{
  "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-audio-flash's 42.5 (E), and leads in 2 of 4 scored categories. Ming-3.0-audio-flash leads on agent interface.",
    "b": {
      "slug": "ming-3-0-audio-flash",
      "name": "Ming-3.0-audio-flash",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "speech-to-text",
      "summary": "Ming-3.0-audio-flash is inclusionAI's open-weight audio-language model for Chinese and English. It takes audio with text instructions and returns transcripts, answers about the audio, or spoken replies, 126B parameters with 6.5B active, under MIT.",
      "url": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash",
      "markdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-flash.json",
      "repo": "https://huggingface.co/inclusionAI/Ming-3.0-audio-flash",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ming-3.0-audio-flash"
        }
      ],
      "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). ModelScope carries the same model under inclusionAI's organisation.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT. inclusionAI sells no per-call access to these weights. Running them costs the hardware, 259.4 GB of BF16 files and eight 80 GB GPUs in inclusionAI's 128K serving profile (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-audio-flash",
      "capabilities": [
        "speech.stt",
        "speech.batch",
        "speech.languages",
        "voice.speech-to-speech",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "speech-to-text",
        "audio-understanding",
        "speech-to-speech",
        "voice-cloning",
        "moe",
        "mit",
        "safetensors",
        "chinese",
        "english",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 42.5,
        "grade": "E",
        "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": 24,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 65,
          "reach": 25,
          "risk": 50,
          "upkeep": 49
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "Ming-3.0-audio-flash is a cousin for Chinese and English transcription, questions about long audio and spoken replies in built-in or cloned voices, under MIT. It needs about 260 GB on a multi-GPU server, with inclusionAI's own runtime branches or Transformers remote code. The main risks are running repository code to load it and no published safety results.",
        "bestFor": "Teams with a multi-GPU server who want Chinese and English transcription, audio understanding and spoken replies from one MIT model",
        "strengths": [
          "MIT with no use policy, downloadable with no account or gate",
          "Transcription, audio question answering and spoken replies from one model, with 484 built-in voices and voice cloning",
          "Request and response format documented for its vLLM-Omni and SGLang servers, with example clients and streamed audio",
          "131,072-token context with no position scaling and thinking switched on per request",
          "Repository, examples and runtime branch updated daily up to 20 October 2026"
        ],
        "weaknesses": [
          "259.3 GB of BF16 files and no quantised build, so it needs a multi-GPU server and doesn't fit 128 GB",
          "No named runtime runs it from released code, it needs inclusionAI's fork branches or trust_remote_code",
          "No hosted provider, so an agent without the hardware can't call it",
          "No safety results, training data, limitations or knowledge cut-off published",
          "No timestamps or JSON transcript output, and where reasoning text returns isn't documented"
        ],
        "agentNotes": [
          "Start it with inclusionAI's recipe, `hf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash \u0026\u0026 export MODEL_PATH=\"$PWD/inclusionAI/Ming-3.0-audio-flash\" \u0026\u0026 pip install -r requirements-vllm-omni.txt \u0026\u0026 MING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh \"$MODEL_PATH\"`, run from a clone of github.com/inclusionAI/Ming-3.0-audio-flash",
          "The 128K profile uses tensor parallelism over eight 80 GB H800 GPUs, the SGLang profile four and SGLang-Omni's cookbook two. It doesn't fit the 107.4 GB reference machine",
          "Use `MING_OUTPUT_MODE=asr` with the validated prompt and greedy decoding for transcripts, and split the language label from the text at the tab",
          "Use `MING_OUTPUT_MODE=text` for audio questions and the Talker profile with `modalities=[\"text\", \"audio\"]` for spoken replies",
          "Send audio as a base64 data URL in an `audio_url` item for vLLM-Omni, or as `input_audio` for SGLang",
          "Pick a voice with `audio.voice` and an id, or clone one with `ref_audio` and its exact `prompt_text`, never both",
          "Turn thinking on per request with `chat_template_kwargs` `{\"enable_thinking\": true}`, it's off by default and the reasoning comes back in think tags",
          "Tool calls follow the template's `\u003ctool_call\u003e` with `\u003carg_key\u003e` and `\u003carg_value\u003e` pairs, and no parser is named, so parse them yourself",
          "Review the repository's Python and the talker/ code, .pyc files included, before loading with trust_remote_code"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "E",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 42.5
          }
        ],
        "editorialScores": {
          "interface": 65,
          "reach": 25,
          "risk": 50,
          "upkeep": 49
        },
        "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": "# inclusionAI's recipe, from github.com/inclusionAI/Ming-3.0-audio-flash (eight 80 GB GPUs for 128K)\nhf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash\nexport MODEL_PATH=\"$PWD/inclusionAI/Ming-3.0-audio-flash\"\npip install -r requirements-vllm-omni.txt\nMING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh \"$MODEL_PATH\"",
        "http": "python examples/vllm_omni_audio_chat.py examples/audio/introduce_ant_group.wav \\\n  --task asr \\\n  --prompt 'Please recognize the language of this speech and transcribe it, placing a `\\t` between the identified language and the transcribe.' \\\n  --max-tokens 256 --no-repeat-ngram-size 10"
      },
      "letme": {
        "capability": "https://letme.dev/speech.stt",
        "tool": "https://letme.dev/ming-3-0-audio-flash"
      },
      "sameCompany": [
        "ming-3-0-asr-tiny",
        "ming-3-0-audio-tiny",
        "ming-3-0-tts-tiny",
        "ling-3-0-tiny"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group)",
        "params": "126B",
        "active": "6.5B",
        "context": 131072,
        "goodFor": "Audio-language model for Chinese and English: speech to text, questions about audio and spoken replies.",
        "runtimes": [],
        "builds": [
          {
            "name": "Ming-3.0-audio-flash",
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-flash",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 259.3,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Main checkpoint 256.4 GB plus the Talker and VAE 3.0 GB"
          }
        ],
        "run": "hf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash \u0026\u0026 export MODEL_PATH=\"$PWD/inclusionAI/Ming-3.0-audio-flash\" \u0026\u0026 pip install -r requirements-vllm-omni.txt \u0026\u0026 MING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh \"$MODEL_PATH\"",
        "runBuild": "Ming-3.0-audio-flash",
        "checked": "2026-10-21",
        "minMemoryGB": 512
      },
      "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-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"
          },
          {
            "name": "Ming-3.0-asr-tiny",
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny",
            "params": "not stated on the card (Hub metadata counts 8,865,800,992 in the files)",
            "listing": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny"
          }
        ],
        "olderCount": 54
      },
      "alsoIn": [
        "voice-agents"
      ],
      "area": "voice",
      "provenance": {
        "legalEntity": "inclusionAI, Ant Group (named in the inclusion-ai.org footer; no registered company named)",
        "domain": "inclusion-ai.org",
        "domainRegistered": "2025-04-03",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/inclusionAI/Ming-3.0-audio-flash/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "inclusion-ai.org (inclusionai.github.io redirects there) describes inclusionAI as founded by Ant Group and links its Hugging Face and GitHub organisations. Its footer reads © 2026 inclusionAI, Ant Group. RDAP gives the domain's registration as 3 April 2025.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the GitHub repository's commit history, whose messages mostly say what changed (a vLLM-Omni fix branch, voice selection, deployment profiles). The Hugging Face history mixes these with card layout edits.",
          "inclusion-ai.org/.well-known/security.txt answers with the site's 404 page, so there is none.",
          "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-audio-flash.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"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3/5 (4)",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Cohere Transcribe 03-2026 scores 59.3 (C) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 2 of 4 scored categories. Ming-3.0-audio-flash leads on agent interface.",
        "question": "Which is better for AI agents, Cohere Transcribe 03-2026 or Ming-3.0-audio-flash?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 65 against 25",
          "Documentation and upkeep, 54 against 49"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.0 against 2.3 out of 5"
        ],
        "goodFor": "Agents that need accurate batch transcription of known-language audio in 14 languages on a small GPU",
        "slug": "cohere-transcribe-03-2026",
        "watchFor": "Gated, so an agent needs a Hugging Face account and must accept the conditions before downloading"
      },
      {
        "aheadOn": [
          "Agent interface, 65 against 45"
        ],
        "also": null,
        "goodFor": "Teams with a multi-GPU server who want Chinese and English transcription, audio understanding and spoken replies from one MIT model",
        "slug": "ming-3-0-audio-flash",
        "watchFor": "259.3 GB of BF16 files and no quantised build, so it needs a multi-GPU server and doesn't fit 128 GB"
      }
    ],
    "job": {
      "capability": "speech.stt",
      "name": "Speech-to-text"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.json",
        "title": "Cohere Transcribe 03-2026 vs Ming-3.0-asr-tiny",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-tiny.json",
        "title": "Cohere Transcribe 03-2026 vs Ming-3.0-audio-tiny",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-5-asr-streaming-0-6b.json",
        "title": "Cohere Transcribe 03-2026 vs Nemotron 3.5 ASR Streaming 0.6B",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-5-asr-streaming-0-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Cohere Transcribe 03-2026 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-parakeet-tdt-0-6b-v3.json",
        "title": "Cohere Transcribe 03-2026 vs Parakeet TDT 0.6B v3",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-parakeet-tdt-0-6b-v3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-0-6b.json",
        "title": "Cohere Transcribe 03-2026 vs Qwen3-ASR-0.6B",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-0-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-1-7b.json",
        "title": "Cohere Transcribe 03-2026 vs Qwen3-ASR-1.7B",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Cohere Transcribe 03-2026 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-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-ming-3-0-audio-flash.json",
        "title": "Ming-3.0-asr-tiny vs Ming-3.0-audio-flash",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny.json",
        "title": "Ming-3.0-audio-flash vs Ming-3.0-audio-tiny",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b.json",
        "title": "Ming-3.0-audio-flash vs Nemotron 3.5 ASR Streaming 0.6B",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Ming-3.0-audio-flash vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-parakeet-tdt-0-6b-v3.json",
        "title": "Ming-3.0-audio-flash vs Parakeet TDT 0.6B v3",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-parakeet-tdt-0-6b-v3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-0-6b.json",
        "title": "Ming-3.0-audio-flash vs Qwen3-ASR-0.6B",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-0-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-1-7b.json",
        "title": "Ming-3.0-audio-flash vs Qwen3-ASR-1.7B",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ming-3.0-audio-flash vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "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"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 40,
        "cohere-transcribe-03-2026": 65,
        "edge": "cohere-transcribe-03-2026",
        "key": "reach",
        "ming-3-0-audio-flash": 25,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 0,
        "cohere-transcribe-03-2026": 50,
        "edge": "",
        "key": "risk",
        "ming-3-0-audio-flash": 50,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 20,
        "cohere-transcribe-03-2026": 45,
        "edge": "ming-3-0-audio-flash",
        "key": "interface",
        "ming-3-0-audio-flash": 65,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 5,
        "cohere-transcribe-03-2026": 54,
        "edge": "cohere-transcribe-03-2026",
        "key": "upkeep",
        "ming-3-0-audio-flash": 49,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Cohere Transcribe 03-2026 scores 59.3 (C) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 2 of 4 scored categories. Ming-3.0-audio-flash leads on agent interface. Both do speech-to-text.",
    "tutorials": [],
    "verdicts": {
      "cohere-transcribe-03-2026": "An accurate offline transcription cousin for 14 languages when the language is known, small enough for about 5 GB, under Apache 2.0, and also on Cohere's API. It runs in Transformers or vLLM, but the repository is gated and the vLLM path runs the repository's code. Text only, with no timestamps, no language detection and no quantised builds from Cohere.",
      "ming-3-0-audio-flash": "Ming-3.0-audio-flash is a cousin for Chinese and English transcription, questions about long audio and spoken replies in built-in or cloned voices, under MIT. It needs about 260 GB on a multi-GPU server, with inclusionAI's own runtime branches or Transformers remote code. The main risks are running repository code to load it and no published safety results."
    }
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  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "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-flash.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash.md",
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  "markdown": "Cohere Transcribe 03-2026 scores 59.3 (C) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 2 of 4 scored categories. Ming-3.0-audio-flash leads on 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-audio-flash: grade E, 42.5/100, rank #98 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-audio-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-flash.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- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md\n- All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/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 25\n- Documentation and upkeep, 54 against 49\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5\n\nWatch for: Gated, so an agent needs a Hugging Face account and must accept the conditions before downloading\n\n### Ming-3.0-audio-flash (E)\n\nGood for: Teams with a multi-GPU server who want Chinese and English transcription, audio understanding and spoken replies from one MIT model\n\nAhead on:\n- Agent interface, 65 against 45\n\nWatch for: 259.3 GB of BF16 files and no quantised build, so it needs a multi-GPU server and doesn't fit 128 GB\n\n\n## Score by category\n\n| Category | Weight | Cohere Transcribe 03-2026 | Ming-3.0-audio-flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 25 | Cohere Transcribe 03-2026 +40 |\n| Risk | 20% (20 this run) | 50 | 50 | even |\n| Agent interface | 10% (10 this run) | 45 | 65 | Ming-3.0-audio-flash +20 |\n| Documentation and upkeep | 10% (10 this run) | 54 | 49 | Cohere Transcribe 03-2026 +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.3 · C** | **42.5 · E** | |\n\n## Facts side by side\n\n| Fact | Cohere Transcribe 03-2026 | Ming-3.0-audio-flash |\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) | 2.3/5 (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-audio-flash.** Ming-3.0-audio-flash is a cousin for Chinese and English transcription, questions about long audio and spoken replies in built-in or cloned voices, under MIT. It needs about 260 GB on a multi-GPU server, with inclusionAI's own runtime branches or Transformers remote code. The main risks are running repository code to load it and no published 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-audio-flash\n\n1. Start it with inclusionAI's recipe, `hf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash \u0026\u0026 export MODEL_PATH=\"$PWD/inclusionAI/Ming-3.0-audio-flash\" \u0026\u0026 pip install -r requirements-vllm-omni.txt \u0026\u0026 MING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh \"$MODEL_PATH\"`, run from a clone of github.com/inclusionAI/Ming-3.0-audio-flash\n2. The 128K profile uses tensor parallelism over eight 80 GB H800 GPUs, the SGLang profile four and SGLang-Omni's cookbook two. It doesn't fit the 107.4 GB reference machine\n3. Use `MING_OUTPUT_MODE=asr` with the validated prompt and greedy decoding for transcripts, and split the language label from the text at the tab\n4. Use `MING_OUTPUT_MODE=text` for audio questions and the Talker profile with `modalities=[\"text\", \"audio\"]` for spoken replies\n5. Send audio as a base64 data URL in an `audio_url` item for vLLM-Omni, or as `input_audio` for SGLang\n6. Pick a voice with `audio.voice` and an id, or clone one with `ref_audio` and its exact `prompt_text`, never both\n7. Turn thinking on per request with `chat_template_kwargs` `{\"enable_thinking\": true}`, it's off by default and the reasoning comes back in think tags\n8. Tool calls follow the template's `\u003ctool_call\u003e` with `\u003carg_key\u003e` and `\u003carg_value\u003e` pairs, and no parser is named, so parse them yourself\n9. Review the repository's Python and the talker/ code, .pyc files included, before loading with trust_remote_code\n\n## Questions\n\n### Which is better for AI agents, Cohere Transcribe 03-2026 or Ming-3.0-audio-flash?\n\nCohere Transcribe 03-2026 scores 59.3 (C) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 2 of 4 scored categories. Ming-3.0-audio-flash leads on 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-audio-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cohere-transcribe-03-2026\", \"b\": \"ming-3-0-audio-flash\"}`. From a terminal: `anchor compare cohere-transcribe-03-2026 ming-3-0-audio-flash`\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-audio-flash.json\n\n## Other comparisons with Cohere Transcribe 03-2026 or Ming-3.0-audio-flash\n\n- [Cohere Transcribe 03-2026 vs Ming-3.0-asr-tiny](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.md)\n- [Cohere Transcribe 03-2026 vs Ming-3.0-audio-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-audio-flash vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny.md)\n- [Ming-3.0-audio-flash vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Ming-3.0-audio-flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Ming-3.0-audio-flash vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-parakeet-tdt-0-6b-v3.md)\n- [Ming-3.0-audio-flash vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-0-6b.md)\n- [Ming-3.0-audio-flash vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-1-7b.md)\n- [Ming-3.0-audio-flash vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-omni-30b-a3b-instruct.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",
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    "description": "Cohere Transcribe 03-2026 scores 59.3 (C) to Ming-3.0-audio-flash's 42.5 (E) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.",
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    "section": "tools",
    "title": "Cohere Transcribe 03-2026 vs Ming-3.0-audio-flash for AI agents (2026)",
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    "updated": "2026-10-21",
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