GroqCloud
Low-cost batch speech-to-text that stays strong on jargon-heavy audio, but shows uneven multilingual accuracy and a strict upload cap.
Good on cost and jargon, weaker on code-switching, and one hard overlap case never got past the upload cap.
- 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.
- You need native speaker diarization.
Our take
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.
In-Depth Review
Our detailed analysis of GroqCloud — features, performance, and real-world testing.
Feature-by-Feature Breakdown
Audio Transcription▾
Feature tested: Audio Transcription
Result: Partial
Expected behavior: 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.
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): INPUT: Medical Jargon audio with verbose_json and word timestamps enabled. — medical_terms.mp3
Observed output: Output artifact (Text/code file): The raw response includes task, language, duration, transcript text, and segment objects; the benchmark detected word_timestamps=yes, confidence=yes, speaker_labels=no, and only segment-level timing rather than native speaker attribution. — raw-response.json
Input artifact: Input artifact (Audio file): INPUT: Medical Jargon audio with verbose_json and word timestamps enabled. — medical_terms.mp3
Output artifact: Output artifact (Text/code file): The raw response includes task, language, duration, transcript text, and segment objects; the benchmark detected word_timestamps=yes, confidence=yes, speaker_labels=no, and only segment-level timing rather than native speaker attribution. — raw-response.json
What changed: Audio file transformed into Text/code file
Test case: Audio file → Text/code file
Input type: Audio file
Input used: Input artifact (Audio file): INPUT: Bilingual Code-Switching audio with verbose_json and word timestamps enabled. — mix_language.mp3
Observed output: Output artifact (Text/code file): The raw response again includes transcript text and segment metadata, with word_timestamps=yes, confidence=yes, speaker_labels=no, and segment-level timing only. — raw-response-2.json
Input artifact: Input artifact (Audio file): INPUT: Bilingual Code-Switching audio with verbose_json and word timestamps enabled. — mix_language.mp3
Output artifact: Output artifact (Text/code file): The raw response again includes transcript text and segment metadata, with word_timestamps=yes, confidence=yes, speaker_labels=no, and segment-level timing only. — raw-response-2.json
What changed: Audio file transformed into Text/code file
Test case: Audio file → Image
Input type: Audio file
Input used: Input artifact (Audio file): The benchmark's scored run for the Gray's Anatomy narration, including speed and cost measurements. — medical_terms.mp3
Observed output: Output artifact (Image): The run completed in 3.82s with RTF 0.0034 and estimated cost $0.03465, making it the fastest engine on this input in the report. — 03-terminal-metrics-2.png
Input artifact: Input artifact (Audio file): The benchmark's scored run for the Gray's Anatomy narration, including speed and cost measurements. — medical_terms.mp3
Output artifact: Output artifact (Image): The run completed in 3.82s with RTF 0.0034 and estimated cost $0.03465, making it the fastest engine on this input in the report. — 03-terminal-metrics-2.png
What changed: Audio file transformed into Image
Test case: Audio file → Image
Input type: Audio file
Input used: Input artifact (Audio file): The scored benchmark run for the Spanish-English conversation, including latency, RTF, and cost. — mix_language.mp3
Observed output: Output artifact (Image): The run completed in 7.74s with RTF 0.00399 and estimated cost $0.05977 while remaining in scored status. — 03-terminal-metrics-3.png
Input artifact: Input artifact (Audio file): The scored benchmark run for the Spanish-English conversation, including latency, RTF, and cost. — mix_language.mp3
Output artifact: Output artifact (Image): The run completed in 7.74s with RTF 0.00399 and estimated cost $0.05977 while remaining in scored status. — 03-terminal-metrics-3.png
What changed: Audio file transformed into Image
Why it matters / Conclusion: 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.
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.
Audio Upload Limit EnforcementCap enforcement works clearly and quickly▾
Feature tested: Audio Upload Limit Enforcement
Result: Passed
Verdict: Cap enforcement works clearly and quickly
Expected behavior: 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.
Test case: Audio file → Image
Input type: Audio file
Input used: Input artifact (Audio file): INPUT: Overlapping speech / crosstalk audio upload (65.39 MB, above the documented 25.0 MB free-tier cap). — crosstalk.wav
Observed output: Output artifact (Image): The API rejected the oversize crosstalk upload with HTTP 413 Request Entity Too Large. No transcript was returned, the request was charged at $0.0, and the report explicitly attributes the failure to the file exceeding the 25.0 MB cap. — 05-limit-evidence-input-1.png
Input artifact: Input artifact (Audio file): INPUT: Overlapping speech / crosstalk audio upload (65.39 MB, above the documented 25.0 MB free-tier cap). — crosstalk.wav
Output artifact: Output artifact (Image): The API rejected the oversize crosstalk upload with HTTP 413 Request Entity Too Large. No transcript was returned, the request was charged at $0.0, and the report explicitly attributes the failure to the file exceeding the 25.0 MB cap. — 05-limit-evidence-input-1.png
What changed: Audio file transformed into Image
Why it matters / Conclusion: The guardrail is explicit and fast, but long overlap recordings need trimming or a different plan before they can be benchmarked.
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.
Official pricing
Published Groq rates for Whisper Large-v3 and related plans, as stated in the research report.
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.
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