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

Nemotron 3.5 Transcribe 30B-A4B vs Parakeet Unified EN 0.6B

Nemotron 3.5 Transcribe 30B-A4B scores 61 (C) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in 3 of 4 scored categories. Both do speech-to-text.

Best speech-to-text APIs for AI agents · All 386 stt comparisons

Which one, for what

Nemotron 3.5 Transcribe 30B-A4B C

Best 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 50
  • Risk, 75 against 35
  • Agent interface, 85 against 35

Watch for

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

Parakeet Unified EN 0.6B D

Best for English voice agents and captioning that want one small model for both recorded and live audio and already run NeMo

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

The only file is a NeMo archive holding a pickle checkpoint

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Agent interface Nemotron 3.5 Transcribe 30B-A4B +50

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.

Parakeet Unified EN 0.6B 35

Chat template 0, no chat template and no prompt. 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 0, Read for a speech model as typed outputs. Reasoning control 0, A speech recognition model with no reasoning. Context 0, no token context is stated. 0 + 25 + 10 + 0 + 0 + 0 = 35.

Risk Nemotron 3.5 Transcribe 30B-A4B +40

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.

Parakeet Unified EN 0.6B 35

File safety 0, the only weights file is parakeet-unified-en-0.6b.nemo, a NeMo archive whose weights are a PyTorch checkpoint read with torch.load, a pickle format, and the card's documented path loads it. Provenance 15, the file sits 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 archive and git hashes for the rest (5). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 5, 15 less 5 for a use policy beyond the law (NVIDIA's Trustworthy AI terms and the guardrail clause) and 5 for terms the licensor can change or revoke, as read for NVIDIA's other listings under the same licence. 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). 0 + 15 + 0 + 5 + 5 + 10 = 35.

Reach Nemotron 3.5 Transcribe 30B-A4B +25

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.

Parakeet Unified EN 0.6B 50

Memory 30, the only build is the FP32 NeMo archive, 2.47 GB, and 2.47 GB × 1.2 = 3.0 GB, under 16 GB. Runtimes 0, none of the six runs it in a released version. Builds 0, NVIDIA publishes no quantised build of it. Terms 10, the NVIDIA Open Model License is a custom licence, not OSI-approved, that lets anyone use the model commercially for free under a use policy, which is the custom-licence band, as on NVIDIA's other listings. Download 10, the Hub API reports gated false on 10 November 2026, so the file downloads with no account and nothing to accept. Hosted 0, Hugging Face's inference provider mapping is empty on 10 November 2026, and the card names no NVIDIA API that serves it. 30 + 0 + 0 + 10 + 10 + 0 = 50.

Score by category

CategoryWeight this runNemotron 3.5 Transcribe 30B-A4BParakeet Unified EN 0.6BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%257550Nemotron 3.5 Transcribe 30B-A4B +25
Risk20%207535Nemotron 3.5 Transcribe 30B-A4B +40
Agent interface10%108535Nemotron 3.5 Transcribe 30B-A4B +50
Documentation and upkeep10%107779Parakeet Unified EN 0.6B +2
Negative events≤1500
TotalC 61/100D 47.5/100

Facts side by side

FactNemotron 3.5 Transcribe 30B-A4BParakeet Unified EN 0.6B
KindOpen-weight modelOpen-weight model
VendorNVIDIANVIDIA
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceOpenMDW-1.1NVIDIA Open Model License
Read-only variant documentednono
llms.txtnono
Last release2026-10-292026-06-09
Terms last updatednot read yet2025-10-24
Privacy policy last updatedno document linkedno document linked
Customer content may train modelsnot read yetyes
Terms restrict automated accessnot read yetnot found in the text
Terms restrict benchmarkingnot read yetnot found in the text
Terms or service can change without noticenot read yetnot found in the text
Arbitration or class-action waivernot read yetnot found in the text

Verdicts

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.

Parakeet Unified EN 0.6B

One English model for batch and live transcription, with a well-documented data list and an NVIDIA team that answers. It runs only in NVIDIA NeMo from a pickle-format archive, with no safetensors, GGUF or quantised build, returns plain text without timestamps, and hasn't changed since June.

Before you call either

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

Parakeet Unified EN 0.6B

  1. Load it with NeMo: nemo_asr.models.ASRModel.from_pretrained(model_name='nvidia/parakeet-unified-en-0.6b'), then transcribe([path])[0].text
  2. Loading a .nemo archive unpickles a PyTorch checkpoint, so only load it from NVIDIA's repository
  3. Send mono wav audio. English only
  4. For live audio use NeMo's chunked RNN-T streaming script with a 5.6 s left context, and pick the chunk and right context from the card's table
  5. Below 240 ms of latency accuracy drops, and at 80 ms NVIDIA recommends its nemotron-speech-streaming-en-0.6b instead
  6. If you need word timestamps, use Parakeet TDT 0.6B v3

Questions

Which is better for AI agents, Nemotron 3.5 Transcribe 30B-A4B or Parakeet Unified EN 0.6B?

Nemotron 3.5 Transcribe 30B-A4B scores 61 (C) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in 3 of 4 scored categories.

Other comparisons with Nemotron 3.5 Transcribe 30B-A4B or Parakeet Unified EN 0.6B

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