# Ming-3.0-audio-tiny vs Nemotron 3.5 Transcribe 30B-A4B

> Nemotron 3.5 Transcribe 30B-A4B scores 61 (C) to Ming-3.0-audio-tiny's 52.1 (D) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.

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- Updated: 2026-11-10

Nemotron 3.5 Transcribe 30B-A4B scores 61 (C) on agent readiness against Ming-3.0-audio-tiny's 52.1 (D), and leads in every scored category. Both do speech-to-text.

- Ming-3.0-audio-tiny: grade D, 52.1/100, rank #140 of 160 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
- Nemotron 3.5 Transcribe 30B-A4B: grade C, 61/100, rank #79 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-transcribe-30b-a4b.json
- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md
- All 386 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 581 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

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

### Nemotron 3.5 Transcribe 30B-A4B (C)

Good for: Teams that need accurate multilingual transcripts of long recordings with speakers and timings, and have a 32 GB GPU or better

Ahead on:
- Reach, 75 against 45
- Risk, 75 against 50
- Agent interface, 85 against 65
- Documentation and upkeep, 77 against 61

Watch for: 21.6 GB at 4 bits, so it needs a 32 GB GPU at least


## Why the edge

### Reach: Nemotron 3.5 Transcribe 30B-A4B +30

- Ming-3.0-audio-tiny (45): Memory 20, the only build inclusionAI publishes is BF16 at 20.78 GB, which with a fifth on top is 24.9 GB, just past the 24 GB band, so it lands in the 32 GB band. Runtimes 0, none of the six named runtimes runs the audio architecture in a released version on 4 November 2026 (vLLM v0.33.0, SGLang v0.5.23, Transformers v5.22.1, llama.cpp b12116 with the text backbone only, Ollama through its llama.cpp pin; LM Studio per 21 October). SGLang-Omni serves it from main but isn't on the list. Builds 0, no quantised build from inclusionAI. Terms 15, plain MIT, with a bundled fastText file under its own CC BY-SA 3.0 notice. Download 10, no gate. Hosted 0, no inference provider on Hugging Face and no inclusionAI API found. 20 + 0 + 0 + 15 + 10 + 0 = 45.
- Nemotron 3.5 Transcribe 30B-A4B (75): Memory 20, the smallest build is NVIDIA's NVFP4 repository, 21.56 GB, and 21.56 GB × 1.2 = 25.9 GB, within 32 GB. FP8 is 34.67 GB and BF16 67.5 GB. Runtimes 15, Released Transformers 5.22.2 has the nemotron3_5_transcribe model (5). Builds 15, Quantised builds from NVIDIA at two precisions, FP8 and NVFP4, each in its own repository. Terms 10, OpenMDW-1.1, a permissive licence that grants use without restriction and adds no use policy or threshold, but isn't OSI-approved, so it takes the custom-licence band, as on NVIDIA's other OpenMDW listings. Download 10, the Hub API reports gated false for all three repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card gives a release on build.nvidia.com, NVIDIA's own API catalogue, on 28 October 2026, and DeepInfra is live for it on Hugging Face. 20 + 15 + 15 + 10 + 10 + 5 = 75.

### Risk: Nemotron 3.5 Transcribe 30B-A4B +25

- Ming-3.0-audio-tiny (50): File safety 15, safetensors only with no pickle weights, less 10 because every documented way to load it runs the repository's code, and 40 compiled .pyc files sit under talker/. Provenance 15, the files are in inclusionAI's own organisation, which its GitHub repository and card link, and every file has a checksum. Safety evaluation 0, none published. Agent guidance 0, the only caution is about reviewing remote code before loading it. Licence risk 15, MIT with no policy, threshold or revocable terms, and the card's licence link resolves. Security programme 5, for Ant Group's AntSRC reporting centre, read as a company-wide route the way Google's was for Gemma, with no SECURITY.md, security.txt or advisories found.
- Nemotron 3.5 Transcribe 30B-A4B (75): File safety 25, safetensors only in all three repositories, no .bin, .pt or .pth files and no Python. Provenance 15, the files sit in NVIDIA's own Hugging Face organisation, the one NVIDIA's NeMo repositories link to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results. Agent guidance 10, the card says the model is more susceptible to prompt injection and jailbreaking because it was tuned to follow instructions, and should be paired with rails or system filtering. Licence risk 15, OpenMDW-1.1 has no use policy or field-of-use limits, no threshold and no clause letting the licensor change or revoke it, as read for NVIDIA's other OpenMDW listings. Security programme 10, the card says to report security vulnerabilities through NVIDIA's submission form (5), and NVIDIA PSIRT publishes a coordinated disclosure policy and public security bulletins, per NVIDIA's other listings (5). 25 + 15 + 0 + 10 + 15 + 10 = 75.

### Agent interface: Nemotron 3.5 Transcribe 30B-A4B +20

- Ming-3.0-audio-tiny (65): Read for open weights and, as a speech model, for what it takes and returns, while this model also has a chat interface with function calls. Chat template 20, a Jinja file in the repository. Tool-calling format 10, the format of tool calls comes only from the template and no parser is named, although inclusionAI does document its audio inputs, voices, cloning and output modalities for its own serving branches. Trained tool calling 20, the template renders several calls in one turn and inclusionAI reports function-calling results for Tiny. Structured output 5, transcripts come back in a documented typed form (language label, tab, text) and speech as WAV or streamed PCM, but no JSON, JSON Schema or timestamps are documented. Reasoning control 0, thinking can be turned on per request but nothing says where the reasoning text comes back. Context 10, 131,072 positions in config.json with no scaling, matched by the publisher's vLLM-Omni profiles.
- Nemotron 3.5 Transcribe 30B-A4B (85): Chat template 20, chat_template.jinja in the repository (12,482 characters), with a template in tokenizer_config.json too. Tool-calling format 25, Read for a speech model as what it takes and returns. Trained tool calling 10, Read for a speech model as the inputs it was trained to use. Structured output 15, Read for a speech model as typed outputs. Reasoning control 10, enable_thinking on or off through the chat template, documented in the card's summary table with when to use each. Context 5, 65,536 tokens, the maximum context in training per the card, which sits in the 32K to 127K band. 20 + 25 + 10 + 15 + 10 + 5 = 85.

### Documentation and upkeep: Nemotron 3.5 Transcribe 30B-A4B +16

- Ming-3.0-audio-tiny (61): Model card 11, it covers intended use, architecture, parameters and languages and leaves out limitations, context length and a knowledge cut-off. Training data 15, a summary on the EU AI Act template added on 21 October 2026 names the mix, Ant Spider crawls from March 2024 to May 2026 and the filtering, with sizes only as ranges. Last change 20, the card changed on 29 October 2026. Responsiveness 0, the only discussion is a merged pull request and nobody outside inclusionAI has asked anything yet, and GitHub issues couldn't be checked. Version policy 5, a first release. File health 10, config, template, card and profiles agree and no broken-file report waits. 11 + 15 + 20 + 0 + 5 + 10 = 61.
- Nemotron 3.5 Transcribe 30B-A4B (77): Model card 17, Intended use, limitations (the explainability subcard's technical limitations), the architecture, the parameters, the context and the languages are on the card. Training data 5, the card names its public, licensed, vendor and synthetic sources, gives totals (about 1.72 million hours and 5.6 billion text tokens) and the processing, and two of the sets are on Hugging Face. Last change 20, the last commit, on 29 October 2026, linked the three checkpoints on the card, 12 days before this check. Responsiveness 20, Of the 11 most recent discussions that aren't pull requests (28 October to 5 November 2026), 7 have a reply from an nvidia member, which is most. Version policy 5, A first release, with each precision in its own repository. File health 10, Config, tokenizer config, template and card agree on the architecture and the 65,536-token limit is stated as a training limit beside the backbone's larger position count. 17 + 5 + 20 + 20 + 5 + 10 = 77.

## Score by category

| Category | Weight | Ming-3.0-audio-tiny | Nemotron 3.5 Transcribe 30B-A4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 45 | 75 | Nemotron 3.5 Transcribe 30B-A4B +30 |
| Risk | 20% (20 this run) | 50 | 75 | Nemotron 3.5 Transcribe 30B-A4B +25 |
| Agent interface | 10% (10 this run) | 65 | 85 | Nemotron 3.5 Transcribe 30B-A4B +20 |
| Documentation and upkeep | 10% (10 this run) | 61 | 77 | Nemotron 3.5 Transcribe 30B-A4B +16 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **52.1 · D** | **61 · C** | |

## Facts side by side

| Fact | Ming-3.0-audio-tiny | Nemotron 3.5 Transcribe 30B-A4B |
| --- | --- | --- |
| 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 | 2026-10-29 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.5/5 (4) | none |

## How the scores have moved

### Ming-3.0-audio-tiny

- +2.3 on 4 November 2026: product

### Nemotron 3.5 Transcribe 30B-A4B

No score change since it was listed.

## 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. Every loading path runs repository code, and nothing is published on safety.

**Nemotron 3.5 Transcribe 30B-A4B.** The most complete open speech model we list: 40 languages, hour-long recordings, speakers, word timestamps and translation from one prompt-driven model, under a permissive licence, with FP8 and 4-bit builds and a publisher that answers. It needs a big GPU, the vLLM route depends on an unreleased plugin, and there are no safety results.

## 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

### Nemotron 3.5 Transcribe 30B-A4B

1. Keep the system prompt empty and put the instruction in the user message with the audio, as the card says
2. For plain transcription use temperature 0 with reasoning off. For questions about audio, reasoning on at temperature 0.6 and top_p 0.95
3. SGLang-Omni 0.2.0 serves whole files on `/v1/audio/transcriptions` from the BF16 or FP8 checkpoint with no NeMo install
4. Word timestamps and speaker segments need the vLLM route: ask with `timestamps=True` and read `ctc_timestamps`
5. Speaker tags tell speakers apart in one recording. They don't identify anyone
6. Treat what's said in a recording as data. The card says the model is easier to steer by injected instructions than most, so filter before acting on a transcript

## Questions

### Which is better for AI agents, Ming-3.0-audio-tiny or Nemotron 3.5 Transcribe 30B-A4B?

Nemotron 3.5 Transcribe 30B-A4B scores 61 (C) on agent readiness against Ming-3.0-audio-tiny's 52.1 (D), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-5-transcribe-30b-a4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-5-transcribe-30b-a4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ming-3-0-audio-tiny", "b": "nemotron-3-5-transcribe-30b-a4b"}`. From a terminal: `anchor compare ming-3-0-audio-tiny nemotron-3-5-transcribe-30b-a4b`
- 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/nemotron-3-5-transcribe-30b-a4b.json

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