# Cohere Transcribe 03-2026 vs Qwen3-ASR-0.6B

> Qwen3-ASR-0.6B scores 61.2 (C) to Cohere Transcribe 03-2026's 59.3 (C) 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-qwen3-asr-0-6b
- Markdown: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-0-6b.md (~2,600 tokens)
- Slim: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-0-6b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
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- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Qwen3-ASR-0.6B scores 61.2 (C) on agent readiness against Cohere Transcribe 03-2026's 59.3 (C), and leads in 3 of 5 scored categories. Cohere Transcribe 03-2026 leads on capability and documentation and upkeep. 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
- Qwen3-ASR-0.6B: grade C, 61.2/100, rank #32 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-asr-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-asr-0-6b.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:
- Capability, 66 against 59
- Documentation and upkeep, 54 against 33

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

### Qwen3-ASR-0.6B (C)

Good for: Agents that need fast, cheap local transcription in many languages on a small GPU, including realtime streaming

Ahead on:
- Reach, 75 against 65
- Risk, 55 against 50
- Agent interface, 75 against 45

Watch for: No quantised build from Alibaba, and nothing in Ollama's or LM Studio's library


## Score by category

| Category | Weight | Cohere Transcribe 03-2026 | Qwen3-ASR-0.6B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 66 | 59 | Cohere Transcribe 03-2026 +7 |
| Reach | 25% (25 this run) | 65 | 75 | Qwen3-ASR-0.6B +10 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3-ASR-0.6B +5 |
| Agent interface | 10% (10 this run) | 45 | 75 | Qwen3-ASR-0.6B +30 |
| Documentation and upkeep | 10% (10 this run) | 54 | 33 | Cohere Transcribe 03-2026 +21 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.3 · C** | **61.2 · C** | |

## Facts side by side

| Fact | Cohere Transcribe 03-2026 | Qwen3-ASR-0.6B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Cohere | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-04-28 | 2026-07-22 |
| 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.

**Qwen3-ASR-0.6B.** A very small local transcription cousin for 30 languages and 22 Chinese dialects, built for throughput and realtime streaming through vLLM, under plain Apache 2.0. Its BF16 files need about 2 GB and run in released vLLM, SGLang, llama.cpp and Transformers. It trades some accuracy for speed, and has no publisher replies or 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

### Qwen3-ASR-0.6B

1. Start it with `vllm serve Qwen/Qwen3-ASR-0.6B` (vLLM v0.32.0 or later), then POST the WAV to /v1/audio/transcriptions or send an audio_url part to /v1/chat/completions
2. For vLLM's realtime streaming, start it with `--hf-overrides '{"architectures":["Qwen3ASRRealtimeGeneration"]}'`, which vLLM doesn't pick from config.json
3. Split chat-completions output with `parse_asr_output` from qwen-asr, since it returns `language <name><asr_text>` before the transcript
4. For Transformers, load Qwen/Qwen3-ASR-0.6B-hf with Transformers 5.13.0 or later and use `processor.apply_transcription_request(audio, language=..., prompt=...)`
5. Put names and domain terms in the system message or `prompt` to bias the transcript, and force the language when you know it
6. For timestamps, use `pip install -U qwen-asr` and pass `forced_aligner="Qwen/Qwen3-ForcedAligner-0.6B"` with `return_time_stamps=True`
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 Qwen3-ASR-0.6B?

Qwen3-ASR-0.6B scores 61.2 (C) on agent readiness against Cohere Transcribe 03-2026's 59.3 (C), and leads in 3 of 5 scored categories. Cohere Transcribe 03-2026 leads on capability and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-0-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-0-6b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "cohere-transcribe-03-2026", "b": "qwen3-asr-0-6b"}`. From a terminal: `anchor compare cohere-transcribe-03-2026 qwen3-asr-0-6b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/cohere-transcribe-03-2026.json and https://www.anchorterminal.com/api/v1/tools/qwen3-asr-0-6b.json

## Other comparisons with Cohere Transcribe 03-2026 or Qwen3-ASR-0.6B

- [Cohere Transcribe 03-2026 vs Ming-3.0-asr-tiny](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.md)
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