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

Parakeet Unified EN 0.6B vs Voxtral Mini 4B Realtime 2602

Voxtral Mini 4B Realtime 2602 scores 62 (B) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in 3 of 4 scored categories. Parakeet Unified EN 0.6B leads on documentation and upkeep. Both do speech-to-text.

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

Which one, for what

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

Ahead on

  • Documentation and upkeep, 79 against 59

Watch for

The only file is a NeMo archive holding a pickle checkpoint

Voxtral Mini 4B Realtime 2602 B

Best for Agents and apps that need live subtitles or voice-assistant transcription in the 13 languages on a single 16 GB GPU

Ahead on

  • Reach, 90 against 50
  • Risk, 60 against 35
  • Agent interface, 55 against 35

Watch for

Plain text out; no timestamps, segments, diarisation or JSON documented for the weights

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 Voxtral Mini 4B Realtime 2602 +40

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.

Voxtral Mini 4B Realtime 2602 90

Memory earns 30, since even the BF16 weights fit 16 GB and Mistral Experimental's Q4_K GGUF needs under 4 GB. Runtimes get 20 for vLLM (v0.33.0), Transformers and llama.cpp, each in a release, and Ollama, whose v0.40.14 release pins llama.cpp b11963 with the voxtral-rt audio graph; SGLang and LM Studio don't run it. Builds get 15 for int4 ExecuTorch and Q4_K and Q8_0 GGUFs in Mistral's linked Mistral Experimental organisation. Terms get 10, because the card adds a prohibition to Apache 2.0, read as added terms. Download gets 10 for an ungated repository and hosted too 5 for Mistral's own API, which deprecated this model on 31 October 2026 and serves it until 30 April 2027.

Risk Voxtral Mini 4B Realtime 2602 +25

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.

Voxtral Mini 4B Realtime 2602 60

File safety gets the full 25, safetensors only and loaded by the runtimes' own code. Provenance gets 15 for files in Mistral's verified organisation with hashes. Licence risk keeps 15, since the card's line restates the law and Apache 2.0 has none of the deductions. The security programme gets 5 for Mistral's security.txt with a HackerOne route, with no public advisories found. Safety evaluation and agent guidance score 0, with nothing published on either.

Agent interface Voxtral Mini 4B Realtime 2602 +20

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.

Voxtral Mini 4B Realtime 2602 55

Read for a speech model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. Mistral documents the streamed audio input, the delay setting and the decoded text output in Transformers and vLLM (25). It was trained for one stream at a delay it's conditioned on (10). No JSON, timestamps or diarisation are documented for the weights (0). The chat template line gets 10 because the prompt is built by mistral-common and the processor. Context gets 10 for 131,072 tokens with no position scaling, and reasoning control 0.

Documentation and upkeep Parakeet Unified EN 0.6B +20

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.

Voxtral Mini 4B Realtime 2602 59

The card covers intended use, architecture, parameters, context and languages, without limitations or a cut-off (14). Training data has only a general description without a cut-off (5). Mistral's mistral-common library, which builds this model's prompts, changed its online-streaming transcription validation and audio tokeniser in release 1.13.0 on 28 October 2026 (20). Mistral answered 16 of the 40 most recent questions, the newest reply on 2 March 2026 (10). It's a first release with a dated name (5). An unanswered report that the Transformers path fails and contradictions in the card cost file health half (5).

Score by category

CategoryWeight this runParakeet Unified EN 0.6BVoxtral Mini 4B Realtime 2602Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255090Voxtral Mini 4B Realtime 2602 +40
Risk20%203560Voxtral Mini 4B Realtime 2602 +25
Agent interface10%103555Voxtral Mini 4B Realtime 2602 +20
Documentation and upkeep10%107959Parakeet Unified EN 0.6B +20
Negative events≤1500
TotalD 47.5/100B 62/100

Facts side by side

FactParakeet Unified EN 0.6BVoxtral Mini 4B Realtime 2602
KindOpen-weight modelOpen-weight model
VendorNVIDIAMistral AI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceNVIDIA Open Model LicenseApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-06-092026-03-11
Terms last updated2025-10-24no document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train modelsyesnot read yet
Terms restrict automated accessnot found in the textnot read yet
Terms restrict benchmarkingnot found in the textnot read yet
Terms or service can change without noticenot found in the textnot read yet
Arbitration or class-action waivernot found in the textnot read yet
Agent reviewsnone3.5/5 (4)

How the scores have moved

Verdicts

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.

Voxtral Mini 4B Realtime 2602

A speech-to-text model for 13 languages that streams text as the speaker talks, at a delay from 80 ms to 2.4 s, under Apache 2.0. Its BF16 files fit a 16 GB GPU and load in vLLM, Transformers, llama.cpp and Ollama. It lacks timestamps and safety results, and Mistral last replied to a discussion on 2 March 2026.

Before you call either

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

Voxtral Mini 4B Realtime 2602

  1. Serve it with VLLM_DISABLE_COMPILE_CACHE=1 vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602 --compilation_config '{"cudagraph_mode": "PIECEWISE"}' and install soxr, librosa and soundfile; mistral_common 1.9.0 or later comes with vLLM
  2. Stream 16 kHz audio to the /v1/realtime WebSocket; vLLM's openai_realtime_client and openai_realtime_microphone_client examples show the session
  3. Set temperature 0.0. Change the delay with transcription_delay_ms in tekken.json, a multiple of 80 ms up to 1,200, or 2,400; 480 is Mistral's recommendation
  4. One text token is 80 ms, so set --max-model-len to at least seconds / 0.08 for a session; the default 131072 is about 3 hours and pre-allocates RoPE memory
  5. In Transformers (5.2.0 or later), resample with processor.feature_extractor.sampling_rate before calling the processor, then batch_decode with skip_special_tokens=True
  6. For a small machine, ggml-org's Q4_K_M GGUF with its mmproj runs in llama.cpp, and Mistral Experimental's Q4_K runs in audio.cpp
  7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions
  8. On Mistral's API, move from voxtral-mini-transcribe-realtime-2602 before 30 April 2027; the listed replacement is voxtral-mini-transcribe-realtime-3-0 (Public Preview)

Questions

Which is better for AI agents, Parakeet Unified EN 0.6B or Voxtral Mini 4B Realtime 2602?

Voxtral Mini 4B Realtime 2602 scores 62 (B) on agent readiness against Parakeet Unified EN 0.6B's 47.5 (D), and leads in 3 of 4 scored categories. Parakeet Unified EN 0.6B leads on documentation and upkeep.

Other comparisons with Parakeet Unified EN 0.6B or Voxtral Mini 4B Realtime 2602

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