# Ming-3.0-asr-tiny vs Nemotron 3.5 ASR Streaming 0.6B

> Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) to Ming-3.0-asr-tiny's 52.5 (D) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b
- Markdown: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.md (~2,700 tokens)
- Slim: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
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- API: https://www.anchorterminal.com/api/v1/index.json
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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

Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-tiny leads on agent interface. Both do speech-to-text.

- Ming-3.0-asr-tiny: grade D, 52.5/100, rank #85 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-asr-tiny.json
- 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
- 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

### Ming-3.0-asr-tiny (D)

Good for: Agents with a CUDA GPU that need local Chinese, English or dialect transcription where names and domain terms matter

Ahead on:
- Agent interface, 60 against 50

Watch for: Loading runs the publisher's Python with trust_remote_code, and the reference path needs a CUDA GPU

### 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:
- Reach, 70 against 50
- Risk, 60 against 55
- Documentation and upkeep, 64 against 46

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


## Score by category

| Category | Weight | Ming-3.0-asr-tiny | Nemotron 3.5 ASR Streaming 0.6B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 70 | Nemotron 3.5 ASR Streaming 0.6B +20 |
| Risk | 20% (20 this run) | 55 | 60 | Nemotron 3.5 ASR Streaming 0.6B +5 |
| Agent interface | 10% (10 this run) | 60 | 50 | Ming-3.0-asr-tiny +10 |
| Documentation and upkeep | 10% (10 this run) | 46 | 64 | Nemotron 3.5 ASR Streaming 0.6B +18 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **52.5 · D** | **59.8 · C** | |

## Facts side by side

| Fact | Ming-3.0-asr-tiny | Nemotron 3.5 ASR Streaming 0.6B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 2026-09-10 |
| 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 | 3/5 (4) | 2.8/5 (4) |

## Verdicts

**Ming-3.0-asr-tiny.** A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no 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.

## Before you call either

### Ming-3.0-asr-tiny

1. Start it with `hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny`, then `pip install -r requirements.txt` in Python 3.12 with CUDA (Transformers 5.12.1, fla-core 0.5.2, PyTorch 2.11.0), then `python examples/transcribe.py --model . --audio speech.wav --language-hint en`
2. To serve it, `sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny` from SGLang-Omni's main branch, then POST the WAV to /v1/audio/transcriptions with response_format json and read the text field
3. Parse raw Transformers output with decode_transcript from asr_request.py, which strips the language label before the tab
4. Pass language zh or en when known, anything else is rejected, and append a topic or hotwords to the base instruction with no newline
5. Load with dtype torch.bfloat16 rather than casting the model, use greedy decoding, max_new_tokens 256 and no_repeat_ngram_size 10, and don't apply the chat template
6. Split long recordings yourself or use --audio-chunk-frames, since input and output share 8,192 tokens
7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions

### 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

## Questions

### Which is better for AI agents, Ming-3.0-asr-tiny or Nemotron 3.5 ASR Streaming 0.6B?

Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-tiny leads on agent interface.


## For agents

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

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