{ "data": { "a": { "slug": "granite-speech-5-0-2b", "name": "Granite Speech 5.0 2B", "vendor": "IBM", "vendorUrl": "https://www.ibm.com/granite", "kind": "weights", "category": "speech-to-text", "summary": "Granite Speech 5.0 2B is IBM's open-weight speech model of 2.3B parameters for transcription in six languages and speech translation to and from English, with keyword lists. Apache 2.0, with IBM's own GGUF builds from about 2 GB.", "url": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b", "markdownUrl": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-speech-5-0-2b.json", "dns": "granite-speech-5-0-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ibm-granite/granite-speech-5.0-2b" }, { "registry": "huggingface", "name": "ibm-granite/granite-speech-5.0-2b-GGUF" }, { "registry": "ollama", "name": "ibm/granite5-speech" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The safetensors and GGUF repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs little: 3.1 GB at Q8_0 with its audio projector, which IBM recommends, or 5.0 GB in BF16. IBM also runs a trial endpoint on Hugging Face that takes a Hugging Face token (checked 2026-11-10).", "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 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b", "capabilities": [ "speech.stt", "speech.batch", "speech.languages", "speech.translation", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "speech-translation", "multilingual", "keyword-biasing", "small", "apache-2.0", "safetensors", "gguf", "signed", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 61, "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": 1518, "categoryRank": 19, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 55, "reach": 95, "risk": 70, "upkeep": 92 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small, well-kept speech model with a clean licence: six languages, translation, keyword lists, IBM's own GGUF and an Ollama entry, a signed repository and training data listed corpus by corpus. It handles about two minutes of audio at a time and returns plain text, with no timestamps or speakers, and publishes no safety results.", "bestFor": "Agents that need to transcribe or translate short recordings in the six supported languages on modest hardware", "strengths": [ "Apache 2.0, ungated, with a signed repository", "Runs in llama.cpp, Ollama, vLLM and Transformers, each documented", "IBM's own GGUF builds, about 3 GB at the precision it recommends", "Training data listed with hours and sources", "Keyword lists of up to 2,000 terms", "Updated in the last week, and both questions on the repository answered" ], "weaknesses": [ "About two minutes of audio per request, so long recordings must be split", "Plain text out: no timestamps, segments or speaker labels", "Six languages", "Builds below Q8_0 lose accuracy on longer audio, by IBM's own account", "No safety results", "No cut-off for the training data" ], "agentNotes": [ "Split recordings into segments of up to about two minutes of 16 kHz mono audio before sending them", "Use the card's prompt for the task word for word, such as `transcribe the speech with proper punctuation and capitalization.`", "Use Q8_0. IBM says lower precisions lose accuracy on audio over about a minute", "To spell names and jargon right, add a keyword list to the prompt, up to 2,000 terms", "With vLLM, serve with `--max-model-len 2048`, as the card does", "An unknown prompt falls back to transcription, so check that the output is the task you asked for" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 55, "reach": 95, "risk": 70, "upkeep": 92 }, "provenanceScore": 88, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "brew install llama.cpp\nllama-cli -st -hf ibm-granite/granite-speech-5.0-2b-GGUF:Q8_0 --audio audio.wav -p \"transcribe the speech with proper punctuation and capitalization.\"\n# or: ollama run ibm/granite5-speech:2b, or vllm serve ibm-granite/granite-speech-5.0-2b --max-model-len 2048", "http": "# vLLM and llama-server both serve an OpenAI-compatible chat API. Send 16 kHz mono audio of up to about\n# two minutes as base64 in the message, with one of the card's task prompts as the text" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/granite-speech-5-0-2b" }, "sameCompany": [ "granite-embedding-small-english-r2", "granite-guardian", "ibm-watson-stt", "granite-switch", "granite-4-2-30b", "granite-4-2-3b", "granite-4-2-8b" ], "weights": { "trainedBy": "IBM", "builtOn": "An intermediate checkpoint of granite-4.0-1b-base, per the card", "params": "2.3B", "goodFor": "Transcribing and translating short recordings in six languages on a laptop, with a keyword list for names and jargon.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "Transformers" ], "builds": [ { "name": "granite-speech-5.0-2b-GGUF Q4_K_M", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b-GGUF/blob/main/granite-speech-5.0-2b-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.3, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "1.14 GB with the 1.16 GB audio projector. IBM says precisions below Q8_0 lose accuracy on audio over about a minute" }, { "name": "granite-speech-5.0-2b-GGUF Q8_0", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b-GGUF/blob/main/granite-speech-5.0-2b-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 3.12, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ], "note": "1.96 GB with the 1.16 GB audio projector. The build IBM recommends, and the one in Ollama's library" }, { "name": "granite-speech-5.0-2b (BF16 safetensors)", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b/tree/main", "who": "publisher", "precision": "bf16", "sizeGB": 5.04, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "Transformers" ], "note": "Two shards" } ], "run": "llama-cli -st -hf ibm-granite/granite-speech-5.0-2b-GGUF:Q8_0 --audio audio.wav -p \"transcribe the speech with proper punctuation and capitalization.\"", "runBuild": "granite-speech-5.0-2b-GGUF Q8_0", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "IBM (Granite)", "family": "Granite Speech", "url": "https://www.anchorterminal.com/open-weights/#ibm", "others": [ { "name": "granite-speech-5.0-470m-turboctc", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-470m-turboctc", "params": "470M, per the name" }, { "name": "granite-speech-4.1-2b", "url": "https://huggingface.co/ibm-granite/granite-speech-4.1-2b", "params": "2B, per the name" } ], "olderCount": 64 }, "area": "voice", "provenance": { "legalEntity": "International Business Machines Corporation (per IBM's naming guidance, as read for granite-guardian)", "domain": "ibm.com", "domainRegistered": "1986-03-19", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b/commits/main", "securityTxt": "valid", "checked": "2026-11-10", "notes": [ "IBM's legal entity, domain and security.txt are carried from our Granite 4.2 listings.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 28 commits from 7 October to 6 November 2026.", "The card gives 20 October 2026 as the release date." ], "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b.json" }, "answer": "Qwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Granite Speech 5.0 2B's 61 (C), and leads in 1 of 4 scored categories. Granite Speech 5.0 2B leads on reach, risk and documentation and upkeep.", "b": { "slug": "qwen3-asr-1-7b", "name": "Qwen3-ASR-1.7B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "speech-to-text", "summary": "Qwen3-ASR-1.7B is Alibaba's open-weight speech recognition model for 30 languages and 22 Chinese dialects, with language identification, context hints and streaming, published on Hugging Face in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-asr-1-7b.json", "dns": "qwen3-asr-1-7b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-ASR-1.7B" }, { "registry": "huggingface", "name": "Qwen/Qwen3-ASR-1.7B-hf" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Both Qwen repositories report gated false (checked 2026-10-21). The qwen-asr package and reference code are public on GitHub.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it costs the hardware, 4.7 GB of BF16 files in the original repository and 4.1 GB in the Transformers-native -hf repository. Alibaba's Model Studio sells a hosted Qwen3-ASR-Flash, a separately named model that isn't graded here (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/QwenLM/Qwen3-ASR", "capabilities": [ "speech.stt", "speech.batch", "speech.streaming", "speech.languages", "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "speech-to-text", "asr", "multilingual", "chinese-dialects", "contextual-asr", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-07-22", "graded": true, "anchor": { "graded": true, "score": 64.4, "grade": "B", "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": 1518, "categoryRank": 15, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 68, "interface": 75, "reach": 75, "risk": 55, "upkeep": 33 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-02" }, "negative": 0, "verdict": "A small local transcription cousin for 30 languages and 22 Chinese dialects that identifies the language, takes context to get names right and streams through vLLM, under plain Apache 2.0. Its BF16 files need about 5 GB and run in released vLLM, SGLang, llama.cpp and Transformers. The main risk is upkeep, with no publisher replies or safety results.", "bestFor": "Agents that need local multilingual or Chinese-dialect transcription on a small GPU, served through vLLM", "strengths": [ "Apache 2.0 with nothing added, ungated, safetensors only in Qwen's own organisation", "4.1 to 4.7 GB of BF16 files, about 5 GB in memory", "Released vLLM, SGLang, llama.cpp and Transformers run it, vLLM with an OpenAI-compatible transcription endpoint", "Language identification, forced language and free-text context hints, with the output format and parser documented", "Independent Open ASR Leaderboard results for its -hf build, average WER 5.4 on the leaderboard's English short-form sets, which Capability uses (5.75 on the nine public sets alone)" ], "weaknesses": [ "No quantised build from Alibaba, and nothing in Ollama's or LM Studio's library", "Timestamps need a second model, Qwen3-ForcedAligner-0.6B, and streaming works only through vLLM without batching or timestamps", "No Qwen member has replied in its Hugging Face discussions", "No safety results, agent guidance or security contact", "The card gives no parameter figure, context length or limitations" ], "agentNotes": [ "Start it with `vllm serve Qwen/Qwen3-ASR-1.7B` (vLLM v0.32.0 or later), then POST the WAV to /v1/audio/transcriptions or send an audio_url part to /v1/chat/completions", "Split chat-completions output with `parse_asr_output` from qwen-asr, since it returns `language \u003cname\u003e\u003casr_text\u003e` before the transcript", "For Transformers, load Qwen/Qwen3-ASR-1.7B-hf with Transformers 5.13.0 or later and use `processor.apply_transcription_request(audio, language=..., prompt=...)`, then `decode(..., return_format=\"transcription_only\")`", "Put names and domain terms in the system message or `prompt` to bias the transcript, and force the language when you know it", "For timestamps, use `pip install -U qwen-asr` and pass `forced_aligner=\"Qwen/Qwen3-ForcedAligner-0.6B\"` with `return_time_stamps=True`", "Use greedy decoding and raise max_new_tokens for long audio; the report gives single recordings up to 20 minutes", "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.4 } ], "editorialScores": { "capability": 68, "interface": 75, "reach": 75, "risk": 55, "upkeep": 33 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install -U \"qwen-asr[vllm]\"\nqwen-asr-serve Qwen/Qwen3-ASR-1.7B --host 0.0.0.0 --port 8000\n# or, with vLLM itself:\nvllm serve Qwen/Qwen3-ASR-1.7B", "http": "curl http://localhost:8000/v1/audio/transcriptions \\\n -F model=Qwen/Qwen3-ASR-1.7B \\\n -F file=@/path/to/speech.wav" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/qwen3-asr-1-7b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-tts-1-7b-customvoice", "qwen-agentworld-35b-a3b", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "builtOn": "Qwen3-Omni (the foundation model the card names; the technical report gives Qwen3-1.7B as its language model)", "params": "2.04B", "context": 65536, "goodFor": "Multilingual and Chinese-dialect transcription with language identification, context hints and streaming, timestamps through the companion forced aligner.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "who": "publisher", "precision": "bf16", "sizeGB": 4.7, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Original layout, loaded by vLLM, SGLang and Qwen's qwen-asr package" }, { "name": "Qwen3-ASR-1.7B-hf", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B-hf", "who": "publisher", "precision": "bf16", "sizeGB": 4.09, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ], "note": "Transformers-native layout, published 26 June 2026, for Transformers 5.13.0 and later" }, { "name": "ggml-org Qwen3-ASR-1.7B-GGUF Q8_0", "url": "https://huggingface.co/ggml-org/Qwen3-ASR-1.7B-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 2.52, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Model 2.17 GB plus Q8_0 audio projector 0.36 GB, from the llama.cpp project's own organisation, named in llama.cpp's multimodal guide. A BF16 pair (4.71 GB) is in the same repository" } ], "run": "vllm serve Qwen/Qwen3-ASR-1.7B", "runBuild": "Qwen3-ASR-1.7B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "unrestrictedCopies": 3 }, "area": "voice", "provenance": { "legalEntity": "Alibaba Cloud (the registrant of qwen.ai per RDAP, carried from the Qwen3.8-27B check)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The QwenLM/Qwen3-ASR README links the Qwen organisation's Qwen3-ASR collection and the -hf repositories on Hugging Face.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The README's News section has two dated lines, the release on 29 January 2026 and Transformers-native support on 26 June 2026.", "qwen.ai/.well-known/security.txt answered with a redirect on 21 October 2026, and no SECURITY.md is in the GitHub repository." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b.json", "live": { "slug": "qwen3-asr-1-7b", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:18.413964975Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-11-08T15:07:12.468678256Z" }, "updatedAt": "2026-11-10T16:09:18.413964975Z" } }, "edges": [ { "by": 59, "category": "upkeep", "leads": "granite-speech-5-0-2b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17, Intended use, limitations, the architecture, the size, the 16K positions and two-minute segments, and the languages are on the card. Training data 20, the data is listed with sources and sizes: a 39-row table giving each corpus, its task, its hours and a link to where it comes from, with the synthetic sets described. Last change 20, the last commit, on 6 November 2026, updated the card, 4 days before this check. Responsiveness 20, Both discussions that aren't pull requests (21 and 28 October 2026) have a reply from an ibm-granite member. Version policy 5, granite-speech-4.1-2b stays in its own repository and 5.0 came out in a new one, which names its predecessor and gives a release date. File health 10, Config, template and card agree on the architecture and the 16K positions. 17 + 20 + 20 + 20 + 5 + 10 = 92.", "b": "The card covers intended use, architecture and languages and misses limitations, a parameter figure, context length and a cut-off (8). Training data is a general description in the technical report with no cut-off (5). The last change to the model's files was the -hf template on 22 July 2026, 103 days before 2 November (10). No Qwen member has replied to any question in its Hugging Face discussions, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release (5). Files agree with the card, but a loading report and a generation_config fix wait unanswered (5)." }, "scores": { "a": 92, "b": 33 } }, { "by": 20, "category": "reach", "leads": "granite-speech-5-0-2b", "name": "Reach", "reasons": { "a": "Memory 30, the smallest build listed that keeps 4 bits is IBM's Q4_K_M GGUF, 1.14 GB with the 1.16 GB audio projector, and 2.30 GB × 1.2 = 2.8 GB, within 16 GB. IBM recommends Q8_0, 3.12 GB with the projector. Runtimes 20, Released Transformers 5.22.2 and vLLM 0.33.0 have the granite_speech model, llama.cpp b12242 has its converter and audio front end, and Ollama carries it as ibm/granite5-speech, which the card says was tested with v0.40.15. Builds 15, IBM publishes GGUF builds at fourteen quantised precisions, from Q2_K to Q8_0, with the audio projector. Terms 15, Apache 2.0 in the metadata and on the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, IBM runs a Hugging Face Inference Endpoint that serves this model through an OpenAI-compatible API, with the address and code on the card. 30 + 20 + 15 + 15 + 10 + 5 = 95.", "b": "Memory earns 30 because Alibaba's smallest build, the -hf BF16 repository, is 4.09 GB, about 4.9 GB with the fifth added, well within 16 GB. Runtimes earn 20 for vLLM, SGLang, llama.cpp and Transformers, each running it in a released version from its own code, while Ollama and LM Studio have no library entry. Builds score 0 because Alibaba publishes BF16 only. Apache 2.0 with nothing added gives terms the full 15 and the ungated repositories give download the full 10. Hosted too scores 0, with one live provider on Hugging Face and Model Studio serving a separately named Flash model." }, "scores": { "a": 95, "b": 75 } }, { "by": 20, "category": "interface", "leads": "qwen3-asr-1-7b", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (193 characters). Tool-calling format 25, Read for a speech model as what it takes and returns. Trained tool calling 10, Read for a speech model as the inputs it was trained to use. Structured output 0, Read for a speech model as typed outputs. Reasoning control 0, A speech model with no reasoning. Context 0, the language model is extended to 16K positions, per the card and config.json (16,384), below the 32K band, and the card says to send audio in segments of about two minutes. 20 + 25 + 10 + 0 + 0 + 0 = 55.", "b": "Read for a speech model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. Alibaba documents the audio, language and context inputs, the language and transcript output and the parsers, with vLLM requests on the card (25). It was trained to use context and a forced language, one clip at a time (10). Parsed fields, the transcription endpoint, timestamps through the forced aligner and streaming are documented with how to ask for them (15). A Jinja template ships in both repositories (20). Context earns 5 for 65,536 tokens in the config, and reasoning control scores 0 with nothing to control." }, "scores": { "a": 55, "b": 75 } }, { "by": 15, "category": "risk", "leads": "granite-speech-5-0-2b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in either repository. Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the model does with a prompt it doesn't know: it ignores an unfamiliar or malformed prompt and transcribes, which it says limits how audio can steer the system. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the Granite 4.2 3B listing on 4 November 2026 (5). 25 + 15 + 0 + 10 + 15 + 5 = 70.", "b": "File safety gets 25, safetensors only and loaded by the runtimes' own code with a Jinja template. Provenance gets 15 for files in Qwen's own organisation with SHA-256 hashes. Apache 2.0 carries none of the licence deductions, so licence risk keeps 15. Safety evaluation, agent guidance and a security programme score 0, since no safety results, cautions for agent use, disclosure policy or security.txt were found." }, "scores": { "a": 70, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "IBM", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-06", "b": "2026-07-22", "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": "none", "b": "3.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Granite Speech 5.0 2B's 61 (C), and leads in 1 of 4 scored categories. Granite Speech 5.0 2B leads on reach, risk and documentation and upkeep.", "question": "Which is better for AI agents, Granite Speech 5.0 2B or Qwen3-ASR-1.7B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 75", "Risk, 70 against 55", "Documentation and upkeep, 92 against 33" ], "also": null, "goodFor": "Agents that need to transcribe or translate short recordings in the six supported languages on modest hardware", "slug": "granite-speech-5-0-2b", "watchFor": "About two minutes of audio per request, so long recordings must be split" }, { "aheadOn": [ "Agent interface, 75 against 55" ], "also": null, "goodFor": "Agents that need local multilingual or Chinese-dialect transcription on a small GPU, served through vLLM", "slug": "qwen3-asr-1-7b", "watchFor": "No quantised build from Alibaba, and nothing in Ollama's or LM Studio's library" } ], "history": null, "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-granite-speech-5-0-2b.json", "title": "Cohere Transcribe 03-2026 vs Granite Speech 5.0 2B", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-granite-speech-5-0-2b" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-1-7b.json", "title": "Cohere Transcribe 03-2026 vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-asr-tiny.json", "title": "Granite Speech 5.0 2B vs Ming-3.0-asr-tiny", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-asr-tiny" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-flash.json", "title": "Granite Speech 5.0 2B vs Ming-3.0-audio-flash", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-flash" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-tiny.json", "title": "Granite Speech 5.0 2B vs Ming-3.0-audio-tiny", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-tiny" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b.json", "title": "Granite Speech 5.0 2B vs Nemotron 3.5 ASR Streaming 0.6B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-transcribe-30b-a4b.json", "title": "Granite Speech 5.0 2B vs Nemotron 3.5 Transcribe 30B-A4B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-transcribe-30b-a4b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Granite Speech 5.0 2B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-tdt-0-6b-v3.json", "title": "Granite Speech 5.0 2B vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-unified-en-0-6b.json", "title": "Granite Speech 5.0 2B vs Parakeet Unified EN 0.6B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-unified-en-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-0-6b.json", "title": "Granite Speech 5.0 2B vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Granite Speech 5.0 2B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3.json", "title": "Granite Speech 5.0 2B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-voxtral-mini-4b-realtime-2602.json", "title": "Granite Speech 5.0 2B vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-voxtral-mini-4b-realtime-2602" }, { "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-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-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/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-transcribe-30b-a4b-vs-qwen3-asr-1-7b.json", "title": "Nemotron 3.5 Transcribe 30B-A4B vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-1-7b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-1-7b.json", "title": "Parakeet TDT 0.6B v3 vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-qwen3-asr-1-7b.json", "title": "Parakeet Unified EN 0.6B vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-qwen3-asr-1-7b.json", "title": "Qwen3-ASR-0.6B vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3-ASR-1.7B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-underdog-bark-3.json", "title": "Qwen3-ASR-1.7B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-underdog-bark-3" }, { "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" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "granite-speech-5-0-2b", "granite-speech-5-0-2b": 95, "key": "reach", "name": "Reach", "qwen3-asr-1-7b": 75, "weight": 25 }, { "by": 15, "edge": "granite-speech-5-0-2b", "granite-speech-5-0-2b": 70, "key": "risk", "name": "Risk", "qwen3-asr-1-7b": 55, "weight": 20 }, { "by": 20, "edge": "qwen3-asr-1-7b", "granite-speech-5-0-2b": 55, "key": "interface", "name": "Agent interface", "qwen3-asr-1-7b": 75, "weight": 10 }, { "by": 59, "edge": "granite-speech-5-0-2b", "granite-speech-5-0-2b": 92, "key": "upkeep", "name": "Documentation and upkeep", "qwen3-asr-1-7b": 33, "weight": 10 } ], "summary": "Qwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Granite Speech 5.0 2B's 61 (C), and leads in 1 of 4 scored categories. Granite Speech 5.0 2B leads on reach, risk and documentation and upkeep. Both do speech-to-text.", "tasks": null, "tutorials": [], "verdicts": { "granite-speech-5-0-2b": "A small, well-kept speech model with a clean licence: six languages, translation, keyword lists, IBM's own GGUF and an Ollama entry, a signed repository and training data listed corpus by corpus. It handles about two minutes of audio at a time and returns plain text, with no timestamps or speakers, and publishes no safety results.", "qwen3-asr-1-7b": "A small local transcription cousin for 30 languages and 22 Chinese dialects that identifies the language, takes context to get names right and streams through vLLM, under plain Apache 2.0. Its BF16 files need about 5 GB and run in released vLLM, SGLang, llama.cpp and Transformers. The main risk is upkeep, with no publisher replies or safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b", "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b.min.md" }, "markdown": "Qwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Granite Speech 5.0 2B's 61 (C), and leads in 1 of 4 scored categories. Granite Speech 5.0 2B leads on reach, risk and documentation and upkeep. Both do speech-to-text.\n\n- Granite Speech 5.0 2B: grade C, 61/100, rank #67 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-speech-5-0-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-speech-5-0-2b.json\n- Qwen3-ASR-1.7B: grade B, 64.4/100, rank #22 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-asr-1-7b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-asr-1-7b.json\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 386 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n\n## Which one, for what\n\n### Granite Speech 5.0 2B (C)\n\nGood for: Agents that need to transcribe or translate short recordings in the six supported languages on modest hardware\n\nAhead on:\n- Reach, 95 against 75\n- Risk, 70 against 55\n- Documentation and upkeep, 92 against 33\n\nWatch for: About two minutes of audio per request, so long recordings must be split\n\n### Qwen3-ASR-1.7B (B)\n\nGood for: Agents that need local multilingual or Chinese-dialect transcription on a small GPU, served through vLLM\n\nAhead on:\n- Agent interface, 75 against 55\n\nWatch for: No quantised build from Alibaba, and nothing in Ollama's or LM Studio's library\n\n\n## Why the edge\n\n### Documentation and upkeep: Granite Speech 5.0 2B +59\n\n- Granite Speech 5.0 2B (92): Model card 17, Intended use, limitations, the architecture, the size, the 16K positions and two-minute segments, and the languages are on the card. Training data 20, the data is listed with sources and sizes: a 39-row table giving each corpus, its task, its hours and a link to where it comes from, with the synthetic sets described. Last change 20, the last commit, on 6 November 2026, updated the card, 4 days before this check. Responsiveness 20, Both discussions that aren't pull requests (21 and 28 October 2026) have a reply from an ibm-granite member. Version policy 5, granite-speech-4.1-2b stays in its own repository and 5.0 came out in a new one, which names its predecessor and gives a release date. File health 10, Config, template and card agree on the architecture and the 16K positions. 17 + 20 + 20 + 20 + 5 + 10 = 92.\n- Qwen3-ASR-1.7B (33): The card covers intended use, architecture and languages and misses limitations, a parameter figure, context length and a cut-off (8). Training data is a general description in the technical report with no cut-off (5). The last change to the model's files was the -hf template on 22 July 2026, 103 days before 2 November (10). No Qwen member has replied to any question in its Hugging Face discussions, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release (5). Files agree with the card, but a loading report and a generation_config fix wait unanswered (5).\n\n### Reach: Granite Speech 5.0 2B +20\n\n- Granite Speech 5.0 2B (95): Memory 30, the smallest build listed that keeps 4 bits is IBM's Q4_K_M GGUF, 1.14 GB with the 1.16 GB audio projector, and 2.30 GB × 1.2 = 2.8 GB, within 16 GB. IBM recommends Q8_0, 3.12 GB with the projector. Runtimes 20, Released Transformers 5.22.2 and vLLM 0.33.0 have the granite_speech model, llama.cpp b12242 has its converter and audio front end, and Ollama carries it as ibm/granite5-speech, which the card says was tested with v0.40.15. Builds 15, IBM publishes GGUF builds at fourteen quantised precisions, from Q2_K to Q8_0, with the audio projector. Terms 15, Apache 2.0 in the metadata and on the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, IBM runs a Hugging Face Inference Endpoint that serves this model through an OpenAI-compatible API, with the address and code on the card. 30 + 20 + 15 + 15 + 10 + 5 = 95.\n- Qwen3-ASR-1.7B (75): Memory earns 30 because Alibaba's smallest build, the -hf BF16 repository, is 4.09 GB, about 4.9 GB with the fifth added, well within 16 GB. Runtimes earn 20 for vLLM, SGLang, llama.cpp and Transformers, each running it in a released version from its own code, while Ollama and LM Studio have no library entry. Builds score 0 because Alibaba publishes BF16 only. Apache 2.0 with nothing added gives terms the full 15 and the ungated repositories give download the full 10. Hosted too scores 0, with one live provider on Hugging Face and Model Studio serving a separately named Flash model.\n\n### Agent interface: Qwen3-ASR-1.7B +20\n\n- Granite Speech 5.0 2B (55): Chat template 20, chat_template.jinja in the repository (193 characters). Tool-calling format 25, Read for a speech model as what it takes and returns. Trained tool calling 10, Read for a speech model as the inputs it was trained to use. Structured output 0, Read for a speech model as typed outputs. Reasoning control 0, A speech model with no reasoning. Context 0, the language model is extended to 16K positions, per the card and config.json (16,384), below the 32K band, and the card says to send audio in segments of about two minutes. 20 + 25 + 10 + 0 + 0 + 0 = 55.\n- Qwen3-ASR-1.7B (75): Read for a speech model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. Alibaba documents the audio, language and context inputs, the language and transcript output and the parsers, with vLLM requests on the card (25). It was trained to use context and a forced language, one clip at a time (10). Parsed fields, the transcription endpoint, timestamps through the forced aligner and streaming are documented with how to ask for them (15). A Jinja template ships in both repositories (20). Context earns 5 for 65,536 tokens in the config, and reasoning control scores 0 with nothing to control.\n\n### Risk: Granite Speech 5.0 2B +15\n\n- Granite Speech 5.0 2B (70): File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in either repository. Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the model does with a prompt it doesn't know: it ignores an unfamiliar or malformed prompt and transcribes, which it says limits how audio can steer the system. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the Granite 4.2 3B listing on 4 November 2026 (5). 25 + 15 + 0 + 10 + 15 + 5 = 70.\n- Qwen3-ASR-1.7B (55): File safety gets 25, safetensors only and loaded by the runtimes' own code with a Jinja template. Provenance gets 15 for files in Qwen's own organisation with SHA-256 hashes. Apache 2.0 carries none of the licence deductions, so licence risk keeps 15. Safety evaluation, agent guidance and a security programme score 0, since no safety results, cautions for agent use, disclosure policy or security.txt were found.\n\n## Score by category\n\n| Category | Weight | Granite Speech 5.0 2B | Qwen3-ASR-1.7B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 75 | Granite Speech 5.0 2B +20 |\n| Risk | 20% (20 this run) | 70 | 55 | Granite Speech 5.0 2B +15 |\n| Agent interface | 10% (10 this run) | 55 | 75 | Qwen3-ASR-1.7B +20 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 33 | Granite Speech 5.0 2B +59 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **64.4 · B** | |\n\n## Facts side by side\n\n| Fact | Granite Speech 5.0 2B | Qwen3-ASR-1.7B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 2026-07-22 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3.3/5 (4) |\n\n## Verdicts\n\n**Granite Speech 5.0 2B.** A small, well-kept speech model with a clean licence: six languages, translation, keyword lists, IBM's own GGUF and an Ollama entry, a signed repository and training data listed corpus by corpus. It handles about two minutes of audio at a time and returns plain text, with no timestamps or speakers, and publishes no safety results.\n\n**Qwen3-ASR-1.7B.** A small local transcription cousin for 30 languages and 22 Chinese dialects that identifies the language, takes context to get names right and streams through vLLM, under plain Apache 2.0. Its BF16 files need about 5 GB and run in released vLLM, SGLang, llama.cpp and Transformers. The main risk is upkeep, with no publisher replies or safety results.\n\n## Before you call either\n\n### Granite Speech 5.0 2B\n\n1. Split recordings into segments of up to about two minutes of 16 kHz mono audio before sending them\n2. Use the card's prompt for the task word for word, such as `transcribe the speech with proper punctuation and capitalization.`\n3. Use Q8_0. IBM says lower precisions lose accuracy on audio over about a minute\n4. To spell names and jargon right, add a keyword list to the prompt, up to 2,000 terms\n5. With vLLM, serve with `--max-model-len 2048`, as the card does\n6. An unknown prompt falls back to transcription, so check that the output is the task you asked for\n\n### Qwen3-ASR-1.7B\n\n1. Start it with `vllm serve Qwen/Qwen3-ASR-1.7B` (vLLM v0.32.0 or later), then POST the WAV to /v1/audio/transcriptions or send an audio_url part to /v1/chat/completions\n2. Split chat-completions output with `parse_asr_output` from qwen-asr, since it returns `language \u003cname\u003e\u003casr_text\u003e` before the transcript\n3. For Transformers, load Qwen/Qwen3-ASR-1.7B-hf with Transformers 5.13.0 or later and use `processor.apply_transcription_request(audio, language=..., prompt=...)`, then `decode(..., return_format=\"transcription_only\")`\n4. Put names and domain terms in the system message or `prompt` to bias the transcript, and force the language when you know it\n5. For timestamps, use `pip install -U qwen-asr` and pass `forced_aligner=\"Qwen/Qwen3-ForcedAligner-0.6B\"` with `return_time_stamps=True`\n6. Use greedy decoding and raise max_new_tokens for long audio; the report gives single recordings up to 20 minutes\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, Granite Speech 5.0 2B or Qwen3-ASR-1.7B?\n\nQwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Granite Speech 5.0 2B's 61 (C), and leads in 1 of 4 scored categories. Granite Speech 5.0 2B leads on reach, risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-speech-5-0-2b\", \"b\": \"qwen3-asr-1-7b\"}`. From a terminal: `anchor compare granite-speech-5-0-2b qwen3-asr-1-7b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-speech-5-0-2b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-asr-1-7b.json\n\n## Other comparisons with Granite Speech 5.0 2B or Qwen3-ASR-1.7B\n\n- [Cohere Transcribe 03-2026 vs Granite Speech 5.0 2B](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-granite-speech-5-0-2b.md)\n- [Cohere Transcribe 03-2026 vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-1-7b.md)\n- [Granite Speech 5.0 2B vs Ming-3.0-asr-tiny](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-asr-tiny.md)\n- [Granite Speech 5.0 2B vs Ming-3.0-audio-flash](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-flash.md)\n- [Granite Speech 5.0 2B vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-tiny.md)\n- [Granite Speech 5.0 2B vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Granite Speech 5.0 2B vs Nemotron 3.5 Transcribe 30B-A4B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-transcribe-30b-a4b.md)\n- [Granite Speech 5.0 2B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Granite Speech 5.0 2B vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-tdt-0-6b-v3.md)\n- [Granite Speech 5.0 2B vs Parakeet Unified EN 0.6B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-unified-en-0-6b.md)\n- [Granite Speech 5.0 2B vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-0-6b.md)\n- [Granite Speech 5.0 2B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Granite Speech 5.0 2B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3.md)\n- [Granite Speech 5.0 2B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-voxtral-mini-4b-realtime-2602.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-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-tiny vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-1-7b.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 Transcribe 30B-A4B vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-qwen3-asr-1-7b.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-1-7b.md)\n- [Parakeet TDT 0.6B v3 vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-1-7b.md)\n- [Parakeet Unified EN 0.6B vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/parakeet-unified-en-0-6b-vs-qwen3-asr-1-7b.md)\n- [Qwen3-ASR-0.6B vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-qwen3-asr-1-7b.md)\n- [Qwen3-ASR-1.7B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Qwen3-ASR-1.7B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-underdog-bark-3.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", "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": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "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": "Granite Speech 5.0 2B vs Qwen3-ASR-1.7B", "url": "" } ], "description": "Qwen3-ASR-1.7B scores 64.4 (B) to Granite Speech 5.0 2B's 61 (C) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Granite Speech 5.0 2B C 61", "Qwen3-ASR-1.7B B 64.4", "scores" ], "h1": "Granite Speech 5.0 2B vs Qwen3-ASR-1.7B", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b", "published": "2026-10-01", "section": "tools", "title": "Granite Speech 5.0 2B vs Qwen3-ASR-1.7B for AI agents (2026)", "toc": null, "updated": "2026-11-10", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b" }, "tokens": { "markdown": 4050, "slim": 780 }, "version": 1 }