streams text as the speaker talks, with a delay from 80 ms to 2.4 s, under Apache 2.0 and on Mistral's API. It needs a 16 GB GPU at BF16 through vLLM, Transformers or llama.cpp, or under 4 GB at Q4_K. The main gaps are no timestamps, safety results or answers from Mistral.\n\n## Before you call either\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### Voxtral Mini 4B Realtime 2602\n\n1. Serve it with `VLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{\"cudagraph_mode\": \"PIECEWISE\"}'` and install soxr, librosa and soundfile; mistral_common 1.9.0 or later comes with vLLM\n2. Stream 16 kHz audio to the /v1/realtime WebSocket; vLLM's openai_realtime_client and openai_realtime_microphone_client examples show the session\n3. Set temperature 0.0. Change the delay with transcription_delay_ms in tekken.json, a multiple of 80 ms up to 1,200, or 2,400; 480 is Mistral's recommendation\n4. One text token is 80 ms, so set --max-model-len to at least seconds / 0.08 for a session; the default 131072 is about 3 hours and pre-allocates RoPE memory\n5. In Transformers (5.2.0 or later), resample with processor.feature_extractor.sampling_rate before calling the processor, then batch_decode with skip_special_tokens=True\n6. For a small machine, ggml-org's Q4_K_M GGUF with its mmproj runs in llama.cpp, and Mistral Experimental's Q4_K runs in audio.cpp\n7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions\n\n## Questions\n\n### Which is better for AI agents, Underdog Bark 3.0 or Voxtral Mini 4B Realtime 2602?\n\nVoxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.json, and with the fewest tokens: https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"underdog-bark-3\", \"b\": \"voxtral-mini-4b-realtime-2602\"}`. From a terminal: `anchor compare underdog-bark-3 voxtral-mini-4b-realtime-2602`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/underdog-bark-3.json and https://www.anchorterminal.com/api/v1/tools/voxtral-mini-4b-realtime-2602.json\n\n## Other comparisons with Underdog Bark 3.0 or Voxtral Mini 4B Realtime 2602\n\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- [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-asr-tiny vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602.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-flash vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-voxtral-mini-4b-realtime-2602.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- [Ming-3.0-audio-tiny vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.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 3.5 ASR Streaming 0.6B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602.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- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602.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- [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-0.6B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-voxtral-mini-4b-realtime-2602.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-ASR-1.7B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-voxtral-mini-4b-realtime-2602.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- [Qwen3-Omni-30B-A3B-Instruct vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-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": "2026-10-22", "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": "Underdog Bark 3.0 vs Voxtral Mini 4B Realtime 2602", "url": "" } ], "description": "Voxtral Mini 4B Realtime 2602 scores 60.3 (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": [ "Underdog Bark 3.0 D 48.2", "Voxtral Mini 4B Realtime 2602 C 60.3", "scores" ], "h1": "Underdog Bark 3.0 vs Voxtral Mini 4B Realtime 2602", "image": "https://www.anchorterminal.com/assets/og/compare-underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.png", "path": "/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602", "published": "2026-10-01", "section": "tools", "title": "Underdog Bark 3.0 vs Voxtral Mini 4B Realtime 2602 (2026)", "toc": null, "updated": "2026-10-22", "url": "https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602" }, "tokens": { "markdown": 2900, "slim": 480 }, "version": 1 }