# Ming-3.0-asr-tiny vs Ming-3.0-audio-tiny

> Ming-3.0-asr-tiny scores 52.5 (D) to Ming-3.0-audio-tiny's 49.8 (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-ming-3-0-audio-tiny
- Markdown: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny.md (~2,650 tokens)
- Slim: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- 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

Ming-3.0-asr-tiny scores 52.5 (D) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 2 of 4 scored categories. Ming-3.0-audio-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
- Ming-3.0-audio-tiny: grade D, 49.8/100, rank #95 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-audio-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-tiny.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
- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md
- All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/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:
- Reach, 50 against 45
- Risk, 55 against 50

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5

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

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

Good for: Self-hosted Chinese and English transcription, audio questions and spoken replies with tool calls on one GPU with about 25 GB free

Ahead on:
- Agent interface, 65 against 60

Watch for: No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches


## Score by category

| Category | Weight | Ming-3.0-asr-tiny | Ming-3.0-audio-tiny | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 45 | Ming-3.0-asr-tiny +5 |
| Risk | 20% (20 this run) | 55 | 50 | Ming-3.0-asr-tiny +5 |
| Agent interface | 10% (10 this run) | 60 | 65 | Ming-3.0-audio-tiny +5 |
| Documentation and upkeep | 10% (10 this run) | 46 | 46 | even |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **52.5 · D** | **49.8 · D** | |

## Facts side by side

| Fact | Ming-3.0-asr-tiny | Ming-3.0-audio-tiny |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | inclusionAI (Ant Group) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 2026-10-20 |
| 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) | 2.5/5 (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.

**Ming-3.0-audio-tiny.** A small MIT-licensed audio-language model that transcribes Chinese and English, answers questions about audio and replies in a built-in or cloned voice. It needs about 25 GB and SGLang-Omni from main or inclusionAI's own vLLM-Omni and SGLang branches. The main risk is that every loading path runs repository code, with nothing published on safety.

## 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

### Ming-3.0-audio-tiny

1. Fastest start is SGLang-Omni from main, `sgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0`, which exposes OpenAI-style /v1/audio/transcriptions, /v1/audio/speech and /v1/chat/completions
2. For transcripts use greedy decoding with 10-gram blocking and the validated ASR prompt, and expect a language label, a tab, then the text
3. Parse tool calls yourself from `<tool_call>name` with `<arg_key>` and `<arg_value>` pairs, since no runtime parser is named
4. Thinking is off by default, turn it on with chat_template_kwargs enable_thinking true and strip the `<think>` block from the reply yourself
5. In vLLM-Omni pick a voice with audio.voice id from docs/voices.md, and don't combine it with a reference recording
6. Review the repository's Python before passing trust_remote_code, and consider deleting the __pycache__ folders under talker/ so only readable source runs

## Questions

### Which is better for AI agents, Ming-3.0-asr-tiny or Ming-3.0-audio-tiny?

Ming-3.0-asr-tiny scores 52.5 (D) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 2 of 4 scored categories. Ming-3.0-audio-tiny leads on agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ming-3-0-asr-tiny", "b": "ming-3-0-audio-tiny"}`. From a terminal: `anchor compare ming-3-0-asr-tiny ming-3-0-audio-tiny`
- 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/ming-3-0-audio-tiny.json

## Other comparisons with Ming-3.0-asr-tiny or Ming-3.0-audio-tiny

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- [Ming-3.0-audio-tiny vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b.md)
- [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)
- [Ming-3.0-audio-tiny vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-omni-30b-a3b-instruct.md)
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- [Ming-3.0-audio-tiny vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.md)

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