{ "data": { "a": { "slug": "ming-3-0-asr-tiny", "name": "Ming-3.0-asr-tiny", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "speech-to-text", "summary": "Ming-3.0-asr-tiny is inclusionAI's open-weight speech recognition model for Chinese, English and Chinese dialects, an audio encoder on the Ling-tiny-v3 mixture of experts with 8.9B parameters counted from the files, that takes hotwords or dialogue context as hints, under MIT.", "url": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny", "markdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ming-3-0-asr-tiny.json", "repo": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/Ming-3.0-asr-tiny" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). The cookbook and evaluation tools are in a public GitHub repository.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. inclusionAI sells no per-call access to these weights. Running them costs the hardware, about 17.7 GB of BF16 safetensors on one CUDA GPU; the publisher validated it on an NVIDIA L20Y 80 GB (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://github.com/inclusionAI/Ming-3.0-asr", "capabilities": [ "speech.stt", "speech.batch", "speech.languages", "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "speech-to-text", "asr", "chinese", "dialects", "contextual-asr", "mit", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-20", "graded": true, "anchor": { "graded": true, "score": 52.5, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 21, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no safety results.", "bestFor": "Agents with a CUDA GPU that need local Chinese, English or dialect transcription where names and domain terms matter", "strengths": [ "MIT with nothing added, ungated, files hashed in inclusionAI's own organisation", "Hotwords, a topic or dialogue context can be added to the prompt to bias names and terms", "17.7 GB of BF16 safetensors, about 21 GB in memory, which fits a 24 GB GPU", "Input format, output format and parser are documented, with an OpenAI-compatible transcription endpoint through SGLang-Omni", "Actively updated, with the last change on 20 October 2026" ], "weaknesses": [ "Loading runs the publisher's Python with trust_remote_code, and the reference path needs a CUDA GPU", "Not in any released version of vLLM, SGLang, llama.cpp, Ollama, LM Studio or Transformers, and no quantised build from the publisher", "Plain text output with no word timestamps or speaker diarisation, Chinese and English only", "An 8,192-token context shared by audio and transcript, so long recordings must be chunked", "No training data or safety results, no security route of its own, and no publisher reply in its repository yet" ], "agentNotes": [ "Start it with `hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny`, then `pip install -r requirements.txt` in Python 3.12 with CUDA (Transformers 5.12.1, fla-core 0.5.2, PyTorch 2.11.0), then `python examples/transcribe.py --model . --audio speech.wav --language-hint en`", "To serve it, `sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny` from SGLang-Omni's main branch, then POST the WAV to /v1/audio/transcriptions with response_format json and read the text field", "Parse raw Transformers output with decode_transcript from asr_request.py, which strips the language label before the tab", "Pass language zh or en when known, anything else is rejected, and append a topic or hotwords to the base instruction with no newline", "Load with dtype torch.bfloat16 rather than casting the model, use greedy decoding, max_new_tokens 256 and no_repeat_ngram_size 10, and don't apply the chat template", "Split long recordings yourself or use --audio-chunk-frames, since input and output share 8,192 tokens", "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.5 } ], "editorialScores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 46 }, "provenanceScore": 57, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny\ncd Ming-3.0-asr-tiny\npip install -r requirements.txt # Python 3.12, CUDA, Transformers 5.12.1, PyTorch 2.11.0\npython examples/transcribe.py --model . --audio /path/to/speech.wav --language-hint en", "http": "# after: sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny\ncurl -X POST http://127.0.0.1:8000/v1/audio/transcriptions \\\n -F model=inclusionAI/Ming-3.0-asr-tiny \\\n -F file=@/path/to/speech.wav \\\n -F language=en \\\n -F response_format=json" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/ming-3-0-asr-tiny" }, "sameCompany": [ "ming-3-0-audio-flash", "ming-3-0-audio-tiny", "ming-3-0-tts-tiny", "ling-3-0-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group)", "builtOn": "Ling-tiny-v3", "params": "8.87B", "goodFor": "Speech recognition for Chinese, English and Chinese dialects, taking hotwords or dialogue context as hints.", "runtimes": [], "builds": [ { "name": "Ming-3.0-asr-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny", "who": "publisher", "precision": "bf16", "sizeGB": 17.7, "memoryGB": 24, "format": "safetensors", "note": "Needs CUDA for its KDA kernels, per the publisher" } ], "run": "hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny \u0026\u0026 cd Ming-3.0-asr-tiny \u0026\u0026 pip install -r requirements.txt \u0026\u0026 python examples/transcribe.py --model . --audio /path/to/speech.wav --language-hint en", "runBuild": "Ming-3.0-asr-tiny", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "Ming 3.0", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "Ming-3.0-tts-0.5B", "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B", "params": "0.5B (dense)", "listing": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b" }, { "name": "Ming-3.0-tts-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny", "params": "7.9B total, about 1.3B active (backbone)", "listing": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny" }, { "name": "Ming-3.0-audio-flash", "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-flash", "params": "126B total, 6.5B active", "listing": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash" }, { "name": "Ming-3.0-audio-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny", "params": "9.3B total, 2.7B active", "listing": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny" } ], "olderCount": 54 }, "area": "voice", "provenance": { "legalEntity": "inclusionAI, Ant Group (named in the site footer of inclusion-ai.org; the LICENSE copyright line reads inclusionAI)", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI, and the GitHub organisation's profile describes inclusionAI as Ant Group's open-source projects. The footer reads \"© 2026 inclusionAI, Ant Group\". No registered company name was found.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, 41 commits from 14 to 20 October 2026 whose messages say what changed (pinned dependencies, a unified request format, the validated audio-only weights on 18 October, the SGLang-Omni instructions on 20 October).", "inclusion-ai.org/.well-known/security.txt answered 404 on 21 October 2026. The domain registration date is carried from the RDAP check made for ming-3-0-tts-tiny on 21 October 2026.", "Ant Group runs a company-wide security response centre, AntSRC (https://security.alipay.com/, read on 21 October 2026). Nothing on inclusionAI's card or sites links it." ], "score": 57 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.json" }, "answer": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-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" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "Mistral AI", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "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" }, { "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": "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-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-tiny leads on agent interface.", "question": "Which is better for AI agents, Ming-3.0-asr-tiny or Voxtral Mini 4B Realtime 2602?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 60 against 55" ], "also": null, "goodFor": "Agents with a CUDA GPU that need local Chinese, English or dialect transcription where names and domain terms matter", "slug": "ming-3-0-asr-tiny", "watchFor": "Loading runs the publisher's Python with trust_remote_code, and the reference path needs a CUDA GPU" }, { "aheadOn": [ "Reach, 85 against 50", "Risk, 60 against 55", "Documentation and upkeep, 54 against 46" ], "also": [ "Rated higher by the reviewer agents, 3.5 against 3.0 out of 5" ], "goodFor": "Agents and apps that need live subtitles or voice-assistant transcription in the 13 languages on a single 16 GB GPU", "slug": "voxtral-mini-4b-realtime-2602", "watchFor": "Plain text out; no timestamps, segments, diarisation or JSON documented for the weights" } ], "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-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-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-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-nemotron-3-5-asr-streaming-0-6b.json", "title": "Ming-3.0-asr-tiny vs Nemotron 3.5 ASR Streaming 0.6B", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Ming-3.0-asr-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-parakeet-tdt-0-6b-v3.json", "title": "Ming-3.0-asr-tiny vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-0-6b.json", "title": "Ming-3.0-asr-tiny vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-1-7b.json", "title": "Ming-3.0-asr-tiny vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3.json", "title": "Ming-3.0-asr-tiny vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-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-voxtral-mini-4b-realtime-2602.json", "title": "Ming-3.0-audio-tiny vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-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": 35, "edge": "voxtral-mini-4b-realtime-2602", "key": "reach", "ming-3-0-asr-tiny": 50, "name": "Reach", "voxtral-mini-4b-realtime-2602": 85, "weight": 25 }, { "by": 5, "edge": "voxtral-mini-4b-realtime-2602", "key": "risk", "ming-3-0-asr-tiny": 55, "name": "Risk", "voxtral-mini-4b-realtime-2602": 60, "weight": 20 }, { "by": 5, "edge": "ming-3-0-asr-tiny", "key": "interface", "ming-3-0-asr-tiny": 60, "name": "Agent interface", "voxtral-mini-4b-realtime-2602": 55, "weight": 10 }, { "by": 8, "edge": "voxtral-mini-4b-realtime-2602", "key": "upkeep", "ming-3-0-asr-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-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-tiny leads on agent interface. Both do speech-to-text.", "tutorials": [], "verdicts": { "ming-3-0-asr-tiny": "A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no safety results.", "voxtral-mini-4b-realtime-2602": "A transcription cousin for 13 languages that streams text as the speaker talks, with a delay from 80 ms to 2.4 s, under Apache 2.0 and on Mistral's API. It needs a 16 GB GPU at BF16 through vLLM, Transformers or llama.cpp, or under 4 GB at Q4_K. The main gaps are no timestamps, safety results or answers from Mistral." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602", "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602.min.md" }, "markdown": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-tiny leads on agent interface. Both do speech-to-text.\n\n- Ming-3.0-asr-tiny: grade D, 52.5/100, rank #85 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-asr-tiny.json\n- 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\n## Which one, for what\n\n### Ming-3.0-asr-tiny (D)\n\nGood for: Agents with a CUDA GPU that need local Chinese, English or dialect transcription where names and domain terms matter\n\nAhead on:\n- Agent interface, 60 against 55\n\nWatch for: Loading runs the publisher's Python with trust_remote_code, and the reference path needs a CUDA GPU\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 50\n- Risk, 60 against 55\n- Documentation and upkeep, 54 against 46\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 3.0 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-asr-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) | 50 | 85 | Voxtral Mini 4B Realtime 2602 +35 |\n| Risk | 20% (20 this run) | 55 | 60 | Voxtral Mini 4B Realtime 2602 +5 |\n| Agent interface | 10% (10 this run) | 60 | 55 | Ming-3.0-asr-tiny +5 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 54 | Voxtral Mini 4B Realtime 2602 +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **52.5 · D** | **60.3 · C** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-asr-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 | 3/5 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**Ming-3.0-asr-tiny.** A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no safety results.\n\n**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-asr-tiny\n\n1. Start it with `hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny`, then `pip install -r requirements.txt` in Python 3.12 with CUDA (Transformers 5.12.1, fla-core 0.5.2, PyTorch 2.11.0), then `python examples/transcribe.py --model . --audio speech.wav --language-hint en`\n2. To serve it, `sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny` from SGLang-Omni's main branch, then POST the WAV to /v1/audio/transcriptions with response_format json and read the text field\n3. Parse raw Transformers output with decode_transcript from asr_request.py, which strips the language label before the tab\n4. Pass language zh or en when known, anything else is rejected, and append a topic or hotwords to the base instruction with no newline\n5. Load with dtype torch.bfloat16 rather than casting the model, use greedy decoding, max_new_tokens 256 and no_repeat_ngram_size 10, and don't apply the chat template\n6. Split long recordings yourself or use --audio-chunk-frames, since input and output share 8,192 tokens\n7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions\n\n### 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-asr-tiny or Voxtral Mini 4B Realtime 2602?\n\nVoxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-tiny leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-asr-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-asr-tiny\", \"b\": \"voxtral-mini-4b-realtime-2602\"}`. From a terminal: `anchor compare ming-3-0-asr-tiny voxtral-mini-4b-realtime-2602`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ming-3-0-asr-tiny.json and https://www.anchorterminal.com/api/v1/tools/voxtral-mini-4b-realtime-2602.json\n\n## Other comparisons with Ming-3.0-asr-tiny or Voxtral Mini 4B Realtime 2602\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 Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-voxtral-mini-4b-realtime-2602.md)\n- [Ming-3.0-asr-tiny vs Ming-3.0-audio-flash](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-flash.md)\n- [Ming-3.0-asr-tiny vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny.md)\n- [Ming-3.0-asr-tiny vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Ming-3.0-asr-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Ming-3.0-asr-tiny vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-parakeet-tdt-0-6b-v3.md)\n- [Ming-3.0-asr-tiny vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-0-6b.md)\n- [Ming-3.0-asr-tiny vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-1-7b.md)\n- [Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Ming-3.0-asr-tiny vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3.md)\n- [Ming-3.0-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 Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs 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", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Ming-3.0-asr-tiny vs Voxtral Mini 4B Realtime 2602", "url": "" } ], "description": "Voxtral Mini 4B Realtime 2602 scores 60.3 (C) to Ming-3.0-asr-tiny's 52.5 (D) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Ming-3.0-asr-tiny D 52.5", "Voxtral Mini 4B Realtime 2602 C 60.3", "scores" ], "h1": "Ming-3.0-asr-tiny vs Voxtral Mini 4B Realtime 2602", "image": "https://www.anchorterminal.com/assets/og/compare-ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602.png", "path": "/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602", "published": "2026-10-01", "section": "tools", "title": "Ming-3.0-asr-tiny vs Voxtral Mini 4B Realtime 2602 (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602" }, "tokens": { "markdown": 2750, "slim": 480 }, "version": 1 }