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

> Voxtral Mini 4B Realtime 2602 scores 60.3 (C) 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-audio-tiny-vs-voxtral-mini-4b-realtime-2602
- Markdown: https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.md (~2,750 tokens)
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- Updated: 2026-10-21

Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on agent interface. Both do speech-to-text.

- 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
- 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
- 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-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 55

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

### 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 45
- Risk, 60 against 50
- Documentation and upkeep, 54 against 46

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 2.5 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-audio-tiny | Voxtral Mini 4B Realtime 2602 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 45 | 85 | Voxtral Mini 4B Realtime 2602 +40 |
| Risk | 20% (20 this run) | 50 | 60 | Voxtral Mini 4B Realtime 2602 +10 |
| Agent interface | 10% (10 this run) | 65 | 55 | Ming-3.0-audio-tiny +10 |
| Documentation and upkeep | 10% (10 this run) | 46 | 54 | Voxtral Mini 4B Realtime 2602 +8 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **49.8 · D** | **60.3 · C** | |

## Facts side by side

| Fact | Ming-3.0-audio-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 | 2.5/5 (4) | 3.5/5 (4) |

## Verdicts

**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.

**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-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

### 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-audio-tiny or Voxtral Mini 4B Realtime 2602?

Voxtral Mini 4B Realtime 2602 scores 60.3 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 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-audio-tiny-vs-voxtral-mini-4b-realtime-2602.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-audio-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-audio-tiny", "b": "voxtral-mini-4b-realtime-2602"}`. From a terminal: `anchor compare ming-3-0-audio-tiny voxtral-mini-4b-realtime-2602`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-tiny.json and https://www.anchorterminal.com/api/v1/tools/voxtral-mini-4b-realtime-2602.json

## Other comparisons with Ming-3.0-audio-tiny or Voxtral Mini 4B Realtime 2602

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- [Parakeet TDT 0.6B v3 vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-voxtral-mini-4b-realtime-2602.md)
- [Qwen3-ASR-0.6B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-voxtral-mini-4b-realtime-2602.md)
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- [Qwen3-Omni-30B-A3B-Instruct vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-voxtral-mini-4b-realtime-2602.md)
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