# Cohere Transcribe 03-2026 vs Ming-3.0-asr-tiny

> Cohere Transcribe 03-2026 scores 59.3 (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/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny
- Markdown: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.md (~2,650 tokens)
- Slim: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.json (this page as data, same URL with Accept: application/json)
- 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

Cohere Transcribe 03-2026 scores 59.3 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 2 of 4 scored categories. Ming-3.0-asr-tiny leads on risk and agent interface. Both do speech-to-text.

- Cohere Transcribe 03-2026: grade C, 59.3/100, rank #61 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/cohere-transcribe-03-2026.md · JSON https://www.anchorterminal.com/api/v1/tools/cohere-transcribe-03-2026.json
- 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
- 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

### Cohere Transcribe 03-2026 (C)

Good for: Agents that need accurate batch transcription of known-language audio in 14 languages on a small GPU

Ahead on:
- Reach, 65 against 50
- Documentation and upkeep, 54 against 46

Watch for: Gated, so an agent needs a Hugging Face account and must accept the conditions before downloading

### 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:
- Risk, 55 against 50
- Agent interface, 60 against 45

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


## Score by category

| Category | Weight | Cohere Transcribe 03-2026 | Ming-3.0-asr-tiny | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 50 | Cohere Transcribe 03-2026 +15 |
| Risk | 20% (20 this run) | 50 | 55 | Ming-3.0-asr-tiny +5 |
| Agent interface | 10% (10 this run) | 45 | 60 | Ming-3.0-asr-tiny +15 |
| Documentation and upkeep | 10% (10 this run) | 54 | 46 | Cohere Transcribe 03-2026 +8 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.3 · C** | **52.5 · D** | |

## Facts side by side

| Fact | Cohere Transcribe 03-2026 | Ming-3.0-asr-tiny |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Cohere | inclusionAI (Ant Group) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-04-28 | 2026-10-20 |
| 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 (4) |

## Verdicts

**Cohere Transcribe 03-2026.** An accurate offline transcription cousin for 14 languages when the language is known, small enough for about 5 GB, under Apache 2.0, and also on Cohere's API. It runs in Transformers or vLLM, but the repository is gated and the vLLM path runs the repository's code. Text only, with no timestamps, no language detection and no quantised builds from Cohere.

**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.

## Before you call either

### Cohere Transcribe 03-2026

1. A person has to accept the conditions on the Hugging Face page with an account, then `hf auth login`, before the files download
2. Offline, `pip install "transformers>=5.4.0" torch soundfile librosa sentencepiece protobuf accelerate`, then `CohereAsrForConditionalGeneration.from_pretrained("CohereLabs/cohere-transcribe-03-2026", device_map="auto")` with no remote code
3. Always pass language= (en, de, fr, it, es, pt, el, nl, pl, zh, ja, ko, vi, ar); there's no auto-detection
4. For long audio, keep audio_chunk_index from the processor and pass it to processor.decode to reassemble the chunks
5. Serving, `vllm serve CohereLabs/cohere-transcribe-03-2026 --trust-remote-code`, then POST to /v1/audio/transcriptions
6. Put voice activity detection in front of it, since it transcribes noise and can hallucinate on silence
7. Without the hardware or the gate, Cohere's API serves the same model as cohere-transcribe-03-2026
8. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions

### 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

## Questions

### Which is better for AI agents, Cohere Transcribe 03-2026 or Ming-3.0-asr-tiny?

Cohere Transcribe 03-2026 scores 59.3 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 2 of 4 scored categories. Ming-3.0-asr-tiny leads on risk and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "cohere-transcribe-03-2026", "b": "ming-3-0-asr-tiny"}`. From a terminal: `anchor compare cohere-transcribe-03-2026 ming-3-0-asr-tiny`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cohere-transcribe-03-2026.json and https://www.anchorterminal.com/api/v1/tools/ming-3-0-asr-tiny.json

## Other comparisons with Cohere Transcribe 03-2026 or Ming-3.0-asr-tiny

- [Cohere Transcribe 03-2026 vs Ming-3.0-audio-flash](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash.md)
- [Cohere Transcribe 03-2026 vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-tiny.md)
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- [Ming-3.0-asr-tiny vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny.md)
- [Ming-3.0-asr-tiny vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.md)
- [Ming-3.0-asr-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)
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- [Ming-3.0-asr-tiny vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-0-6b.md)
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- [Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct.md)
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