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

Granite Speech 5.0 2B vs Parakeet Unified EN 0.6B

Granite Speech 5.0 2B scores 61 (C) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in every scored category. Both do speech-to-text.

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

Which one, for what

Granite Speech 5.0 2B C

Best for Agents that need to transcribe or translate short recordings in the six supported languages on modest hardware

Ahead on

  • Reach, 95 against 50
  • Risk, 70 against 35
  • Agent interface, 55 against 35
  • Documentation and upkeep, 92 against 79

Watch for

About two minutes of audio per request, so long recordings must be split

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.

Reach Granite Speech 5.0 2B +45

Granite Speech 5.0 2B 95

Memory 30, the smallest build listed that keeps 4 bits is IBM's Q4_K_M GGUF, 1.14 GB with the 1.16 GB audio projector, and 2.30 GB × 1.2 = 2.8 GB, within 16 GB. IBM recommends Q8_0, 3.12 GB with the projector. Runtimes 20, Released Transformers 5.22.2 and vLLM 0.33.0 have the granite_speech model, llama.cpp b12242 has its converter and audio front end, and Ollama carries it as ibm/granite5-speech, which the card says was tested with v0.40.15. Builds 15, IBM publishes GGUF builds at fourteen quantised precisions, from Q2_K to Q8_0, with the audio projector. Terms 15, Apache 2.0 in the metadata and on the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, IBM runs a Hugging Face Inference Endpoint that serves this model through an OpenAI-compatible API, with the address and code on the card. 30 + 20 + 15 + 15 + 10 + 5 = 95.

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.

Risk Granite Speech 5.0 2B +35

Granite Speech 5.0 2B 70

File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in either repository. Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the model does with a prompt it doesn't know: it ignores an unfamiliar or malformed prompt and transcribes, which it says limits how audio can steer the system. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the Granite 4.2 3B listing on 4 November 2026 (5). 25 + 15 + 0 + 10 + 15 + 5 = 70.

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.

Agent interface Granite Speech 5.0 2B +20

Granite Speech 5.0 2B 55

Chat template 20, chat_template.jinja in the repository (193 characters). 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 model with no reasoning. Context 0, the language model is extended to 16K positions, per the card and config.json (16,384), below the 32K band, and the card says to send audio in segments of about two minutes. 20 + 25 + 10 + 0 + 0 + 0 = 55.

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.

Documentation and upkeep Granite Speech 5.0 2B +13

Granite Speech 5.0 2B 92

Model card 17, Intended use, limitations, the architecture, the size, the 16K positions and two-minute segments, and the languages are on the card. Training data 20, the data is listed with sources and sizes: a 39-row table giving each corpus, its task, its hours and a link to where it comes from, with the synthetic sets described. Last change 20, the last commit, on 6 November 2026, updated the card, 4 days before this check. Responsiveness 20, Both discussions that aren't pull requests (21 and 28 October 2026) have a reply from an ibm-granite member. Version policy 5, granite-speech-4.1-2b stays in its own repository and 5.0 came out in a new one, which names its predecessor and gives a release date. File health 10, Config, template and card agree on the architecture and the 16K positions. 17 + 20 + 20 + 20 + 5 + 10 = 92.

Parakeet Unified EN 0.6B 79

Model card 14, Intended use, limitations (buffered streaming only, accuracy falling at 160 ms and below), architecture, parameters (600M) and the language are there. Training data 20, the card lists the training datasets by name, with hours for the four Granary subsets and about 250,000 hours in all, says how the transcripts were made, and NVIDIA publishes Granary on Hugging Face. Last change 10, the last commit to the repository, on 9 June 2026, edited the card, 154 days before this check and within 180. Responsiveness 20, All 5 discussions that aren't pull requests (8 April to 24 June 2026) have a reply from an NVIDIA member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release of the unified model. File health 10, one archive and a card that agree on the model's name, size and NeMo as the way to load it. 14 + 20 + 10 + 20 + 5 + 10 = 79.

Score by category

CategoryWeight this runGranite Speech 5.0 2BParakeet Unified EN 0.6BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259550Granite Speech 5.0 2B +45
Risk20%207035Granite Speech 5.0 2B +35
Agent interface10%105535Granite Speech 5.0 2B +20
Documentation and upkeep10%109279Granite Speech 5.0 2B +13
Negative events≤1500
TotalC 61/100D 47.5/100

Facts side by side

FactGranite Speech 5.0 2BParakeet Unified EN 0.6B
KindOpen-weight modelOpen-weight model
VendorIBMNVIDIA
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0NVIDIA Open Model License
Read-only variant documentednono
llms.txtnono
Last release2026-11-062026-06-09
Terms last updatedno document linked2025-10-24
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

Granite Speech 5.0 2B

A small, well-kept speech model with a clean licence: six languages, translation, keyword lists, IBM's own GGUF and an Ollama entry, a signed repository and training data listed corpus by corpus. It handles about two minutes of audio at a time and returns plain text, with no timestamps or speakers, and publishes 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

Granite Speech 5.0 2B

  1. Split recordings into segments of up to about two minutes of 16 kHz mono audio before sending them
  2. Use the card's prompt for the task word for word, such as transcribe the speech with proper punctuation and capitalization.
  3. Use Q8_0. IBM says lower precisions lose accuracy on audio over about a minute
  4. To spell names and jargon right, add a keyword list to the prompt, up to 2,000 terms
  5. With vLLM, serve with --max-model-len 2048, as the card does
  6. An unknown prompt falls back to transcription, so check that the output is the task you asked for

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, Granite Speech 5.0 2B or Parakeet Unified EN 0.6B?

Granite Speech 5.0 2B scores 61 (C) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in every scored category.

Other comparisons with Granite Speech 5.0 2B or Parakeet Unified EN 0.6B

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