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Head to head · Speech-to-text · October 2026 research run

Cohere Transcribe 03-2026 vs Nemotron 3.5 ASR Streaming 0.6B

Nemotron 3.5 ASR Streaming 0.6B and Cohere Transcribe 03-2026 score within a point of each other on agent readiness, 59.8 (C) and 59.3 (C). Cohere Transcribe 03-2026 leads on capability. Both do speech-to-text.

Best speech-to-text APIs for AI agents · All 231 stt comparisons

Cohere Transcribe 03-2026Cohere · Open-weight model C59.3/100#61 of 100 open-weight models Nemotron 3.5 ASR Streaming 0.6BNVIDIA · Open-weight model C59.8/100#58 of 100 open-weight models

Which one, for what

Cohere Transcribe 03-2026 C

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

Ahead on

  • Capability, 66 against 54

Watch for

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

Nemotron 3.5 ASR Streaming 0.6B C

Best for Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop

Ahead on

  • Reach, 70 against 65
  • Risk, 60 against 50
  • Agent interface, 50 against 45
  • Documentation and upkeep, 64 against 54

Watch for

Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp

Score by category

CategoryWeight this runCohere Transcribe 03-2026Nemotron 3.5 ASR Streaming 0.6BEdge
Capability35%356654Cohere Transcribe 03-2026 +12
Reach25%256570Nemotron 3.5 ASR Streaming 0.6B +5
Risk20%205060Nemotron 3.5 ASR Streaming 0.6B +10
Agent interface10%104550Nemotron 3.5 ASR Streaming 0.6B +5
Documentation and upkeep10%105464Nemotron 3.5 ASR Streaming 0.6B +10
Negative events≤1500
TotalC 59.3/100C 59.8/100

Facts side by side

FactCohere Transcribe 03-2026Nemotron 3.5 ASR Streaming 0.6B
KindOpen-weight modelOpen-weight model
VendorCohereNVIDIA
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0OpenMDW-1.1
Read-only variant documentednono
llms.txtyesno
Last release2026-04-282026-09-10
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno 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 reviews3/5 (4)2.8/5 (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.

Nemotron 3.5 ASR Streaming 0.6B

A small streaming transcription cousin for live audio in about 32 languages, with language detection and 80 ms to 1.12 s latency. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NeMo-Speech.cpp, and several providers host it. The main gaps are no documented timestamps and weak accuracy outside the top 19 locales.

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

Nemotron 3.5 ASR Streaming 0.6B

  1. Quickest local run, pip install "transformers>=5.13.0", then pipeline("automatic-speech-recognition", model="nvidia/nemotron-3.5-asr-streaming-0.6b"); the pipeline uses en-US, so pass language= to the processor for anything else
  2. With NeMo-Speech.cpp, nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto
  3. Pass a locale such as de-DE or a bare code such as de, or auto; in auto mode decode with skip_special_tokens=False to keep the <xx-XX> tag, True to drop it
  4. Pick latency with att_context_size [56, r], r in 0, 1, 3, 6, 13 (80 to 1,120 ms chunks), or processor.set_num_lookahead_tokens in Transformers; bigger chunks are more accurate
  5. Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends
  6. Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle
  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 Nemotron 3.5 ASR Streaming 0.6B?

Nemotron 3.5 ASR Streaming 0.6B and Cohere Transcribe 03-2026 score within a point of each other on agent readiness, 59.8 (C) and 59.3 (C). Cohere Transcribe 03-2026 leads on capability.

Other comparisons with Cohere Transcribe 03-2026 or Nemotron 3.5 ASR Streaming 0.6B

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