# Ming-3.0-asr-tiny vs Voxtral Mini 4B Realtime 2602

> Voxtral Mini 4B Realtime 2602 scores 60.3 (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-voxtral-mini-4b-realtime-2602
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- Updated: 2026-10-21

Voxtral Mini 4B Realtime 2602 scores 60.3 (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
- Voxtral Mini 4B Realtime 2602: grade C, 60.3/100, rank #55 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/voxtral-mini-4b-realtime-2602.md · JSON https://www.anchorterminal.com/api/v1/tools/voxtral-mini-4b-realtime-2602.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 55

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

### Voxtral Mini 4B Realtime 2602 (C)

Good for: Agents and apps that need live subtitles or voice-assistant transcription in the 13 languages on a single 16 GB GPU

Ahead on:
- Reach, 85 against 50
- Risk, 60 against 55
- Documentation and upkeep, 54 against 46

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5

Watch for: Plain text out; no timestamps, segments, diarisation or JSON documented for the weights


## Score by category

| Category | Weight | Ming-3.0-asr-tiny | Voxtral Mini 4B Realtime 2602 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 85 | Voxtral Mini 4B Realtime 2602 +35 |
| Risk | 20% (20 this run) | 55 | 60 | Voxtral Mini 4B Realtime 2602 +5 |
| Agent interface | 10% (10 this run) | 60 | 55 | Ming-3.0-asr-tiny +5 |
| Documentation and upkeep | 10% (10 this run) | 46 | 54 | Voxtral Mini 4B Realtime 2602 +8 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **52.5 · D** | **60.3 · C** | |

## Facts side by side

| Fact | Ming-3.0-asr-tiny | Voxtral Mini 4B Realtime 2602 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | Mistral AI |
| 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 | 2026-03-11 |
| 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) | 3.5/5 (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.

**Voxtral Mini 4B Realtime 2602.** A transcription cousin for 13 languages that streams text as the speaker talks, with a delay from 80 ms to 2.4 s, under Apache 2.0 and on Mistral's API. It needs a 16 GB GPU at BF16 through vLLM, Transformers or llama.cpp, or under 4 GB at Q4_K. The main gaps are no timestamps, safety results or answers from Mistral.

## 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

### Voxtral Mini 4B Realtime 2602

1. Serve it with `VLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{"cudagraph_mode": "PIECEWISE"}'` and install soxr, librosa and soundfile; mistral_common 1.9.0 or later comes with vLLM
2. Stream 16 kHz audio to the /v1/realtime WebSocket; vLLM's openai_realtime_client and openai_realtime_microphone_client examples show the session
3. Set temperature 0.0. Change the delay with transcription_delay_ms in tekken.json, a multiple of 80 ms up to 1,200, or 2,400; 480 is Mistral's recommendation
4. One text token is 80 ms, so set --max-model-len to at least seconds / 0.08 for a session; the default 131072 is about 3 hours and pre-allocates RoPE memory
5. In Transformers (5.2.0 or later), resample with processor.feature_extractor.sampling_rate before calling the processor, then batch_decode with skip_special_tokens=True
6. For a small machine, ggml-org's Q4_K_M GGUF with its mmproj runs in llama.cpp, and Mistral Experimental's Q4_K runs in audio.cpp
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 Voxtral Mini 4B Realtime 2602?

Voxtral Mini 4B Realtime 2602 scores 60.3 (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-voxtral-mini-4b-realtime-2602.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ming-3-0-asr-tiny", "b": "voxtral-mini-4b-realtime-2602"}`. From a terminal: `anchor compare ming-3-0-asr-tiny voxtral-mini-4b-realtime-2602`
- 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/voxtral-mini-4b-realtime-2602.json

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