{
  "data": {
    "a": {
      "slug": "ming-3-0-audio-tiny",
      "name": "Ming-3.0-audio-tiny",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "speech-to-text",
      "summary": "Ming-3.0-audio-tiny is inclusionAI's open-weight audio-language model for Chinese and English speech recognition, audio understanding and spoken replies, 9.3B parameters with 2.7B active, published on Hugging Face under MIT.",
      "url": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny",
      "markdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-tiny.json",
      "repo": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ming-3.0-audio-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). inclusionAI also publishes it on ModelScope.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT. One bundled file, the fastText language-identification model in talker/data, is under CC BY-SA 3.0. inclusionAI sells no per-call access to these weights. Running them costs the hardware, about 20.8 GB of BF16 files (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny",
      "capabilities": [
        "speech.stt",
        "speech.batch",
        "speech.languages",
        "speech.voices",
        "voice.speech-to-speech",
        "voice.tools",
        "voice.clone",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "speech-to-text",
        "audio-understanding",
        "spoken-dialogue",
        "voice-cloning",
        "moe",
        "tool-calling",
        "chinese",
        "mit",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 49.8,
        "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": 22,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 65,
          "reach": 45,
          "risk": 50,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small MIT-licensed audio-language model that transcribes Chinese and English, answers questions about audio and replies in a built-in or cloned voice. It needs about 25 GB and SGLang-Omni from main or inclusionAI's own vLLM-Omni and SGLang branches. The main risk is that every loading path runs repository code, with nothing published on safety.",
        "bestFor": "Self-hosted Chinese and English transcription, audio questions and spoken replies with tool calls on one GPU with about 25 GB free",
        "strengths": [
          "MIT weights in safetensors, downloadable with no account",
          "Speech recognition, audio question answering and spoken replies in one 9.3B model with 2.7B active, one GPU for text or ASR",
          "About 480 built-in voices or a cloned voice from a reference WAV, with streamed speech through vLLM-Omni",
          "A Jinja chat template with tools, several calls per turn and a per-request thinking switch",
          "131,072 positions in config.json with no scaling"
        ],
        "weaknesses": [
          "No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches",
          "Every loading path runs repository code, and 40 compiled .pyc files ship under talker/",
          "BF16 only from inclusionAI, about 25 GB with headroom, and no inference provider",
          "No tool-call parser named, no JSON or timestamps documented, and no word on where reasoning text comes back",
          "No safety evaluation, agent guidance, training-data description, limitations or cut-off, and no security route of its own"
        ],
        "agentNotes": [
          "Fastest start is SGLang-Omni from main, `sgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0`, which exposes OpenAI-style /v1/audio/transcriptions, /v1/audio/speech and /v1/chat/completions",
          "For transcripts use greedy decoding with 10-gram blocking and the validated ASR prompt, and expect a language label, a tab, then the text",
          "Parse tool calls yourself from `\u003ctool_call\u003ename` with `\u003carg_key\u003e` and `\u003carg_value\u003e` pairs, since no runtime parser is named",
          "Thinking is off by default, turn it on with chat_template_kwargs enable_thinking true and strip the `\u003cthink\u003e` block from the reply yourself",
          "In vLLM-Omni pick a voice with audio.voice id from docs/voices.md, and don't combine it with a reference recording",
          "Review the repository's Python before passing trust_remote_code, and consider deleting the __pycache__ folders under talker/ so only readable source runs"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 49.8
          }
        ],
        "editorialScores": {
          "interface": 65,
          "reach": 45,
          "risk": 50,
          "upkeep": 46
        },
        "provenanceScore": 31,
        "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": "# SGLang-Omni (main branch has the Ming-3.0 audio pipeline); install it per https://sgl-project.github.io/sglang-omni/\nsgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0",
        "http": "curl http://127.0.0.1:8000/v1/audio/transcriptions \\\n  -F model=inclusionAI/Ming-3.0-audio-tiny \\\n  -F file=@input.wav \\\n  -F temperature=0"
      },
      "letme": {
        "capability": "https://letme.dev/speech.stt",
        "tool": "https://letme.dev/ming-3-0-audio-tiny"
      },
      "sameCompany": [
        "ming-3-0-asr-tiny",
        "ming-3-0-audio-flash",
        "ming-3-0-tts-tiny",
        "ling-3-0-tiny"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group)",
        "params": "9.3B",
        "active": "2.7B",
        "context": 131072,
        "goodFor": "Small audio-language model for Chinese and English speech recognition, audio understanding and spoken replies.",
        "runtimes": [],
        "builds": [
          {
            "name": "Ming-3.0-audio-tiny",
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 20.7,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "Main shards 17.7 GB plus the Talker and VAE 3.0 GB; runs through SGLang-Omni or inclusionAI's code"
          }
        ],
        "run": "sgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0",
        "runBuild": "Ming-3.0-audio-tiny",
        "checked": "2026-10-21",
        "minMemoryGB": 32
      },
      "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-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": "",
        "domain": "inclusion-ai.org",
        "domainRegistered": "2025-04-03",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card names inclusionAI as the developer. inclusionAI's GitHub profile describes it as the series of open-source projects from Ant Group. No registered legal entity is named on the card, the repository or the site.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, some of whose messages say what changed (ASR prompt and generation settings on 5 September, voice selection on 19 October, SGLang-Omni serving on 20 October). The GitHub repository inclusionAI/Ming-3.0-audio-flash has a fuller history.",
          "www.inclusion-ai.org/.well-known/security.txt answered 404 on 21 October 2026. The domain was registered on 3 April 2025 (Public Interest Registry RDAP).",
          "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": 31
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny.json"
    },
    "answer": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on agent interface.",
    "b": {
      "slug": "voxtral-mini-4b-realtime-2602",
      "name": "Voxtral Mini 4B Realtime 2602",
      "vendor": "Mistral AI",
      "vendorUrl": "https://mistral.ai",
      "kind": "weights",
      "category": "speech-to-text",
      "summary": "Voxtral Mini 4B Realtime 2602 is Mistral AI's open-weight streaming speech recognition model, a causal audio encoder of about 970M parameters on a Ministral 3B decoder, 4.4B in all, transcribing 13 languages live under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602",
      "markdownUrl": "https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/voxtral-mini-4b-realtime-2602.json",
      "repo": "https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "mistralai/Voxtral-Mini-4B-Realtime-2602"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21), though its metadata carries a gating description pointing to Mistral's privacy policy.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Mistral also serves the same model on its API as voxtral-mini-transcribe-realtime-2602 at $0.006 per audio minute, listed separately as mistral-voxtral-transcribe. Running the weights costs the hardware, 8.86 GB of BF16 safetensors on a GPU with 16 GB or more, per the card (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602",
      "capabilities": [
        "speech.stt",
        "speech.streaming",
        "speech.languages",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "speech-to-text",
        "asr",
        "streaming",
        "realtime",
        "multilingual",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-03-11",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 60.3,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 16,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 46,
          "interface": 55,
          "reach": 85,
          "risk": 60,
          "upkeep": 54
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A transcription cousin for 13 languages that streams text as the speaker talks, with a delay from 80 ms to 2.4 s, under Apache 2.0 and on Mistral's API. It needs a 16 GB GPU at BF16 through vLLM, Transformers or llama.cpp, or under 4 GB at Q4_K. The main gaps are no timestamps, safety results or answers from Mistral.",
        "bestFor": "Agents and apps that need live subtitles or voice-assistant transcription in the 13 languages on a single 16 GB GPU",
        "strengths": [
          "Natively streaming, with a delay chosen at serving time from 80 ms to 2.4 s",
          "Runs in released vLLM, Transformers and llama.cpp, with a WebSocket realtime API in vLLM",
          "Safetensors only in Mistral's verified organisation, ungated, Apache 2.0",
          "Fits a 16 GB GPU at BF16, with int4 and Q4_K builds in Mistral's experimental organisation",
          "The same model is on Mistral's API for agents without a GPU"
        ],
        "weaknesses": [
          "Plain text out; no timestamps, segments, diarisation or JSON documented for the weights",
          "No safety results, no agent guidance, and training data described in one line",
          "Mistral hasn't answered a discussion since February 2026, including a report that the Transformers path fails",
          "Not in SGLang, Ollama or LM Studio, and the quantised builds are for ExecuTorch and audio.cpp",
          "The card contradicts itself on supported runtimes and the delay range"
        ],
        "agentNotes": [
          "Serve it with `VLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{\"cudagraph_mode\": \"PIECEWISE\"}'` and install soxr, librosa and soundfile; mistral_common 1.9.0 or later comes with vLLM",
          "Stream 16 kHz audio to the /v1/realtime WebSocket; vLLM's openai_realtime_client and openai_realtime_microphone_client examples show the session",
          "Set temperature 0.0. Change the delay with transcription_delay_ms in tekken.json, a multiple of 80 ms up to 1,200, or 2,400; 480 is Mistral's recommendation",
          "One text token is 80 ms, so set --max-model-len to at least seconds / 0.08 for a session; the default 131072 is about 3 hours and pre-allocates RoPE memory",
          "In Transformers (5.2.0 or later), resample with processor.feature_extractor.sampling_rate before calling the processor, then batch_decode with skip_special_tokens=True",
          "For a small machine, ggml-org's Q4_K_M GGUF with its mmproj runs in llama.cpp, and Mistral Experimental's Q4_K runs in audio.cpp",
          "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 60.3
          }
        ],
        "editorialScores": {
          "capability": 46,
          "interface": 55,
          "reach": 85,
          "risk": 60,
          "upkeep": 54
        },
        "provenanceScore": 81,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "uv pip install -U vllm soxr librosa soundfile\nVLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{\"cudagraph_mode\": \"PIECEWISE\"}'",
        "http": "# after vllm serve: stream 16 kHz PCM audio to the realtime WebSocket\n# ws://127.0.0.1:8000/v1/realtime  (see vLLM's openai_realtime_client example)"
      },
      "letme": {
        "capability": "https://letme.dev/speech.stt",
        "tool": "https://letme.dev/voxtral-mini-4b-realtime-2602"
      },
      "sameCompany": [
        "mistral-api",
        "mistral-embeddings",
        "mistral-moderation",
        "mistral-ocr",
        "mistral-vibe-cli"
      ],
      "sameModel": [
        "mistral-voxtral-transcribe"
      ],
      "weights": {
        "trainedBy": "Mistral AI",
        "builtOn": "mistralai/Ministral-3-3B-Base-2512 (Mistral's own, Apache 2.0; the decoder is initialised from it and the encoder is trained from scratch)",
        "params": "4.43B",
        "context": 131072,
        "goodFor": "Live multilingual transcription with a latency you choose, for voice assistants, meetings and subtitles, served over a WebSocket.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Voxtral-Mini-4B-Realtime-2602-ExecuTorch (model-metal-int4.pte)",
            "url": "https://huggingface.co/mistral-experimental/Voxtral-Mini-4B-Realtime-2602-ExecuTorch",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 4.44,
            "memoryGB": 8,
            "format": "executorch",
            "note": "Mistral Experimental, linked from the card; ExecuTorch Metal build for Apple devices, marked untested. Size includes tekken.json"
          },
          {
            "name": "AudioCPP-Voxtral-Mini-4B-Realtime-2602-GGUF q4_k",
            "url": "https://huggingface.co/mistral-experimental/AudioCPP-Voxtral-Mini-4B-Realtime-2602-GGUF",
            "who": "publisher",
            "precision": "q4_k",
            "sizeGB": 3.1,
            "memoryGB": 8,
            "format": "gguf",
            "note": "Mistral Experimental; for audio.cpp, a third-party runtime, not llama.cpp. A Q8_0 file of 5.1 GB sits beside it"
          },
          {
            "name": "Voxtral-Mini-4B-Realtime-2602",
            "url": "https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 8.86,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers"
            ],
            "note": "The repository holds two copies, consolidated.safetensors for vLLM and model.safetensors for Transformers, 17.7 GB in all; size is one copy"
          }
        ],
        "run": "VLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{\"cudagraph_mode\": \"PIECEWISE\"}'",
        "runBuild": "Voxtral-Mini-4B-Realtime-2602",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Mistral AI",
        "family": "Voxtral",
        "url": "https://www.anchorterminal.com/open-weights/#mistral",
        "others": [
          {
            "name": "Voxtral-Mini-4B-Realtime-Arabic",
            "url": "https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-Arabic",
            "params": "4B (Hub safetensors count 4.43B)"
          },
          {
            "name": "Voxtral-4B-TTS-2603",
            "url": "https://huggingface.co/mistralai/Voxtral-4B-TTS-2603",
            "params": "4B"
          },
          {
            "name": "Voxtral-Small-24B-2507",
            "url": "https://huggingface.co/mistralai/Voxtral-Small-24B-2507",
            "params": "24B (Hub safetensors count 24.26B)"
          },
          {
            "name": "Voxtral-Mini-3B-2507",
            "url": "https://huggingface.co/mistralai/Voxtral-Mini-3B-2507",
            "params": "3B (Hub safetensors count 4.68B)"
          }
        ],
        "olderCount": 49
      },
      "area": "voice",
      "provenance": {
        "legalEntity": "Mistral AI (RCS Paris 952 418 325)",
        "domain": "mistral.ai",
        "domainRegistered": "2019-05-15",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602/commits/main",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "The files sit in Mistral's verified mistralai organisation, and Mistral's announcement links to them. Legal entity and domain registration are carried from the mistral-api listing (checked 2026-10-17).",
          "Nothing is hosted in this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. The hosted model is mistral-voxtral-transcribe.",
          "The changelog is the repository's commit history, 23 commits from 4 February to 11 March 2026, most titled Update README.md.",
          "mistral.ai/.well-known/security.txt answered on 21 October 2026 with a security contact, an expiry of 5 May 2027 and a HackerOne submission policy.",
          "The card's ExecuTorch link, under mistral-labs, redirects to the unverified mistral-experimental organisation (Mistral Experimental), which also holds audio.cpp GGUF builds; counted as the publisher's linked organisation."
        ],
        "score": 81
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602.json"
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    "facts": [
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      {
        "a": "inclusionAI (Ant Group)",
        "b": "Mistral AI",
        "name": "Vendor"
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      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
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      {
        "a": "Free",
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        "name": "Pricing"
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      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "MIT",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-20",
        "b": "2026-03-11",
        "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"
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        "a": "",
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        "a": "",
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        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.5/5 (4)",
        "b": "3.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on agent interface.",
        "question": "Which is better for AI agents, Ming-3.0-audio-tiny or Voxtral Mini 4B Realtime 2602?"
      }
    ],
    "goodFor": [
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        "goodFor": "Self-hosted Chinese and English transcription, audio questions and spoken replies with tool calls on one GPU with about 25 GB free",
        "slug": "ming-3-0-audio-tiny",
        "watchFor": "No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches"
      },
      {
        "aheadOn": [
          "Reach, 85 against 45",
          "Risk, 60 against 50",
          "Documentation and upkeep, 54 against 46"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.5 against 2.5 out of 5"
        ],
        "goodFor": "Agents and apps that need live subtitles or voice-assistant transcription in the 13 languages on a single 16 GB GPU",
        "slug": "voxtral-mini-4b-realtime-2602",
        "watchFor": "Plain text out; no timestamps, segments, diarisation or JSON documented for the weights"
      }
    ],
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      "capability": "speech.stt",
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      {
        "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-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-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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Ming-3.0-asr-tiny vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-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-voxtral-mini-4b-realtime-2602.json",
        "title": "Ming-3.0-audio-flash vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-voxtral-mini-4b-realtime-2602"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-5-asr-streaming-0-6b.json",
        "title": "Ming-3.0-audio-tiny vs Nemotron 3.5 ASR Streaming 0.6B",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-5-asr-streaming-0-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Ming-3.0-audio-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3.json",
        "title": "Ming-3.0-audio-tiny vs Parakeet TDT 0.6B v3",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b.json",
        "title": "Ming-3.0-audio-tiny vs Qwen3-ASR-0.6B",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-1-7b.json",
        "title": "Ming-3.0-audio-tiny vs Qwen3-ASR-1.7B",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ming-3.0-audio-tiny vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3.json",
        "title": "Ming-3.0-audio-tiny vs Underdog Bark 3.0",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Nemotron 3.5 ASR Streaming 0.6B vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602"
      },
      {
        "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Parakeet TDT 0.6B v3 vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-voxtral-mini-4b-realtime-2602"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Qwen3-ASR-0.6B vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-voxtral-mini-4b-realtime-2602"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Qwen3-ASR-1.7B vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-voxtral-mini-4b-realtime-2602"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-voxtral-mini-4b-realtime-2602"
      },
      {
        "json": "https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Underdog Bark 3.0 vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 40,
        "edge": "voxtral-mini-4b-realtime-2602",
        "key": "reach",
        "ming-3-0-audio-tiny": 45,
        "name": "Reach",
        "voxtral-mini-4b-realtime-2602": 85,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "voxtral-mini-4b-realtime-2602",
        "key": "risk",
        "ming-3-0-audio-tiny": 50,
        "name": "Risk",
        "voxtral-mini-4b-realtime-2602": 60,
        "weight": 20
      },
      {
        "by": 10,
        "edge": "ming-3-0-audio-tiny",
        "key": "interface",
        "ming-3-0-audio-tiny": 65,
        "name": "Agent interface",
        "voxtral-mini-4b-realtime-2602": 55,
        "weight": 10
      },
      {
        "by": 8,
        "edge": "voxtral-mini-4b-realtime-2602",
        "key": "upkeep",
        "ming-3-0-audio-tiny": 46,
        "name": "Documentation and upkeep",
        "voxtral-mini-4b-realtime-2602": 54,
        "weight": 10
      }
    ],
    "summary": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on agent interface. Both do speech-to-text.",
    "tutorials": [],
    "verdicts": {
      "ming-3-0-audio-tiny": "A small MIT-licensed audio-language model that transcribes Chinese and English, answers questions about audio and replies in a built-in or cloned voice. It needs about 25 GB and SGLang-Omni from main or inclusionAI's own vLLM-Omni and SGLang branches. The main risk is that every loading path runs repository code, with nothing published on safety.",
      "voxtral-mini-4b-realtime-2602": "A transcription cousin for 13 languages that streams text as the speaker talks, with a delay from 80 ms to 2.4 s, under Apache 2.0 and on Mistral's API. It needs a 16 GB GPU at BF16 through vLLM, Transformers or llama.cpp, or under 4 GB at Q4_K. The main gaps are no timestamps, safety results or answers from Mistral."
    }
  },
  "kind": "anchor.page",
  "links": {
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    "html": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602",
    "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.min.md"
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  "markdown": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on agent interface. Both do speech-to-text.\n\n- Ming-3.0-audio-tiny: grade D, 49.8/100, rank #95 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-audio-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-tiny.json\n- Voxtral Mini 4B Realtime 2602: grade C, 60.3/100, rank #55 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602.md · JSON https://www.anchorterminal.com/api/v1/tools/voxtral-mini-4b-realtime-2602.json\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 231 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n- 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### Ming-3.0-audio-tiny (D)\n\nGood for: Self-hosted Chinese and English transcription, audio questions and spoken replies with tool calls on one GPU with about 25 GB free\n\nAhead on:\n- Agent interface, 65 against 55\n\nWatch for: No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches\n\n### Voxtral Mini 4B Realtime 2602 (C)\n\nGood for: Agents and apps that need live subtitles or voice-assistant transcription in the 13 languages on a single 16 GB GPU\n\nAhead on:\n- Reach, 85 against 45\n- Risk, 60 against 50\n- Documentation and upkeep, 54 against 46\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.5 out of 5\n\nWatch for: Plain text out; no timestamps, segments, diarisation or JSON documented for the weights\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-audio-tiny | Voxtral Mini 4B Realtime 2602 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 45 | 85 | Voxtral Mini 4B Realtime 2602 +40 |\n| Risk | 20% (20 this run) | 50 | 60 | Voxtral Mini 4B Realtime 2602 +10 |\n| Agent interface | 10% (10 this run) | 65 | 55 | Ming-3.0-audio-tiny +10 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 54 | Voxtral Mini 4B Realtime 2602 +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **49.8 · D** | **60.3 · C** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-audio-tiny | Voxtral Mini 4B Realtime 2602 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | Mistral AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-03-11 |\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 | 2.5/5 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**Ming-3.0-audio-tiny.** A small MIT-licensed audio-language model that transcribes Chinese and English, answers questions about audio and replies in a built-in or cloned voice. It needs about 25 GB and SGLang-Omni from main or inclusionAI's own vLLM-Omni and SGLang branches. The main risk is that every loading path runs repository code, with nothing published on safety.\n\n**Voxtral Mini 4B Realtime 2602.** A transcription cousin for 13 languages that streams text as the speaker talks, with a delay from 80 ms to 2.4 s, under Apache 2.0 and on Mistral's API. It needs a 16 GB GPU at BF16 through vLLM, Transformers or llama.cpp, or under 4 GB at Q4_K. The main gaps are no timestamps, safety results or answers from Mistral.\n\n## Before you call either\n\n### Ming-3.0-audio-tiny\n\n1. Fastest start is SGLang-Omni from main, `sgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0`, which exposes OpenAI-style /v1/audio/transcriptions, /v1/audio/speech and /v1/chat/completions\n2. For transcripts use greedy decoding with 10-gram blocking and the validated ASR prompt, and expect a language label, a tab, then the text\n3. Parse tool calls yourself from `\u003ctool_call\u003ename` with `\u003carg_key\u003e` and `\u003carg_value\u003e` pairs, since no runtime parser is named\n4. Thinking is off by default, turn it on with chat_template_kwargs enable_thinking true and strip the `\u003cthink\u003e` block from the reply yourself\n5. In vLLM-Omni pick a voice with audio.voice id from docs/voices.md, and don't combine it with a reference recording\n6. Review the repository's Python before passing trust_remote_code, and consider deleting the __pycache__ folders under talker/ so only readable source runs\n\n### Voxtral Mini 4B Realtime 2602\n\n1. Serve it with `VLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{\"cudagraph_mode\": \"PIECEWISE\"}'` and install soxr, librosa and soundfile; mistral_common 1.9.0 or later comes with vLLM\n2. Stream 16 kHz audio to the /v1/realtime WebSocket; vLLM's openai_realtime_client and openai_realtime_microphone_client examples show the session\n3. Set temperature 0.0. Change the delay with transcription_delay_ms in tekken.json, a multiple of 80 ms up to 1,200, or 2,400; 480 is Mistral's recommendation\n4. One text token is 80 ms, so set --max-model-len to at least seconds / 0.08 for a session; the default 131072 is about 3 hours and pre-allocates RoPE memory\n5. In Transformers (5.2.0 or later), resample with processor.feature_extractor.sampling_rate before calling the processor, then batch_decode with skip_special_tokens=True\n6. For a small machine, ggml-org's Q4_K_M GGUF with its mmproj runs in llama.cpp, and Mistral Experimental's Q4_K runs in audio.cpp\n7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions\n\n## Questions\n\n### Which is better for AI agents, Ming-3.0-audio-tiny or Voxtral Mini 4B Realtime 2602?\n\nVoxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ming-3-0-audio-tiny\", \"b\": \"voxtral-mini-4b-realtime-2602\"}`. From a terminal: `anchor compare ming-3-0-audio-tiny voxtral-mini-4b-realtime-2602`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-tiny.json and https://www.anchorterminal.com/api/v1/tools/voxtral-mini-4b-realtime-2602.json\n\n## Other comparisons with Ming-3.0-audio-tiny or Voxtral Mini 4B Realtime 2602\n\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 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-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 Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602.md)\n- [Ming-3.0-audio-flash vs 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 Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-voxtral-mini-4b-realtime-2602.md)\n- [Ming-3.0-audio-tiny vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Ming-3.0-audio-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Ming-3.0-audio-tiny vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3.md)\n- [Ming-3.0-audio-tiny vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b.md)\n- [Ming-3.0-audio-tiny vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-1-7b.md)\n- [Ming-3.0-audio-tiny vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Ming-3.0-audio-tiny vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602.md)\n- [Parakeet TDT 0.6B v3 vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-voxtral-mini-4b-realtime-2602.md)\n- [Qwen3-ASR-0.6B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-voxtral-mini-4b-realtime-2602.md)\n- [Qwen3-ASR-1.7B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-voxtral-mini-4b-realtime-2602.md)\n- [Qwen3-Omni-30B-A3B-Instruct vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-voxtral-mini-4b-realtime-2602.md)\n- [Underdog Bark 3.0 vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/underdog-bark-3-vs-voxtral-mini-4b-realtime-2602.md)\n",
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    "description": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) to Ming-3.0-audio-tiny's 49.8 (D) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.",
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