{
  "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 Ming-3.0-asr-tiny's 52.5 (D), and leads in 2 of 4 scored categories. Ming-3.0-asr-tiny leads on risk and agent interface.",
    "b": {
      "slug": "ming-3-0-asr-tiny",
      "name": "Ming-3.0-asr-tiny",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "speech-to-text",
      "summary": "Ming-3.0-asr-tiny is inclusionAI's open-weight speech recognition model for Chinese, English and Chinese dialects, an audio encoder on the Ling-tiny-v3 mixture of experts with 8.9B parameters counted from the files, that takes hotwords or dialogue context as hints, under MIT.",
      "url": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny",
      "markdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ming-3-0-asr-tiny.json",
      "repo": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ming-3.0-asr-tiny"
        }
      ],
      "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). The cookbook and evaluation tools are in a public GitHub repository.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. inclusionAI sells no per-call access to these weights. Running them costs the hardware, about 17.7 GB of BF16 safetensors on one CUDA GPU; the publisher validated it on an NVIDIA L20Y 80 GB (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://github.com/inclusionAI/Ming-3.0-asr",
      "capabilities": [
        "speech.stt",
        "speech.batch",
        "speech.languages",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "speech-to-text",
        "asr",
        "chinese",
        "dialects",
        "contextual-asr",
        "mit",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 52.5,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 21,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no safety results.",
        "bestFor": "Agents with a CUDA GPU that need local Chinese, English or dialect transcription where names and domain terms matter",
        "strengths": [
          "MIT with nothing added, ungated, files hashed in inclusionAI's own organisation",
          "Hotwords, a topic or dialogue context can be added to the prompt to bias names and terms",
          "17.7 GB of BF16 safetensors, about 21 GB in memory, which fits a 24 GB GPU",
          "Input format, output format and parser are documented, with an OpenAI-compatible transcription endpoint through SGLang-Omni",
          "Actively updated, with the last change on 20 October 2026"
        ],
        "weaknesses": [
          "Loading runs the publisher's Python with trust_remote_code, and the reference path needs a CUDA GPU",
          "Not in any released version of vLLM, SGLang, llama.cpp, Ollama, LM Studio or Transformers, and no quantised build from the publisher",
          "Plain text output with no word timestamps or speaker diarisation, Chinese and English only",
          "An 8,192-token context shared by audio and transcript, so long recordings must be chunked",
          "No training data or safety results, no security route of its own, and no publisher reply in its repository yet"
        ],
        "agentNotes": [
          "Start it with `hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny`, then `pip install -r requirements.txt` in Python 3.12 with CUDA (Transformers 5.12.1, fla-core 0.5.2, PyTorch 2.11.0), then `python examples/transcribe.py --model . --audio speech.wav --language-hint en`",
          "To serve it, `sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny` from SGLang-Omni's main branch, then POST the WAV to /v1/audio/transcriptions with response_format json and read the text field",
          "Parse raw Transformers output with decode_transcript from asr_request.py, which strips the language label before the tab",
          "Pass language zh or en when known, anything else is rejected, and append a topic or hotwords to the base instruction with no newline",
          "Load with dtype torch.bfloat16 rather than casting the model, use greedy decoding, max_new_tokens 256 and no_repeat_ngram_size 10, and don't apply the chat template",
          "Split long recordings yourself or use --audio-chunk-frames, since input and output share 8,192 tokens",
          "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": "low",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 52.5
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 46
        },
        "provenanceScore": 57,
        "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": "hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny\ncd Ming-3.0-asr-tiny\npip install -r requirements.txt   # Python 3.12, CUDA, Transformers 5.12.1, PyTorch 2.11.0\npython examples/transcribe.py --model . --audio /path/to/speech.wav --language-hint en",
        "http": "# after: sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny\ncurl -X POST http://127.0.0.1:8000/v1/audio/transcriptions \\\n  -F model=inclusionAI/Ming-3.0-asr-tiny \\\n  -F file=@/path/to/speech.wav \\\n  -F language=en \\\n  -F response_format=json"
      },
      "letme": {
        "capability": "https://letme.dev/speech.stt",
        "tool": "https://letme.dev/ming-3-0-asr-tiny"
      },
      "sameCompany": [
        "ming-3-0-audio-flash",
        "ming-3-0-audio-tiny",
        "ming-3-0-tts-tiny",
        "ling-3-0-tiny"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group)",
        "builtOn": "Ling-tiny-v3",
        "params": "8.87B",
        "goodFor": "Speech recognition for Chinese, English and Chinese dialects, taking hotwords or dialogue context as hints.",
        "runtimes": [],
        "builds": [
          {
            "name": "Ming-3.0-asr-tiny",
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 17.7,
            "memoryGB": 24,
            "format": "safetensors",
            "note": "Needs CUDA for its KDA kernels, per the publisher"
          }
        ],
        "run": "hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny \u0026\u0026 cd Ming-3.0-asr-tiny \u0026\u0026 pip install -r requirements.txt \u0026\u0026 python examples/transcribe.py --model . --audio /path/to/speech.wav --language-hint en",
        "runBuild": "Ming-3.0-asr-tiny",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "inclusionAI (Ant Group)",
        "family": "Ming 3.0",
        "url": "https://www.anchorterminal.com/open-weights/#inclusionai",
        "others": [
          {
            "name": "Ming-3.0-tts-0.5B",
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B",
            "params": "0.5B (dense)",
            "listing": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b"
          },
          {
            "name": "Ming-3.0-tts-tiny",
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny",
            "params": "7.9B total, about 1.3B active (backbone)",
            "listing": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny"
          },
          {
            "name": "Ming-3.0-audio-flash",
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-flash",
            "params": "126B total, 6.5B active",
            "listing": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash"
          },
          {
            "name": "Ming-3.0-audio-tiny",
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny",
            "params": "9.3B total, 2.7B active",
            "listing": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny"
          }
        ],
        "olderCount": 54
      },
      "area": "voice",
      "provenance": {
        "legalEntity": "inclusionAI, Ant Group (named in the site footer of inclusion-ai.org; the LICENSE copyright line reads inclusionAI)",
        "domain": "inclusion-ai.org",
        "domainRegistered": "2025-04-03",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI, and the GitHub organisation's profile describes inclusionAI as Ant Group's open-source projects. The footer reads \"© 2026 inclusionAI, Ant Group\". No registered company name was found.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the Hugging Face commit history, 41 commits from 14 to 20 October 2026 whose messages say what changed (pinned dependencies, a unified request format, the validated audio-only weights on 18 October, the SGLang-Omni instructions on 20 October).",
          "inclusion-ai.org/.well-known/security.txt answered 404 on 21 October 2026. The domain registration date is carried from the RDAP check made for ming-3-0-tts-tiny on 21 October 2026.",
          "Ant Group runs a company-wide security response centre, AntSRC (https://security.alipay.com/, read on 21 October 2026). Nothing on inclusionAI's card or sites links it."
        ],
        "score": 57
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Cohere",
        "b": "inclusionAI (Ant Group)",
        "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": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-04-28",
        "b": "2026-10-20",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "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"
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        "a": "",
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        "b": "",
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        "name": "Arbitration or class-action waiver"
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      {
        "a": "3/5 (4)",
        "b": "3/5 (4)",
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      {
        "answer": "Cohere Transcribe 03-2026 scores 59.3 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 2 of 4 scored categories. Ming-3.0-asr-tiny leads on risk and agent interface.",
        "question": "Which is better for AI agents, Cohere Transcribe 03-2026 or Ming-3.0-asr-tiny?"
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          "Risk, 55 against 50",
          "Agent interface, 60 against 45"
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        "also": null,
        "goodFor": "Agents with a CUDA GPU that need local Chinese, English or dialect transcription where names and domain terms matter",
        "slug": "ming-3-0-asr-tiny",
        "watchFor": "Loading runs the publisher's Python with trust_remote_code, and the reference path needs a CUDA GPU"
      }
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        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3.json",
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        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-underdog-bark-3"
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        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-voxtral-mini-4b-realtime-2602.json",
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        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-voxtral-mini-4b-realtime-2602"
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        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny.json",
        "title": "Ming-3.0-asr-tiny vs Ming-3.0-audio-tiny",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny"
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      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.json",
        "title": "Ming-3.0-asr-tiny vs Nemotron 3.5 ASR Streaming 0.6B",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Ming-3.0-asr-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-parakeet-tdt-0-6b-v3.json",
        "title": "Ming-3.0-asr-tiny vs Parakeet TDT 0.6B v3",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-parakeet-tdt-0-6b-v3"
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      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-0-6b.json",
        "title": "Ming-3.0-asr-tiny vs Qwen3-ASR-0.6B",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-0-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-1-7b.json",
        "title": "Ming-3.0-asr-tiny vs Qwen3-ASR-1.7B",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "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"
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        "name": "Capability",
        "pending": true,
        "weight": 35
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        "by": 15,
        "cohere-transcribe-03-2026": 65,
        "edge": "cohere-transcribe-03-2026",
        "key": "reach",
        "ming-3-0-asr-tiny": 50,
        "name": "Reach",
        "weight": 25
      },
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        "cohere-transcribe-03-2026": 50,
        "edge": "ming-3-0-asr-tiny",
        "key": "risk",
        "ming-3-0-asr-tiny": 55,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 15,
        "cohere-transcribe-03-2026": 45,
        "edge": "ming-3-0-asr-tiny",
        "key": "interface",
        "ming-3-0-asr-tiny": 60,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 8,
        "cohere-transcribe-03-2026": 54,
        "edge": "cohere-transcribe-03-2026",
        "key": "upkeep",
        "ming-3-0-asr-tiny": 46,
        "name": "Documentation and upkeep",
        "weight": 10
      }
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    "summary": "Cohere Transcribe 03-2026 scores 59.3 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 2 of 4 scored categories. Ming-3.0-asr-tiny leads on risk and agent interface. Both do speech-to-text.",
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      "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.",
      "ming-3-0-asr-tiny": "A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no safety results."
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  "markdown": "Cohere Transcribe 03-2026 scores 59.3 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 2 of 4 scored categories. Ming-3.0-asr-tiny leads on risk and agent interface. 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- Ming-3.0-asr-tiny: grade D, 52.5/100, rank #85 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-asr-tiny.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- Reach, 65 against 50\n- Documentation and upkeep, 54 against 46\n\nWatch for: Gated, so an agent needs a Hugging Face account and must accept the conditions before downloading\n\n### Ming-3.0-asr-tiny (D)\n\nGood for: Agents with a CUDA GPU that need local Chinese, English or dialect transcription where names and domain terms matter\n\nAhead on:\n- Risk, 55 against 50\n- Agent interface, 60 against 45\n\nWatch for: Loading runs the publisher's Python with trust_remote_code, and the reference path needs a CUDA GPU\n\n\n## Score by category\n\n| Category | Weight | Cohere Transcribe 03-2026 | Ming-3.0-asr-tiny | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 50 | Cohere Transcribe 03-2026 +15 |\n| Risk | 20% (20 this run) | 50 | 55 | Ming-3.0-asr-tiny +5 |\n| Agent interface | 10% (10 this run) | 45 | 60 | Ming-3.0-asr-tiny +15 |\n| Documentation and upkeep | 10% (10 this run) | 54 | 46 | Cohere Transcribe 03-2026 +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.3 · C** | **52.5 · D** | |\n\n## Facts side by side\n\n| Fact | Cohere Transcribe 03-2026 | Ming-3.0-asr-tiny |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Cohere | inclusionAI (Ant Group) |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-04-28 | 2026-10-20 |\n| Terms last updated | no document linked | 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) | 3/5 (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**Ming-3.0-asr-tiny.** A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no safety results.\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### Ming-3.0-asr-tiny\n\n1. Start it with `hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny`, then `pip install -r requirements.txt` in Python 3.12 with CUDA (Transformers 5.12.1, fla-core 0.5.2, PyTorch 2.11.0), then `python examples/transcribe.py --model . --audio speech.wav --language-hint en`\n2. To serve it, `sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny` from SGLang-Omni's main branch, then POST the WAV to /v1/audio/transcriptions with response_format json and read the text field\n3. Parse raw Transformers output with decode_transcript from asr_request.py, which strips the language label before the tab\n4. Pass language zh or en when known, anything else is rejected, and append a topic or hotwords to the base instruction with no newline\n5. Load with dtype torch.bfloat16 rather than casting the model, use greedy decoding, max_new_tokens 256 and no_repeat_ngram_size 10, and don't apply the chat template\n6. Split long recordings yourself or use --audio-chunk-frames, since input and output share 8,192 tokens\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, Cohere Transcribe 03-2026 or Ming-3.0-asr-tiny?\n\nCohere Transcribe 03-2026 scores 59.3 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 2 of 4 scored categories. Ming-3.0-asr-tiny leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cohere-transcribe-03-2026\", \"b\": \"ming-3-0-asr-tiny\"}`. From a terminal: `anchor compare cohere-transcribe-03-2026 ming-3-0-asr-tiny`\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/ming-3-0-asr-tiny.json\n\n## Other comparisons with Cohere Transcribe 03-2026 or Ming-3.0-asr-tiny\n\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 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 Ming-3.0-audio-flash](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-flash.md)\n- [Ming-3.0-asr-tiny vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny.md)\n- [Ming-3.0-asr-tiny vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Ming-3.0-asr-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Ming-3.0-asr-tiny vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-parakeet-tdt-0-6b-v3.md)\n- [Ming-3.0-asr-tiny vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-0-6b.md)\n- [Ming-3.0-asr-tiny vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-1-7b.md)\n- [Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct.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",
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        "name": "Cohere Transcribe 03-2026 vs Ming-3.0-asr-tiny",
        "url": ""
      }
    ],
    "description": "Cohere Transcribe 03-2026 scores 59.3 (C) to Ming-3.0-asr-tiny's 52.5 (D) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.",
    "facts": [
      "Cohere Transcribe 03-2026 C 59.3",
      "Ming-3.0-asr-tiny D 52.5",
      "scores"
    ],
    "h1": "Cohere Transcribe 03-2026 vs Ming-3.0-asr-tiny",
    "image": "https://www.anchorterminal.com/assets/og/compare-cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.png",
    "path": "/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny",
    "published": "2026-10-01",
    "section": "tools",
    "title": "Cohere Transcribe 03-2026 vs Ming-3.0-asr-tiny for AI agents (2026)",
    "toc": null,
    "updated": "2026-10-21",
    "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny"
  },
  "tokens": {
    "markdown": 2650,
    "slim": 480
  },
  "version": 1
}