{ "data": { "a": { "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" }, "answer": "Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 3 of 4 scored categories. Ming-3.0-audio-flash leads on agent interface.", "b": { "slug": "nemotron-3-5-asr-streaming-0-6b", "name": "Nemotron 3.5 ASR Streaming 0.6B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "speech-to-text", "summary": "Nemotron 3.5 ASR Streaming 0.6B is NVIDIA's open-weight streaming speech recognition model, a 600M-parameter FastConformer with an RNN-T decoder, transcribing 32 language-locales with punctuation and an optional detected-language tag at chunk sizes from 80 ms to 1.12 s, under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json", "repo": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/nemotron-3.5-asr-streaming-0.6b" } ], "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).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1 (the Linux Foundation's permissive model licence). NVIDIA sells no per-call access to these weights in this listing. Running them costs the hardware, about 2.6 GB of F32 safetensors or a 0.74 GB Q8_0 GGUF. fal, Together and DeepInfra serve it as inference providers on Hugging Face (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/docs/transformers/en/model_doc/nemotron3_5_asr", "capabilities": [ "speech.stt", "speech.streaming", "speech.batch", "speech.languages", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "streaming", "multilingual", "rnnt", "fastconformer", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-09-10", "graded": true, "anchor": { "graded": true, "score": 59.8, "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": 17, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 54, "interface": 50, "reach": 70, "risk": 60, "upkeep": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A small streaming transcription cousin for live audio in about 32 languages, with language detection and 80 ms to 1.12 s latency. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NeMo-Speech.cpp, and several providers host it. The main gaps are no documented timestamps and weak accuracy outside the top 19 locales.", "bestFor": "Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop", "strengths": [ "Tiny, 0.64B parameters, with a 0.74 GB Q8_0 GGUF from NVIDIA and F32 safetensors of 2.55 GB", "True cache-aware streaming with chunk sizes from 80 ms to 1.12 s chosen at run time", "One model for 32 working locales with automatic language detection and a language tag in the output", "Native in Transformers from v5.13.0, no remote code, and hosted by fal, Together and DeepInfra", "OpenMDW-1.1, permissive with no use policy, ungated, and NVIDIA answers most discussions" ], "weaknesses": [ "Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp", "A pickled NeMo checkpoint sits beside the safetensors and NeMo's documented path loads it", "Plain text out; timestamps and confidence scores are claimed in a subcard but not documented", "Publisher WERs of 23% to 26% across the 13 broad-coverage locales, and 8 locales need fine-tuning", "No safety results, and training data without a cut-off" ], "agentNotes": [ "Quickest local run, `pip install \"transformers\u003e=5.13.0\"`, then `pipeline(\"automatic-speech-recognition\", model=\"nvidia/nemotron-3.5-asr-streaming-0.6b\")`; the pipeline uses en-US, so pass language= to the processor for anything else", "With NeMo-Speech.cpp, `nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto`", "Pass a locale such as de-DE or a bare code such as de, or auto; in auto mode decode with skip_special_tokens=False to keep the \u003cxx-XX\u003e tag, True to drop it", "Pick latency with att_context_size [56, r], r in 0, 1, 3, 6, 13 (80 to 1,120 ms chunks), or processor.set_num_lookahead_tokens in Transformers; bigger chunks are more accurate", "Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends", "Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle", "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.8 } ], "editorialScores": { "capability": 54, "interface": 50, "reach": 70, "risk": 60, "upkeep": 64 }, "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": "pip install \"transformers\u003e=5.13.0\"\npython -c \"from transformers import pipeline; print(pipeline('automatic-speech-recognition', model='nvidia/nemotron-3.5-asr-streaming-0.6b')('speech.wav'))\"" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/nemotron-3-5-asr-streaming-0-6b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "parakeet-tdt-0-6b-v3", "cosmos-reason2-2b", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/nemotron-speech-streaming-en-0.6b (NVIDIA's own English model, extended to 40 locales)", "params": "0.64B", "goodFor": "Low-latency streaming transcription in 32 languages from one small model, with automatic language detection, for voice agents and many parallel streams.", "runtimes": [ "Transformers" ], "builds": [ { "name": "nemotron-3.5-asr-streaming-0.6b (safetensors)", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/model.safetensors", "who": "publisher", "precision": "fp32", "sizeGB": 2.55, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ] }, { "name": "nemotron-3.5-asr-streaming-0.6b.nemo", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/nemotron-3.5-asr-streaming-0.6b.nemo", "who": "publisher", "precision": "fp32", "sizeGB": 2.37, "memoryGB": 8, "format": "nemo", "note": "NeMo checkpoint, a tar archive holding a PyTorch pickle; loads with NeMo from its main branch" }, { "name": "nemotron-3.5-asr-streaming-0.6b.q8_0.gguf", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/nemotron-3.5-asr-streaming-0.6b.q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 0.74, "memoryGB": 8, "format": "gguf", "note": "For NVIDIA's NeMo-Speech.cpp, not llama.cpp (GGUF architecture asr). Re-converted with an embedded SentencePiece tokenizer on 10 September 2026" } ], "run": "hf download nvidia/nemotron-3.5-asr-streaming-0.6b nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --local-dir models \u0026\u0026 nemo-speech transcribe audio.wav --model models/nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto", "runBuild": "nemotron-3.5-asr-streaming-0.6b.q8_0.gguf", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron Labs 3 Elastic 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron Labs 3 Puzzle 75B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16", "params": "75B total, 9B active" }, { "name": "Nemotron 3 Labs Ultra Math SFT", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Labs Ultra Math RL", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL", "params": "550B total, 55B active" }, { "name": "Nemotron Labs 3 Competitive Coding 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4", "params": "550B total, 55B active" } ], "olderCount": 43 }, "area": "voice", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card says the model was developed by NVIDIA and the files sit in NVIDIA's verified nvidia organisation on Hugging Face, which developer.nvidia.com links to. The domain registration date is carried from the RDAP check made for nemotron-3-super-120b-a12b on 21 October 2026.", "Nothing is hosted in this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored. The privacy subcard links NVIDIA's privacy policy.", "The changelog is the repository's commit history, 36 commits from 15 May to 10 September 2026, the last re-converting the GGUF with an embedded SentencePiece tokenizer.", "www.nvidia.com/.well-known/security.txt was not requested again; earlier NVIDIA listings record a 403. The card links NVIDIA's vulnerability submission page." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "NVIDIA", "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": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-20", "b": "2026-09-10", "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": "2.3/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 3 of 4 scored categories. Ming-3.0-audio-flash leads on agent interface.", "question": "Which is better for AI agents, Ming-3.0-audio-flash or Nemotron 3.5 ASR Streaming 0.6B?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 65 against 50" ], "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" }, { "aheadOn": [ "Reach, 70 against 25", "Risk, 60 against 50", "Documentation and upkeep, 64 against 49" ], "also": [ "Rated higher by the reviewer agents, 2.8 against 2.3 out of 5" ], "goodFor": "Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop", "slug": "nemotron-3-5-asr-streaming-0-6b", "watchFor": "Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp" } ], "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash.json", "title": "Cohere Transcribe 03-2026 vs Ming-3.0-audio-flash", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-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/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-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-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-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" }, { "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/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 45, "edge": "nemotron-3-5-asr-streaming-0-6b", "key": "reach", "ming-3-0-audio-flash": 25, "name": "Reach", "nemotron-3-5-asr-streaming-0-6b": 70, "weight": 25 }, { "by": 10, "edge": "nemotron-3-5-asr-streaming-0-6b", "key": "risk", "ming-3-0-audio-flash": 50, "name": "Risk", "nemotron-3-5-asr-streaming-0-6b": 60, "weight": 20 }, { "by": 15, "edge": "ming-3-0-audio-flash", "key": "interface", "ming-3-0-audio-flash": 65, "name": "Agent interface", "nemotron-3-5-asr-streaming-0-6b": 50, "weight": 10 }, { "by": 15, "edge": "nemotron-3-5-asr-streaming-0-6b", "key": "upkeep", "ming-3-0-audio-flash": 49, "name": "Documentation and upkeep", "nemotron-3-5-asr-streaming-0-6b": 64, "weight": 10 } ], "summary": "Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 3 of 4 scored categories. Ming-3.0-audio-flash leads on agent interface. Both do speech-to-text.", "tutorials": [], "verdicts": { "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.", "nemotron-3-5-asr-streaming-0-6b": "A small streaming transcription cousin for live audio in about 32 languages, with language detection and 80 ms to 1.12 s latency. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NeMo-Speech.cpp, and several providers host it. The main gaps are no documented timestamps and weak accuracy outside the top 19 locales." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "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-3-5-asr-streaming-0-6b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b.min.md" }, "markdown": "Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 3 of 4 scored categories. Ming-3.0-audio-flash leads on agent interface. Both do speech-to-text.\n\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- Nemotron 3.5 ASR Streaming 0.6B: grade C, 59.8/100, rank #58 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.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-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 50\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### Nemotron 3.5 ASR Streaming 0.6B (C)\n\nGood for: Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop\n\nAhead on:\n- Reach, 70 against 25\n- Risk, 60 against 50\n- Documentation and upkeep, 64 against 49\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5\n\nWatch for: Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-audio-flash | Nemotron 3.5 ASR Streaming 0.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 25 | 70 | Nemotron 3.5 ASR Streaming 0.6B +45 |\n| Risk | 20% (20 this run) | 50 | 60 | Nemotron 3.5 ASR Streaming 0.6B +10 |\n| Agent interface | 10% (10 this run) | 65 | 50 | Ming-3.0-audio-flash +15 |\n| Documentation and upkeep | 10% (10 this run) | 49 | 64 | Nemotron 3.5 ASR Streaming 0.6B +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **42.5 · E** | **59.8 · C** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-audio-flash | Nemotron 3.5 ASR Streaming 0.6B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | NVIDIA |\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 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-09-10 |\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 | 2.3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\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**Nemotron 3.5 ASR Streaming 0.6B.** A small streaming transcription cousin for live audio in about 32 languages, with language detection and 80 ms to 1.12 s latency. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NeMo-Speech.cpp, and several providers host it. The main gaps are no documented timestamps and weak accuracy outside the top 19 locales.\n\n## Before you call either\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### Nemotron 3.5 ASR Streaming 0.6B\n\n1. Quickest local run, `pip install \"transformers\u003e=5.13.0\"`, then `pipeline(\"automatic-speech-recognition\", model=\"nvidia/nemotron-3.5-asr-streaming-0.6b\")`; the pipeline uses en-US, so pass language= to the processor for anything else\n2. With NeMo-Speech.cpp, `nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto`\n3. Pass a locale such as de-DE or a bare code such as de, or auto; in auto mode decode with skip_special_tokens=False to keep the \u003cxx-XX\u003e tag, True to drop it\n4. Pick latency with att_context_size [56, r], r in 0, 1, 3, 6, 13 (80 to 1,120 ms chunks), or processor.set_num_lookahead_tokens in Transformers; bigger chunks are more accurate\n5. Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends\n6. Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle\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-flash or Nemotron 3.5 ASR Streaming 0.6B?\n\nNemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 3 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/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ming-3-0-audio-flash\", \"b\": \"nemotron-3-5-asr-streaming-0-6b\"}`. From a terminal: `anchor compare ming-3-0-audio-flash nemotron-3-5-asr-streaming-0-6b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-flash.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json\n\n## Other comparisons with Ming-3.0-audio-flash or Nemotron 3.5 ASR Streaming 0.6B\n\n- [Cohere Transcribe 03-2026 vs Ming-3.0-audio-flash](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash.md)\n- [Cohere Transcribe 03-2026 vs 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- [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 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-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 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- [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- [Nemotron 3.5 ASR Streaming 0.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602.md)\n", "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-audio-flash vs Nemotron 3.5 ASR Streaming 0.6B", "url": "" } ], "description": "Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (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.", "facts": [ "Ming-3.0-audio-flash E 42.5", "Nemotron 3.5 ASR Streaming 0.6B C 59.8", "scores" ], "h1": "Ming-3.0-audio-flash vs Nemotron 3.5 ASR Streaming 0.6B", "image": "https://www.anchorterminal.com/assets/og/compare-ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b.png", "path": "/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b", "published": "2026-10-01", "section": "tools", "title": "Ming-3.0-audio-flash vs Nemotron 3.5 ASR Streaming 0.6B (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b" }, "tokens": { "markdown": 2900, "slim": 480 }, "version": 1 }