# Ming-3.0-audio-flash vs Qwen3-ASR-1.7B

> Qwen3-ASR-1.7B scores 64.4 (B) to Ming-3.0-audio-flash's 42.5 (E) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-1-7b
- Markdown: https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-1-7b.md (~2,750 tokens)
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- 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

Qwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 3 of 4 scored categories. Ming-3.0-audio-flash leads on documentation and upkeep. Both do speech-to-text.

- Ming-3.0-audio-flash: grade E, 42.5/100, rank #98 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-audio-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-flash.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
- 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-audio-flash (E)

Good for: Teams with a multi-GPU server who want Chinese and English transcription, audio understanding and spoken replies from one MIT model

Ahead on:
- Documentation and upkeep, 49 against 33

Watch for: 259.3 GB of BF16 files and no quantised build, so it needs a multi-GPU server and doesn't fit 128 GB

### 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:
- Reach, 75 against 25
- Risk, 55 against 50
- Agent interface, 75 against 65

Also in its favour:
- Rated higher by the reviewer agents, 3.3 against 2.3 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 | Ming-3.0-audio-flash | Qwen3-ASR-1.7B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 25 | 75 | Qwen3-ASR-1.7B +50 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3-ASR-1.7B +5 |
| Agent interface | 10% (10 this run) | 65 | 75 | Qwen3-ASR-1.7B +10 |
| Documentation and upkeep | 10% (10 this run) | 49 | 33 | Ming-3.0-audio-flash +16 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **42.5 · E** | **64.4 · B** | |

## Facts side by side

| Fact | Ming-3.0-audio-flash | Qwen3-ASR-1.7B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 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 | 2.3/5 (4) | 3.3/5 (4) |

## Verdicts

**Ming-3.0-audio-flash.** Ming-3.0-audio-flash is a cousin for Chinese and English transcription, questions about long audio and spoken replies in built-in or cloned voices, under MIT. It needs about 260 GB on a multi-GPU server, with inclusionAI's own runtime branches or Transformers remote code. The main risks are running repository code to load it and no published 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

### Ming-3.0-audio-flash

1. Start it with inclusionAI's recipe, `hf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash && export MODEL_PATH="$PWD/inclusionAI/Ming-3.0-audio-flash" && pip install -r requirements-vllm-omni.txt && MING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh "$MODEL_PATH"`, run from a clone of github.com/inclusionAI/Ming-3.0-audio-flash
2. The 128K profile uses tensor parallelism over eight 80 GB H800 GPUs, the SGLang profile four and SGLang-Omni's cookbook two. It doesn't fit the 107.4 GB reference machine
3. Use `MING_OUTPUT_MODE=asr` with the validated prompt and greedy decoding for transcripts, and split the language label from the text at the tab
4. Use `MING_OUTPUT_MODE=text` for audio questions and the Talker profile with `modalities=["text", "audio"]` for spoken replies
5. Send audio as a base64 data URL in an `audio_url` item for vLLM-Omni, or as `input_audio` for SGLang
6. Pick a voice with `audio.voice` and an id, or clone one with `ref_audio` and its exact `prompt_text`, never both
7. Turn thinking on per request with `chat_template_kwargs` `{"enable_thinking": true}`, it's off by default and the reasoning comes back in think tags
8. Tool calls follow the template's `<tool_call>` with `<arg_key>` and `<arg_value>` pairs, and no parser is named, so parse them yourself
9. Review the repository's Python and the talker/ code, .pyc files included, before loading with trust_remote_code

### 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, Ming-3.0-audio-flash or Qwen3-ASR-1.7B?

Qwen3-ASR-1.7B scores 64.4 (B) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 3 of 4 scored categories. Ming-3.0-audio-flash leads on documentation and upkeep.


## For agents

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

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