---
title: "GroqCloud"
type: "AI Tool"
url: "https://aidemos.com/tools/groqcloud"
description: "Medical narration returned 3.15% WER with 100% jargon recall, but bilingual code-switching jumped to 27.94% WER and a 25 MB cap blocked upload."
category: "audio-speech"
website: "https://console.groq.com/docs/speech-to-text"
published: "2026-08-13T09:18:22.557946+00:00"
updated: "2026-09-01T02:56:36.924226+00:00"
evidenceCount: 20
verifiedCount: 18
coverage: "dense"
---

# GroqCloud

Low-cost batch speech-to-text that stays strong on jargon-heavy audio, but shows uneven multilingual accuracy and a strict upload cap.

## TL;DR Verdict

**Good on cost and jargon, weaker on code-switching, and one hard overlap case never got past the upload cap.**

**Where it wins:**
- You want low-cost batch transcription for valid audio uploads.
- You need word-level timestamps and confidence metadata in the response.
- Your audio is jargon-heavy and mostly English-dominant.

**Main limitation:** You need native speaker diarization.

**Pricing:** whisper-large-v3 (pay-as-you-go) $0.111 / audio hour · whisper-large-v3-turbo (pay-as-you-go) $0.04 / audio hour · Free plan — whisper-large-v3 $0 · Free plan — whisper-large-v3-turbo $0

`Batch STT` · `Word timestamps` · `25 MB free cap` · `3-input benchmark`

**Website:** [Visit GroqCloud](https://console.groq.com/docs/speech-to-text)

## Evidence (first-party, tested)

*20 tested cells · 18/20 artifact-verified. Scores are out of 5. Cite a cell by its Evidence ID, e.g. `ev:groqcloud-whisper-large-v3·cross·automation-level`.*

| Criterion | Scenario | Verdict | Score | Proof | Evidence ID |
| --- | --- | --- | --- | --- | --- |
| Automation level | cross-scenario | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/1809c01dc5aa4f839bb88a004d91daec.mov?v=1) | `ev:groqcloud-whisper-large-v3·cross·automation-level` |
| Automation level | Overlapping meeting speech with cross-talk | ◐ mixed | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/58a7203ea550446abd2da62f5d8e98e7.png?v=1) | `ev:groqcloud-whisper-large-v3·overlapping-meeting-speech-with-cross-talk·automation-level` |
| Automation level | Bilingual Spanish-English code-switching speech | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/01818d8b3aba4e3b9b00e99507f12354.png?v=1) | `ev:groqcloud-whisper-large-v3·bilingual-spanish-english-code-switching-speech·automation-level` |
| Automation level | Medical anatomy narration with dense jargon | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/b8a1ee4efb5349c6be01b70ace9e707e.png?v=1) | `ev:groqcloud-whisper-large-v3·medical-anatomy-narration-with-dense-jargon·automation-level` |
| Automation level | Overlapping meeting speech and cross-talk | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/1967b51db15e484bbfb7e80bbfccf169.png?v=1) | `ev:groqcloud-whisper-large-v3·overlapping-meeting-speech-and-cross-talk·automation-level` |
| Export | cross-scenario | ◐ mixed | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/81eb07019aa348848fc385ac270c930f.png?v=1) | `ev:groqcloud-whisper-large-v3·cross·export` |
| Export | Medical anatomy narration with dense jargon | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/9689062f81734b12bf63307c0cfc7209.png?v=1) | `ev:groqcloud-whisper-large-v3·medical-anatomy-narration-with-dense-jargon·export` |
| Export | Bilingual Spanish-English code-switching speech | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/6dff968358774caf83621ec29e30a609.png?v=1) | `ev:groqcloud-whisper-large-v3·bilingual-spanish-english-code-switching-speech·export` |
| Export | Overlapping meeting speech with cross-talk | ✗ failed | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/4db7654a8645413fb817f28cbc3ec8da.png?v=1) | `ev:groqcloud-whisper-large-v3·overlapping-meeting-speech-with-cross-talk·export` |
| Export | Medical anatomy dictation with dense jargon | ◐ mixed | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/dc7f973646ac4b9884ecc8cd6270bbdb.png?v=1) | `ev:groqcloud-whisper-large-v3·medical-anatomy-dictation-with-dense-jargon·export` |
| Input handling | Overlapping meeting speech with cross-talk | ✗ failed | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/13b423cdf8004c87ab43e7659a59c05b.png?v=1) | `ev:groqcloud-whisper-large-v3·overlapping-meeting-speech-with-cross-talk·input-handling` |
| Input handling | Medical anatomy narration with dense jargon | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/c6f5cef353f245619e12e8d85afd6eed.png?v=1) | `ev:groqcloud-whisper-large-v3·medical-anatomy-narration-with-dense-jargon·input-handling` |
| Input handling | Bilingual Spanish-English code-switching speech | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/739ff8a558764d538f75c00c672f92e1.png?v=1) | `ev:groqcloud-whisper-large-v3·bilingual-spanish-english-code-switching-speech·input-handling` |
| Input handling | cross-scenario | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/1f207f202d0e42b9837f6fa013add313.jpeg?v=1) | `ev:groqcloud-whisper-large-v3·cross·input-handling` |
| Input handling | Overlapping meeting speech and cross-talk | ✗ failed | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/57a6d1c78868468abdfccecb8bd430ff.png?v=1) | `ev:groqcloud-whisper-large-v3·overlapping-meeting-speech-and-cross-talk·input-handling` |
| Output quality | cross-scenario | ◐ mixed | — | 👁 observed | `ev:groqcloud-whisper-large-v3·cross·output-quality` |
| Output quality | Bilingual Spanish-English code-switching speech | ⚠ struggled | 27.9/5 | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/273c34241c26405b815f3c333770c24d.png?v=1) | `ev:groqcloud-whisper-large-v3·bilingual-spanish-english-code-switching-speech·output-quality` |
| Output quality | Medical anatomy narration with dense jargon | ✓ worked | 3.1/5 | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/0df33af2d8f7401aac4ffa6f80ccbd2a.png?v=1) | `ev:groqcloud-whisper-large-v3·medical-anatomy-narration-with-dense-jargon·output-quality` |
| Output quality | Overlapping meeting speech with cross-talk | ◐ mixed | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/4db7654a8645413fb817f28cbc3ec8da.png?v=1) | `ev:groqcloud-whisper-large-v3·overlapping-meeting-speech-with-cross-talk·output-quality` |
| Output quality | Spanish-English code-switching conversation | ⚠ struggled | 27.9/5 | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/ef957a6b1c104180b810a92d894d4c4d.png?v=1) | `ev:groqcloud-whisper-large-v3·spanish-english-code-switching-conversation·output-quality` |

> 🧾 = artifact-verified (proof captured) · 👁 = observed (noted, no artifact) · verdicts: worked / mixed / struggled / failed.

> **Good on cost and jargon, weaker on code-switching, and one hard overlap case never got past the upload cap.**
>
> GroqCloud Whisper Large-v3 is attractive on price and handled the accepted uploads quickly, with 3.15% WER and 100% jargon recall on the medical narration. But it dropped sharply on bilingual code-switching at 27.94% WER, and the overlapping-speech test was rejected before transcription because the file exceeded the documented 25 MB cap. This makes it a solid low-cost batch STT option for valid uploads, not a confirmed winner on the hardest audio types.

## Demo Recording

[Video: GroqCloud demo recording](https://cdn.futuresmart.ai/public/aidemos/77a73c41319647a48b381cc072a763bf.mov?v=1)
*Video — Screen recording of the Groq console and a terminal benchmark run showing API setup and the transcription test flow.*

## Feature-by-Feature Breakdown

### Audio Transcription

Converts uploaded spoken audio into transcript text through the transcription endpoint, including accepted multipart uploads and narration/bilingual speech inputs. The same flow also produced verbose JSON with segment timing, token arrays, confidence fields, and word-level timestamps on the exercised tests.

**Input:**

> **Audio**

**Output:** Raw API response

> **Json** — Raw API response

**Input:**

> **Audio**

**Output:** Raw API response

> **Json** — Raw API response

**Input:** Same medical-jargon run metrics

> **Audio** — Same medical-jargon run metrics

**Output:** Run metrics

> **Image** — Run metrics

**Input:** Same bilingual run metrics

> **Audio** — Same bilingual run metrics

**Output:** Run metrics

> **Image** — Run metrics

**Bottom line:** Works reliably as a batch transcription API on accepted uploads, but its quality is uneven across hard audio: excellent on the jargon-heavy narration and much weaker on bilingual speech.

### Audio Upload Limit Enforcement

**Verdict:** Cap enforcement works clearly and quickly

Rejects oversized audio uploads at the API boundary with explicit errors such as HTTP 413 or request_too_large instead of attempting transcription. The tested over-cap WAV/large-file cases failed immediately before any transcript was produced.

**Input:**

> **Audio**

**Output:**

**Bottom line:** The guardrail is explicit and fast, but long overlap recordings need trimming or a different plan before they can be benchmarked.

## Official pricing

Published Groq rates for Whisper Large-v3 and related plans, as stated in the research report.

| Plan | Price | Notes |
| --- | --- | --- |
| whisper-large-v3 (pay-as-you-go) ★ (tested) | $0.111 / audio hour | 100 MB max file (dev tier) · 189x real-time speed factor · 10.3% WER · 99+ languages |
| whisper-large-v3-turbo (pay-as-you-go) | $0.04 / audio hour | 100 MB max file (dev tier) · 216x speed factor · 12% WER · transcription only (no translation) |
| Free plan — whisper-large-v3 | $0 | 20 RPM · 2,000 requests/day · 7,200 audio-sec/hour · 28,800 audio-sec/day · 25 MB max file |
| Free plan — whisper-large-v3-turbo | $0 | Same free-tier limits as whisper-large-v3 |
| Developer plan | Pay-as-you-go, no monthly fee published | Higher rate limits; Batch and Flex processing mentioned in the docs |

*Free-tier limits include a 25 MB max file size on Whisper Large-v3. The report also notes no native speaker diarization and no separately priced add-ons for Whisper.*

## Is It Right For You?

**Use it if**
- You want low-cost batch transcription for valid audio uploads.
- You need word-level timestamps and confidence metadata in the response.
- Your audio is jargon-heavy and mostly English-dominant.

**Skip it if**
- You need native speaker diarization.
- You need proven robustness on overlapping speech under a valid upload.
- You need strong code-switching accuracy on mixed-language speech.
- Your files may exceed the documented 25 MB free-tier cap.

## Classification

- **Category:** audio-speech
- **Subcategory:** audio-to-text
- **Type:** text
- **Built for:** Other

## Frequently Asked Questions

**Q: What happened to the overlapping-speech test?**

It never reached transcription. The 65.39 MB crosstalk file exceeded the documented 25.0 MB free-tier cap, so the API returned HTTP 413 Request Entity Too Large and no transcript was produced.

**Q: How accurate was GroqCloud Whisper Large-v3 on jargon-heavy audio?**

On the medical-jargon narration, it scored 3.15% WER, returned 2730 words against 2728 reference words, and reached 100.0% recall on the scored jargon terms.

**Q: How did it perform on bilingual code-switching?**

It was much weaker there: 27.94% WER, 5598 returned words versus 6517 reference words, and 53.8% Spanish token recall.

**Q: Does it return word-level timestamps or speaker labels?**

The response metadata shows word timestamps and confidence fields are present, but speaker labels are not. The report also notes no native speaker diarization.

**Q: What does it cost?**

The report lists Whisper Large-v3 at $0.111 per audio hour, or $0.00185 per minute. The free plan is $0, with the documented 25 MB max file size on Whisper Large-v3.

**Q: Was streaming latency measured?**

No. This benchmark was batch-only, so streaming latency was not measured in the report.

## Similar Tools

AI tools similar to GroqCloud:

- [AssemblyAI](https://aidemos.com/tools/assemblyai-speech-to-text) — Fast batch STT with strong metadata and mixed-language performance, but overlap-heavy meetings can drop too many words.
- [Speechmatics](https://aidemos.com/tools/speechmatics) — Strong batch STT for hard English audio, but weak on code-switching as configured.
- [OpenAI](https://aidemos.com/tools/openai) — Batch speech-to-text with word timestamps, but a strict upload cap and weak multilingual performance make it a mixed fit for hard audio.
- [AWS Transcribe](https://aidemos.com/tools/aws-transcribe) — Batch speech-to-text with timestamps and speaker labels, but weak on multilingual audio.
- [ElevenLabs Scribe](https://aidemos.com/tools/elevenlabs-scribe) — Fast batch speech-to-text with word-level metadata, strongest on jargon and weaker on overlap/code-switching.
- [Rev AI](https://aidemos.com/tools/rev-ai) — Low-cost batch speech-to-text with structured JSON, word timestamps, and speaker labels, but mixed accuracy on hard audio and weak multilingual recall.
- [Gladia](https://aidemos.com/tools/gladia) — Batch STT with rich JSON metadata and strong jargon recall, but weak overlap handling and a channel-duplication caveat on bilingual audio.
- [Deepgram](https://aidemos.com/tools/deepgram) — Batch speech-to-text with rich metadata, but weak on crosstalk and code-switching.
- [ElevenLabs](https://aidemos.com/tools/elevenlabs) — Natural-sounding voice cloning and narration, but with only approximate voice identity.

## Need a custom AI solution for this use case?

If you are looking to build a custom speech-to-text, audio transcription, or transcription workflow for your business or internal workflow, email us at [contact@futuresmart.ai](mailto:contact@futuresmart.ai).

### Found something inaccurate or missing?

We try to keep our AI research accurate and useful. If you found outdated information, an issue, or have a suggestion, email us at [collaborate@aidemos.com](mailto:collaborate@aidemos.com).