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

> Qwen3-ASR-0.6B scores 61.2 (C) 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-0-6b
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b.md (~2,650 tokens)
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- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Qwen3-ASR-0.6B scores 61.2 (C) 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-0.6B: grade C, 61.2/100, rank #32 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-asr-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-asr-0-6b.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-0.6B (C)

Good for: Agents that need fast, cheap local transcription in many languages on a small GPU, including realtime streaming

Ahead on:
- Capability, 59 against 54
- Reach, 75 against 70
- Agent interface, 75 against 50

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-0.6B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 54 | 59 | Qwen3-ASR-0.6B +5 |
| Reach | 25% (25 this run) | 70 | 75 | Qwen3-ASR-0.6B +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-0.6B +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** | **61.2 · C** | |

## Facts side by side

| Fact | Nemotron 3.5 ASR Streaming 0.6B | Qwen3-ASR-0.6B |
| --- | --- | --- |
| 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) | 3/5 (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-0.6B.** A very small local transcription cousin for 30 languages and 22 Chinese dialects, built for throughput and realtime streaming through vLLM, under plain Apache 2.0. Its BF16 files need about 2 GB and run in released vLLM, SGLang, llama.cpp and Transformers. It trades some accuracy for speed, and has 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-0.6B

1. Start it with `vllm serve Qwen/Qwen3-ASR-0.6B` (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. For vLLM's realtime streaming, start it with `--hf-overrides '{"architectures":["Qwen3ASRRealtimeGeneration"]}'`, which vLLM doesn't pick from config.json
3. Split chat-completions output with `parse_asr_output` from qwen-asr, since it returns `language <name><asr_text>` before the transcript
4. For Transformers, load Qwen/Qwen3-ASR-0.6B-hf with Transformers 5.13.0 or later and use `processor.apply_transcription_request(audio, language=..., prompt=...)`
5. Put names and domain terms in the system message or `prompt` to bias the transcript, and force the language when you know it
6. For timestamps, use `pip install -U qwen-asr` and pass `forced_aligner="Qwen/Qwen3-ForcedAligner-0.6B"` with `return_time_stamps=True`
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-0.6B?

Qwen3-ASR-0.6B scores 61.2 (C) 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-0-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b.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-0-6b"}`. From a terminal: `anchor compare nemotron-3-5-asr-streaming-0-6b qwen3-asr-0-6b`
- 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-0-6b.json

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