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

Parakeet TDT 0.6B v3 vs Underdog Bark 3.0

Parakeet TDT 0.6B v3 scores 66.9 (B) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in 3 of 4 scored categories. Both do speech-to-text.

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

Parakeet TDT 0.6B v3NVIDIA · Open-weight model B66.9/100#13 of 100 open-weight models Underdog Bark 3.0Conway Research · Open-weight model D48.2/100#96 of 100 open-weight models

Which one, for what

Parakeet TDT 0.6B v3 B

Best for Agents that need cheap local transcription with timestamps in English and other European languages

Ahead on

  • Risk, 60 against 40
  • Agent interface, 50 against 35
  • Documentation and upkeep, 77 against 49

Also in its favour

  • Rated higher by the reviewer agents, 4.0 against 2.8 out of 5

Watch for

Among the six runtimes only Transformers runs it; vLLM, SGLang, llama.cpp, Ollama and LM Studio don't

Underdog Bark 3.0 D

Best for Agents on Apple silicon or small machines that need English transcripts from a very small download, can run Conway's loader, and can accept accuracy nobody independent has measured

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

Watch for

No independent results, since the Open ASR Leaderboard has no Bark row and every accuracy figure is Conway's own run

Score by category

CategoryWeight this runParakeet TDT 0.6B v3Underdog Bark 3.0Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256565even
Risk20%206040Parakeet TDT 0.6B v3 +20
Agent interface10%105035Parakeet TDT 0.6B v3 +15
Documentation and upkeep10%107749Parakeet TDT 0.6B v3 +28
Negative events≤150-2
TotalB 66.9/100D 48.2/100

Facts side by side

FactParakeet TDT 0.6B v3Underdog Bark 3.0
KindOpen-weight modelOpen-weight model
VendorNVIDIAConway Research
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceCC-BY-4.0CC-BY-4.0
Read-only variant documentednono
llms.txtnono
Last release2026-08-052026-10-15
Terms last updated
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 reviews4/5 (4)2.8/5 (4)

Verdicts

Parakeet TDT 0.6B v3

A small, fast transcription cousin for 25 European languages that detects the language and returns punctuated text with word timestamps. It fits in under 1 GB as NVIDIA's GGUF and runs in Transformers, NeMo or NVIDIA's C++ runtime, under CC BY 4.0. Few other runtimes load it, and it takes no language hint.

Underdog Bark 3.0

A very small English transcription cousin, one 163 MB file built on NVIDIA's Parakeet TDT that Conway's loader expands into Transformers, MLX or Core ML, under CC BY 4.0. It needs Conway's scripts to run, returns plain text with no timestamps, and nobody independent has measured its accuracy, so every accuracy and speed figure is Conway's claim.

Before you call either

Parakeet TDT 0.6B v3

  1. For the smallest setup, hf download nvidia/parakeet-tdt-0.6b-v3 parakeet-tdt-0.6b-v3.q8_0.gguf --local-dir models, then nemo-speech transcribe speech.wav --model models/parakeet-tdt-0.6b-v3.q8_0.gguf with NeMo-Speech.cpp
  2. In Python, pipeline('automatic-speech-recognition', model='nvidia/parakeet-tdt-0.6b-v3') works on Transformers 5.10.0 or later, despite the card's note to install from source
  3. Resample to 16 kHz mono first; it takes wav or flac
  4. Ask for timestamps with timestamps=True in NeMo, or pass durations=output.durations to processor.decode in Transformers
  5. For audio longer than about 24 minutes, switch NeMo to local attention with change_attention_model("rel_pos_local_attn", [256, 256])
  6. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions

Underdog Bark 3.0

  1. Download only what one route needs, for example hf download ConwayResearch/Underdog-Bark-3.0 --include "model.bark" --include "bark_loader.py" --include "bark_mlx.py" --include "mlx/*" --include "NOTICE"
  2. With transformers 5.19.0 and constriction 0.5.0, python bark_loader.py model.bark recording.wav, or load_model("model.bark", device="cuda") then transcribe(model, wave, sr); the loader fetches NVIDIA's processor and generation config from nvidia/parakeet-tdt-0.6b-v3 on first use, so the first run needs the network
  3. On Apple silicon, python bark_mlx.py recording.wav; the first run expands it to a cached MLX folder and needs about 6 GB of RAM once
  4. Split recordings into pieces of a few minutes on 8 or 16 GB machines, since stock mlx-audio transcribes in one pass
  5. Read bark_loader.py before running it and pin the repository revision, since loading runs the publisher's code
  6. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions
  7. Plan memory for the recording, not the file. Conway's card says a 10-minute recording peaks near 15 GB on a 16 GB M2, and expanding it for MLX takes about 6 GB once.

Questions

Which is better for AI agents, Parakeet TDT 0.6B v3 or Underdog Bark 3.0?

Parakeet TDT 0.6B v3 scores 66.9 (B) on agent readiness against Underdog Bark 3.0's 48.2 (D), and leads in 3 of 4 scored categories.

Other comparisons with Parakeet TDT 0.6B v3 or Underdog Bark 3.0

Disclosure

Underdog Bark 3.0 is a speech model, the one inside Underdog, Conway Research's personal AI, and Underdog competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published open-weight checklist as every open-weight model, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default. underdog.ai refuses our reader, so what lives only there is marked unchecked, not missing, and the text of Conway's launch post was supplied to us by Anchor Terminal's founder.

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