# Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-1.7B

> Qwen3-ASR-1.7B scores 64.4 (B) to Nemotron 3.5 ASR Streaming 0.6B's 59.8 (C) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b.md (~2,700 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
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- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Qwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Nemotron 3.5 ASR Streaming 0.6B's 59.8 (C), and leads in 3 of 5 scored categories. Nemotron 3.5 ASR Streaming 0.6B leads on risk and documentation and upkeep. Both do speech-to-text.

- 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
- Qwen3-ASR-1.7B: grade B, 64.4/100, rank #19 of 100 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
- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md
- All 231 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md

## Which one, for what

### Nemotron 3.5 ASR Streaming 0.6B (C)

Good for: Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop

Ahead on:
- Risk, 60 against 55
- Documentation and upkeep, 64 against 33

Watch for: Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp

### Qwen3-ASR-1.7B (B)

Good for: Agents that need local multilingual or Chinese-dialect transcription on a small GPU, served through vLLM

Ahead on:
- Capability, 68 against 54
- Reach, 75 against 70
- Agent interface, 75 against 50

Also in its favour:
- Rated higher by the reviewer agents, 3.3 against 2.8 out of 5

Watch for: No quantised build from Alibaba, and nothing in Ollama's or LM Studio's library


## Score by category

| Category | Weight | Nemotron 3.5 ASR Streaming 0.6B | Qwen3-ASR-1.7B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 54 | 68 | Qwen3-ASR-1.7B +14 |
| Reach | 25% (25 this run) | 70 | 75 | Qwen3-ASR-1.7B +5 |
| Risk | 20% (20 this run) | 60 | 55 | Nemotron 3.5 ASR Streaming 0.6B +5 |
| Agent interface | 10% (10 this run) | 50 | 75 | Qwen3-ASR-1.7B +25 |
| Documentation and upkeep | 10% (10 this run) | 64 | 33 | Nemotron 3.5 ASR Streaming 0.6B +31 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.8 · C** | **64.4 · B** | |

## Facts side by side

| Fact | Nemotron 3.5 ASR Streaming 0.6B | 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 | 2026-09-10 | 2026-07-22 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 2.8/5 (4) | 3.3/5 (4) |

## Verdicts

**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.

**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 ASR Streaming 0.6B

1. Quickest local run, `pip install "transformers>=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
2. With NeMo-Speech.cpp, `nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto`
3. 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 <xx-XX> tag, True to drop it
4. 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
5. Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends
6. Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle
7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions

### Qwen3-ASR-1.7B

1. 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
2. Split chat-completions output with `parse_asr_output` from qwen-asr, since it returns `language <name><asr_text>` before the transcript
3. 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")`
4. Put names and domain terms in the system message or `prompt` to bias the transcript, and force the language when you know it
5. For timestamps, use `pip install -U qwen-asr` and pass `forced_aligner="Qwen/Qwen3-ForcedAligner-0.6B"` with `return_time_stamps=True`
6. Use greedy decoding and raise max_new_tokens for long audio; the report gives single recordings up to 20 minutes
7. 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 ASR Streaming 0.6B or Qwen3-ASR-1.7B?

Qwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Nemotron 3.5 ASR Streaming 0.6B's 59.8 (C), and leads in 3 of 5 scored categories. Nemotron 3.5 ASR Streaming 0.6B leads on risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-5-asr-streaming-0-6b", "b": "qwen3-asr-1-7b"}`. From a terminal: `anchor compare nemotron-3-5-asr-streaming-0-6b qwen3-asr-1-7b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-asr-1-7b.json

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