# Ming-3.0-audio-flash vs Nemotron 3.5 Transcribe 30B-A4B (slim)

> Nemotron 3.5 Transcribe 30B-A4B scores 61 (C) to Ming-3.0-audio-flash's 42.5 (E) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.

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- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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Nemotron 3.5 Transcribe 30B-A4B scores 61 (C) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in every scored category. Both do speech-to-text.

- Ming-3.0-audio-flash: E 42.5, rank #157 of 160 open-weight models · https://www.anchorterminal.com/tools/ming-3-0-audio-flash.min.md
- Nemotron 3.5 Transcribe 30B-A4B: C 61, rank #79 of 160 open-weight models · https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b.min.md
- Ming-3.0-audio-flash, good for Teams with a multi-GPU server who want Chinese and English transcription, audio understanding and spoken replies from one MIT model
- Nemotron 3.5 Transcribe 30B-A4B, 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 25; Risk, 75 against 50; Agent interface, 85 against 65; Documentation and upkeep, 77 against 49.

| Category | Ming-3.0-audio-flash | Nemotron 3.5 Transcribe 30B-A4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 75 |
| Risk (20%) | 50 | 75 |
| Agent interface (10%) | 65 | 85 |
| Documentation and upkeep (10%) | 49 | 77 |

| Fact (where they differ) | Ming-3.0-audio-flash | Nemotron 3.5 Transcribe 30B-A4B |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | NVIDIA |
| Licence | MIT | OpenMDW-1.1 |
| Last release | 2026-10-20 | 2026-10-29 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Reach, Nemotron 3.5 Transcribe 30B-A4B +50: Ming-3.0-audio-flash 25 (Memory earns 0, because the only build is inclusionAI's BF16 release at 259.3 GB, and a fifth on top is 311.2 GB, above the 256 GB band.); 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.)
- Documentation and upkeep, Nemotron 3.5 Transcribe 30B-A4B +28: Ming-3.0-audio-flash 49 (The model card earns 14, covering five of the seven points and missing limitations and a knowledge cut-off.); 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 a…)
- Risk, Nemotron 3.5 Transcribe 30B-A4B +25: Ming-3.0-audio-flash 50 (File safety earns 15, safetensors weights with no pickle files, less 10 because loading needs trust_remote_code and speech output imports Python from the model…); 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.)
- Agent interface, Nemotron 3.5 Transcribe 30B-A4B +20: Ming-3.0-audio-flash 65 (This model has a chat interface with function calls as well as speech, so the tool-calling line is read for its tool calls, as for Ming-3.0-audio-tiny, and stru…); 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.)