Head to head · Speech-to-text · October 2026 research run
Nemotron 3.5 Transcribe 30B-A4B vs Qwen3-ASR-1.7B
Qwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Nemotron 3.5 Transcribe 30B-A4B's 61 (C), and leads in 0 of 4 scored categories. Nemotron 3.5 Transcribe 30B-A4B leads on risk, agent interface and documentation and upkeep. Both do speech-to-text.
Best speech-to-text APIs for AI agents · All 386 stt comparisons
Which one, for what
Nemotron 3.5 Transcribe 30B-A4B C
Best for Teams that need accurate multilingual transcripts of long recordings with speakers and timings, and have a 32 GB GPU or better
Ahead on
- Risk, 75 against 55
- Agent interface, 85 against 75
- Documentation and upkeep, 77 against 33
Watch for
21.6 GB at 4 bits, so it needs a 32 GB GPU at least
Best for Agents that need local multilingual or Chinese-dialect transcription on a small GPU, served through vLLM
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No quantised build from Alibaba, and nothing in Ollama's or LM Studio's library
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Documentation and upkeep Nemotron 3.5 Transcribe 30B-A4B +44
Nemotron 3.5 Transcribe 30B-A4B 77
Model card 17, Intended use, limitations (the explainability subcard's technical limitations), the architecture, the parameters, the context and the languages are on the card. Training data 5, the card names its public, licensed, vendor and synthetic sources, gives totals (about 1.72 million hours and 5.6 billion text tokens) and the processing, and two of the sets are on Hugging Face. Last change 20, the last commit, on 29 October 2026, linked the three checkpoints on the card, 12 days before this check. Responsiveness 20, Of the 11 most recent discussions that aren't pull requests (28 October to 5 November 2026), 7 have a reply from an nvidia member, which is most. Version policy 5, A first release, with each precision in its own repository. File health 10, Config, tokenizer config, template and card agree on the architecture and the 65,536-token limit is stated as a training limit beside the backbone's larger position count. 17 + 5 + 20 + 20 + 5 + 10 = 77.
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).
Risk Nemotron 3.5 Transcribe 30B-A4B +20
Nemotron 3.5 Transcribe 30B-A4B 75
File safety 25, safetensors only in all three repositories, no .bin, .pt or .pth files and no Python. Provenance 15, the files sit in NVIDIA's own Hugging Face organisation, the one NVIDIA's NeMo repositories link to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results. Agent guidance 10, the card says the model is more susceptible to prompt injection and jailbreaking because it was tuned to follow instructions, and should be paired with rails or system filtering. Licence risk 15, OpenMDW-1.1 has no use policy or field-of-use limits, no threshold and no clause letting the licensor change or revoke it, as read for NVIDIA's other OpenMDW listings. Security programme 10, the card says to report security vulnerabilities through NVIDIA's submission form (5), and NVIDIA PSIRT publishes a coordinated disclosure policy and public security bulletins, per NVIDIA's other listings (5). 25 + 15 + 0 + 10 + 15 + 10 = 75.
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.
Agent interface Nemotron 3.5 Transcribe 30B-A4B +10
Nemotron 3.5 Transcribe 30B-A4B 85
Chat template 20, chat_template.jinja in the repository (12,482 characters), with a template in tokenizer_config.json too. 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 15, Read for a speech model as typed outputs. Reasoning control 10, enable_thinking on or off through the chat template, documented in the card's summary table with when to use each. Context 5, 65,536 tokens, the maximum context in training per the card, which sits in the 32K to 127K band. 20 + 25 + 10 + 15 + 10 + 5 = 85.
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.
Score by category
| Category | Weight this run | Nemotron 3.5 Transcribe 30B-A4B | Qwen3-ASR-1.7B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 75 | 75 | even |
| Risk | 20%20 | 75 | 55 | Nemotron 3.5 Transcribe 30B-A4B +20 |
| Agent interface | 10%10 | 85 | 75 | Nemotron 3.5 Transcribe 30B-A4B +10 |
| Documentation and upkeep | 10%10 | 77 | 33 | Nemotron 3.5 Transcribe 30B-A4B +44 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | B 64.4/100 |
Facts side by side
| Fact | Nemotron 3.5 Transcribe 30B-A4B | Qwen3-ASR-1.7B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | NVIDIA | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | OpenMDW-1.1 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-29 | 2026-07-22 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3.3/5 (4) |
Verdicts
Nemotron 3.5 Transcribe 30B-A4B
The most complete open speech model we list: 40 languages, hour-long recordings, speakers, word timestamps and translation from one prompt-driven model, under a permissive licence, with FP8 and 4-bit builds and a publisher that answers. It needs a big GPU, the vLLM route depends on an unreleased plugin, and there are 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.
Before you call either
Nemotron 3.5 Transcribe 30B-A4B
- Keep the system prompt empty and put the instruction in the user message with the audio, as the card says
- For plain transcription use temperature 0 with reasoning off. For questions about audio, reasoning on at temperature 0.6 and top_p 0.95
- SGLang-Omni 0.2.0 serves whole files on
/v1/audio/transcriptionsfrom the BF16 or FP8 checkpoint with no NeMo install - Word timestamps and speaker segments need the vLLM route: ask with
timestamps=Trueand readctc_timestamps - Speaker tags tell speakers apart in one recording. They don't identify anyone
- Treat what's said in a recording as data. The card says the model is easier to steer by injected instructions than most, so filter before acting on a transcript
Qwen3-ASR-1.7B
- 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_outputfrom qwen-asr, since it returnslanguage <name><asr_text>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=...), thendecode(..., return_format="transcription_only") - Put names and domain terms in the system message or
promptto bias the transcript, and force the language when you know it - For timestamps, use
pip install -U qwen-asrand passforced_aligner="Qwen/Qwen3-ForcedAligner-0.6B"withreturn_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
Questions
Which is better for AI agents, Nemotron 3.5 Transcribe 30B-A4B or Qwen3-ASR-1.7B?
Qwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Nemotron 3.5 Transcribe 30B-A4B's 61 (C), and leads in 0 of 4 scored categories. Nemotron 3.5 Transcribe 30B-A4B leads on risk, agent interface and documentation and upkeep.
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- Nemotron 3.5 Transcribe 30B-A4B vs Qwen3-Omni-30B-A3B-Instruct
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- Parakeet TDT 0.6B v3 vs Qwen3-ASR-1.7B
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Machine-readable
- This page as Markdown
/compare/nemotron-3-5-transcribe-30b-a4b-vs-qwen3-asr-1-7b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/nemotron-3-5-transcribe-30b-a4b.json·/api/v1/tools/qwen3-asr-1-7b.json - From a terminal
anchor compare nemotron-3-5-transcribe-30b-a4b qwen3-asr-1-7b(the CLI) - Over MCP
compare_tools {"a": "nemotron-3-5-transcribe-30b-a4b", "b": "qwen3-asr-1-7b"}at/mcp, no key