{ "data": { "a": { "slug": "giggy-stt", "name": "Giggy Speech to Text", "vendor": "Giggy", "vendorUrl": "https://giggy.ai", "kind": "model", "category": "speech-to-text", "summary": "Giggy's hosted speech-to-text API transcribes 16 kHz mono WAV recordings in free Batch, prepaid Fast and Streaming modes, and live audio over a WebSocket, with Python and Node SDKs and a remote MCP server.", "url": "https://www.anchorterminal.com/tools/giggy-stt", "markdownUrl": "https://www.anchorterminal.com/tools/giggy-stt.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/giggy-stt.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/giggy-stt.json", "dns": "giggy-stt.grade.anchorterminal.com", "repo": "https://github.com/giggy-ai/giggy-js", "license": "Proprietary hosted service. The terms page draws its text by script and was not read. The Python and Node SDKs, the Pipecat adapter and the MCP repository are ISC (Copyright 2026 Giggy), the n8n node MIT", "transports": [ "http", "sse", "websocket", "streamable-http" ], "remoteUrl": "https://api.giggy.ai/v1", "packages": [ { "registry": "pypi", "name": "giggy" }, { "registry": "npm", "name": "@giggy-ai/sdk" }, { "registry": "pypi", "name": "pipecat-giggy" } ], "auth": "api-key", "authNotes": "Self-serve API key prefixed `giggy_sk_`, shown once when created or rotated, rejected immediately after revocation, up to ten active per Personal space or Workspace and tied for good to the space that created it. REST sends it in `xi-api-key`; the WebSocket upgrade and the MCP server take `Authorization: Bearer`. No key scopes or expiry were found, and no query-string option is documented (https://giggy.ai/docs/api-reference).", "pricing": "freemium", "pricingNotes": "Batch transcription is free, queued, for recordings of 0.5 seconds to 25 minutes. Fast costs 4,000 credits ($0.04) and Streaming 8,000 credits ($0.08) per input audio hour on Pro, Studio or Scale credits, rounded up to one credit per request. Credits come in one-time packs from $3 and expire a year after purchase. Personal accounts get free credits topped up to 10,000 a month. Whether a card is needed was not stated on any page read (https://giggy.ai/pricing).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on the pricing page, the speech API docs, the API reference, llms.txt or llms-full.txt read on 2026-11-04. Credits are bought in prepaid packs; the payment method is not named on any page read.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-04" }, "docsUrl": "https://giggy.ai/docs/speech-api", "llmsTxt": "https://giggy.ai/llms.txt", "openapi": "https://api.giggy.ai/v1/openapi.json", "registryName": "io.github.GRQDigitalCapital/giggy-mcp", "capabilities": [ "speech.stt", "speech.batch", "speech.streaming", "speech.languages" ], "tags": [ "official", "hosted", "model", "closed-source", "streaming", "batch", "async-jobs", "free-tier", "python", "typescript", "openapi", "llms-txt", "mcp", "websocket", "new" ], "lastRelease": "2026-11-01", "graded": true, "anchor": { "graded": true, "score": 54.5, "grade": "C", "agentReady": false, "rank": 805, "ranked": true, "rankOf": 1349, "categoryRank": 15, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 68, "maintenance": 69, "payments": 30, "reliability": 50, "schema": 68, "security": 57, "transparency": 30 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "Fast transcription costs $0.04 an input audio hour and Batch is free up to 25 minutes, with published rate limits, Retry-After and a rule to check a generation before resubmitting. Input must be 16 kHz mono WAV, there are no timestamps or speaker labels, the status page covers text-to-speech only, and no company is named on pages we could read.", "bestFor": "Agents and voice pipelines that need plain transcripts of recorded or live speech at a few cents an hour, or free for clips under 25 minutes, and can convert audio to 16 kHz mono WAV first.", "strengths": [ "Fast is $0.04 and Streaming $0.08 per input audio hour on Pro credits, and Batch is free for recordings of 0.5 seconds to 25 minutes", "Per-credential rate limits are published with numbers, 429 carries `Retry-After`, and `RateLimit-*` headers report what is left", "The docs say to read a generation by UUID after a timeout or 504 instead of uploading again, and the SDKs never retry paid requests", "llms.txt, llms-full.txt and SDK READMEs state what the API does not do, including no diarisation, timestamps or OpenAI-compatible STT", "Official Python and Node SDKs at 0.2.1, a Pipecat adapter with live STT and a remote MCP server, all released within the last four weeks" ], "weaknesses": [ "Recorded input must be PCM16 mono 16 kHz WAV, so MP3, M4A, stereo or other sample rates need converting before upload", "No word timestamps, speaker labels, confidence scores or redaction, per the speech API docs", "The status page has one component, Text to speech, and no incident history, so speech-to-text availability isn't shown", "REST transcription has no idempotency key, and `client_request_id` is documented as not making a retried upload safe", "The terms, privacy policy, support and contact pages draw by script, and no company, address or security contact was found on any page read" ], "agentNotes": [ "Pass `mode=batch` explicitly for free transcription. Raw REST and MCP default to paid Fast when `mode` is omitted.", "Convert audio to PCM16 mono 16 kHz WAV first and send it as the raw body with `Content-Type: audio/wav`.", "Keep the `generation_uuid` from the 202 and poll `GET /v1/generations/{uuid}`. Never re-upload after a timeout or 504 before reading that generation.", "Fetch the transcript from the signed `generation.result.url` after completion, without the API key. It can disappear after retention cleanup.", "On the live WebSocket, replace earlier text with each cumulative `transcription` event and publish final text only after `input_complete`." ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.5 } ], "editorialScores": { "ergonomics": 68, "maintenance": 69, "payments": 30, "reliability": 50, "schema": 68, "security": 57, "transparency": 23 }, "provenanceScore": 36, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "python -m pip install giggy==0.2.1 # or: npm install @giggy-ai/sdk", "http": "curl -X POST \"https://api.giggy.ai/v1/audio/transcriptions?mode=batch\u0026language=en\" \\\n -H \"xi-api-key: $GIGGY_API_KEY\" -H \"Content-Type: audio/wav\" \\\n --data-binary @recording.wav", "config": { "mcpServers": { "giggy-speech": { "headers": { "Authorization": "Bearer ${GIGGY_API_KEY}" }, "type": "http", "url": "https://giggy.ai/mcp" } } } }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/giggy-stt" }, "area": "voice", "unitPrices": [ { "item": "Fast, Pro, Studio or Scale credits", "unit": "audio-minute", "usd": 0.000667, "note": "published as $0.04 per input audio hour, rounded up to one credit per request" }, { "item": "Streaming, Pro, Studio or Scale credits", "unit": "audio-minute", "usd": 0.001333, "note": "published as $0.08 per input audio hour" }, { "item": "Fast, Starter pack", "unit": "audio-minute", "usd": 0.000765, "note": "$3 for 261,300 credits, $0.0459 an hour" }, { "item": "Streaming, Starter pack", "unit": "audio-minute", "usd": 0.00153, "note": "$0.0918 an hour" }, { "item": "Batch", "unit": "audio-minute", "usd": 0, "note": "free, queued, 0.5 seconds to 25 minutes a recording" } ], "provenance": { "legalEntity": "", "domain": "giggy.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://giggy.ai/terms", "privacy": "https://giggy.ai/privacy", "statusPage": "https://giggy.ai/status", "changelog": "", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "unchecked: the legal entity. /terms, /privacy, /acceptable-use, /contact, /docs/support and /docs/privacy-rights returned only their titles and metadata, because their text is drawn by script, and /terms.md answered a client error. The about, pricing, grants and docs pages that did render name only Giggy, with 'Made in California' on the about page. The repositories' licences say 'Copyright 2026 Giggy'.", "The status page tracks one component, Text to speech, with 14-day availability and completion figures refreshed every 30 minutes and no incident history. It has no speech-to-text component.", "No service changelog was found. The SDK and Pipecat repositories carry CHANGELOG.md files, dated in the Python SDK and the Pipecat adapter.", "https://giggy.ai/.well-known/security.txt answered a client error. None of the six public repositories has a SECURITY.md.", "robots.txt on giggy.ai allows / and disallows /api/, /ops/, /app/ and /blog/*.md for `*` and for the named AI agents, with no Content-Signal line. api.giggy.ai's robots.txt could not be read (the fetch needed an approval that wasn't given), so nothing on that host was fetched, the OpenAPI file included.", "unchecked: RDAP for giggy.ai (refused for want of an approval on 4 November 2026)." ], "score": 36 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/giggy-stt.json", "live": { "slug": "giggy-stt", "probe": { "target": "https://api.giggy.ai/v1", "method": "get", "lastAt": "2026-11-04T20:46:47.290932988Z", "lastOk": true, "lastStatus": 404, "lastMs": 524, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 286, "p95ms24h": 654, "samples24h": 55, "samples30d": 55, "days": [ { "date": "2026-11-04", "probes": 55, "ok": 55 } ] }, "vendorStatus": { "page": "https://giggy.ai/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-04T15:31:56.257084594Z" }, "versions": [ { "registry": "github", "name": "giggy-ai/giggy-js", "version": "v0.1.0", "released": "2026-10-07", "seenAt": "2026-11-04T20:18:19.850001725Z" }, { "registry": "mcp-registry", "name": "io.github.GRQDigitalCapital/giggy-mcp", "version": "1.0.2", "seenAt": "2026-11-04T17:11:44.552009456Z" }, { "registry": "npm", "name": "@giggy-ai/sdk", "version": "0.2.1", "seenAt": "2026-11-04T20:18:17.973963585Z" }, { "registry": "pypi", "name": "giggy", "version": "0.2.1", "released": "2026-11-01", "seenAt": "2026-11-04T20:18:17.733675794Z" }, { "registry": "pypi", "name": "pipecat-giggy", "version": "0.3.3", "released": "2026-11-01", "seenAt": "2026-11-04T20:18:18.946085778Z" } ], "githubStars": 0, "npmWeekly": 209, "pypiWeekly": 32, "securityTxt": { "url": "https://giggy.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-04T15:54:29.107165985Z" }, "updatedAt": "2026-11-04T20:46:47.290932988Z" } }, "answer": "Groq Speech-to-Text scores 70.1 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 5 of 7 scored categories. Giggy Speech to Text leads on schema \u0026 documentation and maintenance \u0026 community.", "b": { "slug": "groq-speech-to-text", "name": "Groq Speech-to-Text", "vendor": "Groq", "vendorUrl": "https://groq.com", "kind": "model", "category": "speech-to-text", "summary": "Groq's hosted speech-to-text API. It runs OpenAI's Whisper Large v3 and Whisper Large v3 Turbo on OpenAI-compatible transcription and translation endpoints, for uploaded files or audio URLs, with a half-price batch mode.", "url": "https://www.anchorterminal.com/tools/groq-speech-to-text", "markdownUrl": "https://www.anchorterminal.com/tools/groq-speech-to-text.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/groq-speech-to-text.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/groq-speech-to-text.json", "dns": "groq-speech-to-text.grade.anchorterminal.com", "repo": "https://github.com/groq/groq-python", "license": "Proprietary hosted service under the Groq Services Agreement. The SDKs are Apache-2.0 and the Whisper weights are published by OpenAI on Hugging Face", "transports": [ "http" ], "remoteUrl": "https://api.groq.com/openai/v1", "packages": [ { "registry": "pypi", "name": "groq" }, { "registry": "npm", "name": "groq-sdk" } ], "auth": "api-key", "authNotes": "Self-serve bearer key from the GroqCloud console, created inside a project. Projects carry their own rate limits per model, usage data and request logs (https://console.groq.com/docs/projects).", "pricing": "freemium", "pricingNotes": "$0.04 per audio hour for Whisper Large v3 Turbo and $0.111 for Whisper Large v3, with a 10-second minimum a request and 50% off through the Batch API (https://console.groq.com/docs/models). The free plan needs no card, so an agent's owner can start without a contract. The Developer plan is postpaid by card, US bank account or SEPA debit.", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the speech-to-text guide, the API reference or the billing pages (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 621, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://console.groq.com/docs/speech-to-text", "llmsTxt": "https://console.groq.com/llms.txt", "capabilities": [ "speech.stt", "speech.batch", "speech.languages" ], "tags": [ "official", "hosted", "model", "fast", "free-tier", "no-card", "llms-txt", "python", "typescript", "batch", "openai-compatible" ], "lastRelease": "2026-08-26", "graded": true, "anchor": { "graded": true, "score": 70.1, "grade": "BB", "agentReady": true, "rank": 192, "ranked": true, "rankOf": 1349, "categoryRank": 6, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 48, "payments": 40, "reliability": 90, "schema": 58, "security": 79, "transparency": 85 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": 0, "verdict": "Whisper Large v3 Turbo costs $0.04 an audio hour and Whisper Large v3 $0.111, with a no-card free plan and zero data retention as a self-serve setting. There is no streaming endpoint, no diarisation and no subtitle output, and uploads stop at 25 MB on the free plan and 100 MB on the Developer plan.", "bestFor": "Suited to cheap, fast transcription of recorded files in many languages, and to agents that already hold a Groq key or use an OpenAI-compatible client.", "strengths": [ "Published prices of $0.04 an audio hour for Whisper Large v3 Turbo and $0.111 for Whisper Large v3, with 50% off through the Batch API", "Free plan with no card at 20 requests a minute, 2,000 a day and 7,200 audio seconds an hour on both models", "Inputs and outputs are not retained by default, and zero data retention is a console setting that covers both audio endpoints", "The status page lists each Whisper model as its own component, both at 100% uptime for August to November 2026", "OpenAI-compatible request shape, with `model` and a `file` or `url` as the only required fields" ], "weaknesses": [ "No streaming or realtime endpoint and no diarisation in the reviewed documentation", "Uploads are capped at 25 MB on the free plan and 100 MB on the Developer plan, so long recordings need client-side chunking", "`srt` and `vtt` response formats are not supported, and Whisper Large v3 Turbo cannot translate", "No OpenAPI document was found, and the docs changelog's newest entry is dated 18 April", "The 99.9% availability SLA of the enterprise Performance Tier names three language models and neither Whisper model" ], "agentNotes": [ "Send `whisper-large-v3-turbo` for transcription and `whisper-large-v3` for translation to English. The translations endpoint does not accept Turbo.", "Pass `url` instead of `file` for audio over 25 MB, and split anything over the plan's size limit into overlapping chunks before sending.", "Set `response_format` to `verbose_json` before asking for `timestamp_granularities[]`. Word timestamps add latency, segment timestamps do not.", "Every request is billed as at least 10 seconds of audio, so join very short clips where the task allows.", "Read `retry-after` on a 429 and back off. Audio limits count seconds an hour and a day as well as requests." ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 70.1 } ], "editorialScores": { "ergonomics": 75, "maintenance": 48, "payments": 40, "reliability": 90, "schema": 58, "security": 79, "transparency": 72 }, "provenanceScore": 98, "checked": { "categories": { "ergonomics": "2026-11-01", "maintenance": "2026-11-01", "payments": "2026-11-01", "reliability": "2026-11-01", "schema": "2026-11-01", "security": "2026-11-01", "transparency": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install groq # or: npm install --save groq-sdk", "http": "curl https://api.groq.com/openai/v1/audio/transcriptions \\\n -H \"Authorization: Bearer $GROQ_API_KEY\" \\\n -H \"Content-Type: multipart/form-data\" \\\n -F file=\"@./sample_audio.m4a\" \\\n -F model=\"whisper-large-v3\"" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/groq-speech-to-text" }, "sameCompany": [ "groq" ], "area": "voice", "unitPrices": [ { "item": "Whisper Large v3 Turbo ($0.04 an audio hour)", "unit": "audio-minute", "usd": 0.000667 }, { "item": "Whisper Large v3 ($0.111 an audio hour)", "unit": "audio-minute", "usd": 0.00185 } ], "provenance": { "legalEntity": "Groq LLC", "domain": "groq.com", "domainRegistered": "2007-07-22", "domainNote": "The registration date is per the 26 September check of the GroqCloud listing and was not re-read on 8 October. groq.com was registered before Groq existed. Customers in the EEA and Switzerland contract with Groq UK Limited.", "endpointOnVendorDomain": true, "terms": "https://console.groq.com/docs/legal/services-agreement", "privacy": "https://groq.com/privacy-policy", "statusPage": "https://groqstatus.com", "changelog": "https://console.groq.com/docs/changelog", "securityTxt": "valid", "checked": "2026-10-08", "score": 98 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/groq-speech-to-text.json", "live": { "slug": "groq-speech-to-text", "probe": { "target": "https://api.groq.com/openai/v1", "method": "get", "lastAt": "2026-11-04T20:46:49.179800834Z", "lastOk": true, "lastStatus": 404, "lastMs": 210, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 198, "p95ms24h": 282, "samples24h": 246, "samples30d": 6644, "days": [ { "date": "2026-10-09", "probes": 169, "ok": 169 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 248, "ok": 248 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 214, "ok": 214 } ] }, "vendorStatus": { "page": "https://groqstatus.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-04T20:39:16.228161919Z" }, "versions": [ { "registry": "github", "name": "groq/groq-python", "version": "v1.7.0", "released": "2026-08-26", "seenAt": "2026-11-04T20:21:13.398295719Z" }, { "registry": "npm", "name": "groq-sdk", "version": "1.6.0", "seenAt": "2026-11-04T20:21:12.583765381Z" }, { "registry": "pypi", "name": "groq", "version": "1.7.0", "released": "2026-08-26", "seenAt": "2026-11-04T20:21:12.391433212Z" } ], "githubStars": 621, "npmWeekly": 1241757, "pypiWeekly": 3870662, "securityTxt": { "url": "https://groq.com/.well-known/security.txt", "state": "valid", "checkedAt": "2026-11-04T15:53:37.784000591Z" }, "llmsTxt": { "url": "https://console.groq.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-04T15:23:08.496102306Z" }, "domain": { "domain": "groq.com", "registered": "2007-07-22", "source": "https://rdap.verisign.com/com/v1/domain/groq.com", "checkedAt": "2026-11-01T14:20:57.072575938Z" }, "pages": [ { "url": "https://console.groq.com/docs/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-11-04T08:17:11.70319473Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "ac2cb86ef05a" }, { "url": "https://groq.com/privacy-policy", "kind": "privacy", "status": 304, "checkedAt": "2026-11-04T08:24:43.432823695Z", "changedAt": "2026-10-09T18:39:59.465247347Z", "fingerprint": "1ae299b9f26b" }, { "url": "https://console.groq.com/docs/legal/services-agreement", "kind": "terms", "status": 200, "checkedAt": "2026-11-04T08:17:16.16929587Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "a0b93cf713d4" } ], "github": { "repo": "groq/groq-python", "checkedAt": "2026-11-02T08:53:10.526782877Z", "stars": 622, "archived": false, "issuesEnabled": false, "pushedAt": "2026-09-04T20:45:45Z", "openIssues": 0, "openPRs": 1, "issuesOpened90d": 0, "issuesClosed90d": 0, "prsMerged90d": 2, "ci": { "branch": "main", "runs": 41, "success": 31, "failure": 2, "other": 8, "latest": "skipped", "latestAt": "2026-09-04T20:45:23Z" } }, "updatedAt": "2026-11-04T20:46:49.179800834Z" } }, "edges": [ { "by": 55, "category": "transparency", "leads": "groq-speech-to-text", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed hosted service. The terms exist at /terms but draw by script and could not be read, so their clarity is unknown; the SDK, Pipecat and MCP repositories are ISC and the n8n node MIT (5 of 30). What could be read agrees with itself, no training on user data, an optional 12-hour retention flag, a seven-day window for Batch audio, and the EEA compute flag. The privacy policy, any DPA and a retention period for transcripts were not found or not readable (12 of 30). No deprecation policy or dated notices, only a statement that the legacy origin remains supported (3 of 20). No subprocessors or data locations are named; the EEA-adequate compute option implies processing outside the EEA by default without saying where (3 of 20). 5+12+3+3 = 23.", "b": "The hosted service is closed under the Groq Services Agreement. Both models are OpenAI's published Whisper weights, linked from each model card, served with Groq's own quantisation, and the SDKs are Apache-2.0 (20 of 30). The data page, the services agreement and the DPA agree on retention and on the training ban. The agreement limits zero retention to 'Eligible Customers' while the data page says all customers may enable it, and the privacy policy effective 12 November 2025 says it does not cover customer data sent to GroqCloud (26 of 30). A deprecations page with dated history and replacements, with no minimum notice period (14 of 20). Customer data is kept in Google Cloud storage in the United States, and the DPA gives 15 days' notice of sub-processor changes to subscribers. The sub-processor list is in the trust centre we could not read (12 of 20)." }, "scores": { "a": 30, "b": 85 } }, { "by": 40, "category": "reliability", "leads": "groq-speech-to-text", "name": "Reliability", "reasons": { "a": "Hosted reading, as for deepgram-stt and cartesia-ink-stt. Giggy's own status page at https://giggy.ai/status lists one component, Text to speech, operational on 4 November 2026 with 98.61 per cent observed availability and 99.85 per cent completion over 14 days, refreshed every 30 minutes. It has no speech-to-text component and no incident log, so it shows nothing about this surface (5 of 20). No readable incident history, so the floor (5). Rate limits are published per credential, 60 Batch generation POSTs, 120 event-stream GETs, 600 reads and 120 MCP requests a minute, with eight live contexts per controller; Fast and Streaming POSTs have no per-credential limit and Batch queue capacity isn't given (15). 429 carries `Retry-After` with `RateLimit-*` headers, and the docs give a safe-retry rule for uploads, read the generation by UUID after a timeout or 504 and never upload the same recording again. REST transcription has no idempotency key, but the MCP `transcribe_audio` tool takes one (15). No SLA on the pricing page or in the docs; the terms could not be read (0). No beta or preview label on the API, the SDKs or the MCP server (10). 5+5+15+15+0+10 = 50.", "b": "Hosted reading. The status page at groqstatus.com (incident.io) lists `whisper-large-v3` and `whisper-large-v3-turbo` as separate production components (20). On 1 November 2026 it showed both at 100% uptime from 3 August to 1 November with \"No incidents\", and neither its RSS feed nor the incident pages it links names either model. The window's three incidents were on other components (an enterprise preview model on 10 and 12 October, and console errors on 12 October that Groq said did not affect inference), plus maintenance on 9 and 18 October (30). The rate limits page gives numbers for both models, 20 requests a minute, 2,000 a day, 7,200 audio seconds an hour and 28,800 a day on the free plan, re-read on 1 November, and the models page gives 300 requests a minute and 200,000 audio seconds an hour (v3) and 400 and 400,000 (Turbo) on the Developer plan (15). A 429 carries `retry-after`, the security onboarding page says to back off exponentially on 429 and 5xx, and transcription is a stateless call (15). The enterprise Performance Tier's 99.9% availability SLA names three language models and neither Whisper model, per the 8 October check, so no SLA was found for this product (0). Both models are listed as production models (10). 20+30+15+15+0+10 = 90." }, "scores": { "a": 50, "b": 90 } }, { "by": 22, "category": "security", "leads": "groq-speech-to-text", "name": "Security \u0026 auth", "reasons": { "a": "Model reading of the checklist, with training and retention in place of the least-privilege and injection lines, as for deepgram-stt and cartesia-ink-stt. Keys prefixed `giggy_sk_` are shown once on creation or rotation, rejected immediately after revocation, limited to ten per space and tied to the space that made them, with REST budgets that Workspace members can set. No scopes or expiry were found. The key travels in a header only, with no query-string option documented (22 of 30). The workspace privacy guide states 'Giggy does not use user data to train models'. The privacy policy and terms draw by script and could not be read to confirm it (18 of 20). Requests can opt into 12-hour content retention and EEA-adequate compute, and a guide gives Batch audio a seven-day window, but no retention period for transcripts was found and results can vanish after an unstated 'retention cleanup' (9 of 15). Generation history per space and a request ID on errors; no audit log of key or admin actions was found (8 of 15). /.well-known/security.txt answered a client error, none of the six repositories has a SECURITY.md, and no disclosure policy, bug bounty or certification was found on the pages that rendered; the support page that may name a reporting route drew by script (0 of 20). 22+18+9+8+0 = 57.", "b": "Model reading, scored like the GroqCloud listing. Bearer keys belong to one project, each project has its own rate limits per model, and the security onboarding page covers rotation and immediate revocation. No way to limit a key to the audio endpoints was found (26 of 30). The services agreement, last modified 22 June 2026, says Groq may not use inputs or outputs for training or fine-tuning unless the customer grants permission (20). Inputs and outputs are not retained by default, logs kept for reliability or abuse last up to 30 days, and the data page lists both audio endpoints as eligible for zero data retention, a setting in Data Controls (15). Usage data and request logs per project and a read-only Reader role. No audit log of administrative actions was found (10 of 15). security.txt on groq.com holds a Contact line only, with no expiry date. The DPA commits to SOC 2 Type II audits, and the trust centre at trust.groq.com is drawn by script and was not read (8 of 20)." }, "scores": { "a": 57, "b": 79 } }, { "by": 21, "category": "maintenance", "leads": "giggy-stt", "name": "Maintenance \u0026 community", "reasons": { "a": "Read as a model, on the lines deepgram-stt and cartesia-ink-stt use (recency 30, dated releases 10, deprecation notice 10, support 15, issues 10, SDKs 15, health 10). The Python and Node SDKs released 0.2.1 and the Pipecat adapter 0.3.3 on 1 November 2026, and the examples and MCP repositories changed on 3 November (30). Since 7 October the Node SDK has four tagged releases, the Python SDK six and the Pipecat adapter six, the STT ones from 12 October (10). No deprecation policy or dated notice. The legacy `https://giggy.ai/v1` origin and older SDK transports are kept 'for existing integrations' with no end date (2 of 10). No service changelog; the support and contact pages drew by script, and GitHub issues is the support URL in the plugin manifest (5 of 15). GitHub issues and reply times could not be read, since the GitHub API was not available to this session (0 of 10). Official SDKs current, `giggy` and `@giggy-ai/sdk` at 0.2.1 on PyPI and npm (15). CI workflows in five repositories and publish workflows, the n8n node through npm OIDC with provenance, and two runtime dependencies in each SDK; run results not seen (7 of 10). 30+10+2+5+0+15+7 = 69.", "b": "Model reading, as for the GroqCloud listing, counting changes to this listing's own models and endpoints, as the OpenAI listings read their own models and the AWS listings their own service models. No dated change to speech-to-text was found in 2026. The Python SDK 1.7.0 (tag 25 August, uploaded to PyPI on 26 August 2026 per the 8 October check) and TypeScript SDK 1.6.0 (25 August) add chat reasoning guidance, CI workflows and lock-file updates and leave the audio code untouched. The last SDK change to the transcription code was Python 1.2.0 on 18 April 2026. A release that doesn't change this service can't set the recency line in this run, as for the Azure Translator listing, and Muse Spark Open's 15 October preview is a chat model, counted on the GroqCloud listing. So no change within 180 days was found (0, was 20). The deprecations page promises an email and a named replacement and states no minimum notice. The one speech-to-text retirement on it is `distil-whisper-large-v3-en` on 23 August 2025, and the newest announcement is 24 August 2026, for Compound (4 of 12). Neither Whisper model was retired or renamed in the last 90 days (8 of 8). A dated docs changelog with a feed exists, its newest entry is 18 April and none of its entries concern speech-to-text (8 of 15). SDK issue replies not sampled (5 of 10). Official `groq` 1.7.0 on PyPI and `groq-sdk` 1.6.0 on npm, per the tags (15). CI, release automation and lock-file vulnerability fixes in 1.7.0 (8 of 10). 0+4+8+8+5+15+8 = 48." }, "scores": { "a": 69, "b": 48 } }, { "by": 10, "category": "schema", "leads": "giggy-stt", "name": "Schema \u0026 documentation", "reasons": { "a": "Model reading, with the API reference. The docs and llms.txt link an OpenAPI 3.1 file at https://api.giggy.ai/v1/openapi.json, but api.giggy.ai's robots.txt could not be read, so the file wasn't fetched. We credit only what the reference pages show of it, typed `mode` values, the privacy booleans and a typed error body, and there is no AsyncAPI file for the live socket (12 of 25). llms.txt and llms-full.txt (10). The docs say when to use each mode and transport, that MCP is not a live transport, that an OpenAI base-URL swap does not work for STT, and which options have no equivalent, more than most listings say about when not to use something (17 of 20). `mode` is an enum of three values and the input format is fixed, but `language` is a free code or name with no published list, and the MCP input schemas were read only from the repository's docs, not from the server (11 of 15). Examples in the SDK READMEs, the examples repository and a Postman collection, and an error table of ten statuses with `type`, `code`, `message`, `status`, `request_id` and `param` fields, though only one code value is named (11 of 15). Paths are under `/v1` and the SDK and Pipecat repositories keep CHANGELOG.md files, dated in two of them, but there is no service changelog (7 of 15). 12+10+17+11+11+7 = 68.", "b": "Model reading. No OpenAPI document was found in the docs, and the Python SDK's `.stats.yml` carries an endpoint count with no specification URL (0). llms.txt at console.groq.com with Markdown twins of the guide, model cards and reference (10). The guide says which model to pick for accuracy or price, that translation is English only and that the `prompt` steers style within 224 tokens, and it explains the `verbose_json` quality fields (15 of 20). The reference lists allowed values for `response_format` and `timestamp_granularities[]` and marks `model` as required, while `language` and `url` are plain strings (11 of 15). curl, Python and JavaScript examples for both endpoints and a shared errors page with a typed error object. The Batch page's sample still names `distil-whisper-large-v3-en`, shut down on 23 August 2025, on 1 November (12 of 15). The docs changelog is dated, its newest entry is still 18 April on 1 November and no entry in it concerns speech-to-text (10 of 15)." }, "scores": { "a": 68, "b": 58 } }, { "by": 10, "category": "payments", "leads": "groq-speech-to-text", "name": "Payments \u0026 pricing", "reasons": { "a": "No x402, MPP or L402 (0). Per-hour prices for Batch, Fast and Streaming and every credit pack published without a login (20). Free Batch transcription and Personal free credits topped up to 10,000 a month, but no page read says whether a card is needed, so half, as other listings read an unstated card requirement (10 of 20). A person signs up in a browser and creates the key there; no programmatic key route was found (0). 0+20+10+0 = 30.", "b": "No x402, MPP or L402 (0). $0.04 an audio hour for Whisper Large v3 Turbo and $0.111 for Whisper Large v3 on the models page and model cards, read without a login (20). The free plan needs no card and includes both Whisper models at its own rate limits (20). A person signs up in the console in a browser (0)." }, "scores": { "a": 30, "b": 40 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Giggy", "b": "Groq", "name": "Vendor" }, { "a": "https://api.giggy.ai/v1", "b": "https://api.groq.com/openai/v1", "name": "Hosted endpoint" }, { "a": "HTTP, SSE (legacy), websocket, Streamable HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Freemium", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary hosted service. The terms page draws its text by script and was not read. The Python and Node SDKs, the Pipecat adapter and the MCP repository are ISC (Copyright 2026 Giggy), the n8n node MIT", "b": "Proprietary hosted service under the Groq Services Agreement. The SDKs are Apache-2.0 and the Whisper weights are published by OpenAI on Hugging Face", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "io.github.GRQDigitalCapital/giggy-mcp", "b": "not listed", "name": "MCP registry" }, { "a": "2026-11-01", "b": "2026-08-26", "name": "Last release" }, { "a": "not read yet", "b": "2026-06-22", "name": "Terms last updated" }, { "a": "not read yet", "b": "2025-11-12", "name": "Privacy policy last updated" }, { "a": "not read yet", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "not read yet", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not read yet", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "621 stars", "name": "Popularity" }, { "a": "none", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Groq Speech-to-Text scores 70.1 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 5 of 7 scored categories. Giggy Speech to Text leads on schema \u0026 documentation and maintenance \u0026 community.", "question": "Which is better for AI agents, Giggy Speech to Text or Groq Speech-to-Text?" }, { "answer": "Both need an API key.", "question": "Do Giggy Speech to Text and Groq Speech-to-Text need an API key?" }, { "answer": "Yes. Giggy Speech to Text has a hosted endpoint at https://api.giggy.ai/v1 and Groq Speech-to-Text at https://api.groq.com/openai/v1.", "question": "Can an agent call Giggy Speech to Text and Groq Speech-to-Text without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 68 against 58", "Maintenance \u0026 community, 69 against 48" ], "also": null, "goodFor": "Agents and voice pipelines that need plain transcripts of recorded or live speech at a few cents an hour, or free for clips under 25 minutes, and can convert audio to 16 kHz mono WAV first.", "slug": "giggy-stt", "watchFor": "Recorded input must be PCM16 mono 16 kHz WAV, so MP3, M4A, stereo or other sample rates need converting before upload" }, { "aheadOn": [ "Reliability, 90 against 50", "Agent ergonomics, 75 against 68", "Security \u0026 auth, 79 against 57", "Payments \u0026 pricing, 40 against 30", "Transparency \u0026 trust, 85 against 30" ], "also": [ "Agent-ready, a grade of BB or better", "Free to start without a card" ], "goodFor": "Suited to cheap, fast transcription of recorded files in many languages, and to agents that already hold a Groq key or use an OpenAI-compatible client.", "slug": "groq-speech-to-text", "watchFor": "No streaming or realtime endpoint and no diarisation in the reviewed documentation" } ], "history": { "giggy-stt": null, "groq-speech-to-text": [ { "causes": [ "correction" ], "date": "2026-11-01", "delta": -1.7, "from": 71.8, "fromGrade": "BB", "to": 70.1, "toGrade": "BB" } ] }, "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-transcribe-vs-giggy-stt.json", "title": "Amazon Transcribe vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/amazon-transcribe-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/amazon-transcribe-vs-groq-speech-to-text.json", "title": "Amazon Transcribe vs Groq Speech-to-Text", "url": "https://www.anchorterminal.com/compare/amazon-transcribe-vs-groq-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/assemblyai-stt-vs-giggy-stt.json", "title": "AssemblyAI Speech-to-Text (Universal) vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/assemblyai-stt-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/assemblyai-stt-vs-groq-speech-to-text.json", "title": "AssemblyAI Speech-to-Text (Universal) vs Groq Speech-to-Text", "url": "https://www.anchorterminal.com/compare/assemblyai-stt-vs-groq-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt.json", "title": "Azure AI Speech speech-to-text vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-groq-speech-to-text.json", "title": "Azure AI Speech speech-to-text vs Groq Speech-to-Text", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-groq-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-giggy-stt.json", "title": "Cartesia Ink vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-groq-speech-to-text.json", "title": "Cartesia Ink vs Groq Speech-to-Text", "url": "https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-groq-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/deepgram-stt-vs-giggy-stt.json", "title": "Deepgram Speech-to-Text (Nova-3, Flux) vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/deepgram-stt-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/deepgram-stt-vs-groq-speech-to-text.json", "title": "Deepgram Speech-to-Text (Nova-3, Flux) vs Groq Speech-to-Text", "url": "https://www.anchorterminal.com/compare/deepgram-stt-vs-groq-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-giggy-stt.json", "title": "ElevenLabs Scribe Speech to Text API vs Giggy Speech to Text", "url": "https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-giggy-stt" }, { "json": "https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-groq-speech-to-text.json", "title": "ElevenLabs Scribe Speech to Text API vs Groq Speech-to-Text", "url": "https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-groq-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-gladia-stt.json", "title": "Giggy Speech to Text vs Gladia Speech-to-Text API + MCP", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-gladia-stt" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-google-speech-to-text.json", "title": "Giggy Speech to Text vs Google Cloud Speech-to-Text", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-google-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-mistral-voxtral-transcribe.json", "title": "Giggy Speech to Text vs Mistral Voxtral Transcribe", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-mistral-voxtral-transcribe" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-openai-speech-to-text.json", "title": "Giggy Speech to Text vs OpenAI Speech to Text", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-openai-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-rev-ai-stt.json", "title": "Giggy Speech to Text vs Rev AI Speech-to-Text API", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-rev-ai-stt" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-soniox-stt.json", "title": "Giggy Speech to Text vs Soniox Speech-to-Text", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-soniox-stt" }, { "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt.json", "title": "Giggy Speech to Text vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt" }, { "json": "https://www.anchorterminal.com/compare/gladia-stt-vs-groq-speech-to-text.json", "title": "Gladia Speech-to-Text API + MCP vs Groq Speech-to-Text", "url": "https://www.anchorterminal.com/compare/gladia-stt-vs-groq-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/google-speech-to-text-vs-groq-speech-to-text.json", "title": "Google Cloud Speech-to-Text vs Groq Speech-to-Text", "url": "https://www.anchorterminal.com/compare/google-speech-to-text-vs-groq-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-mistral-voxtral-transcribe.json", "title": "Groq Speech-to-Text vs Mistral Voxtral Transcribe", "url": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-mistral-voxtral-transcribe" }, { "json": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-openai-speech-to-text.json", "title": "Groq Speech-to-Text vs OpenAI Speech to Text", "url": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-openai-speech-to-text" }, { "json": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-rev-ai-stt.json", "title": "Groq Speech-to-Text vs Rev AI Speech-to-Text API", "url": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-rev-ai-stt" }, { "json": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-soniox-stt.json", "title": "Groq Speech-to-Text vs Soniox Speech-to-Text", "url": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-soniox-stt" }, { "json": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-speechmatics-stt.json", "title": "Groq Speech-to-Text vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/groq-speech-to-text-vs-speechmatics-stt" } ], "panel": null, "scores": [ { "by": 40, "edge": "groq-speech-to-text", "giggy-stt": 50, "groq-speech-to-text": 90, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 10, "edge": "giggy-stt", "giggy-stt": 68, "groq-speech-to-text": 58, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 7, "edge": "groq-speech-to-text", "giggy-stt": 68, "groq-speech-to-text": 75, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 22, "edge": "groq-speech-to-text", "giggy-stt": 57, "groq-speech-to-text": 79, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 10, "edge": "groq-speech-to-text", "giggy-stt": 30, "groq-speech-to-text": 40, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 21, "edge": "giggy-stt", "giggy-stt": 69, "groq-speech-to-text": 48, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 55, "edge": "groq-speech-to-text", "giggy-stt": 30, "groq-speech-to-text": 85, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Groq Speech-to-Text scores 70.1 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 5 of 7 scored categories. Giggy Speech to Text leads on schema \u0026 documentation and maintenance \u0026 community. Both do speech-to-text.", "tasks": [ { "a": "$0.04 to $0.09", "b": "$0.04 to $0.11", "cheaper": "", "task": "Transcribe a one-hour meeting", "what": "60 minutes of recorded audio" } ], "tutorials": [], "verdicts": { "giggy-stt": "Fast transcription costs $0.04 an input audio hour and Batch is free up to 25 minutes, with published rate limits, Retry-After and a rule to check a generation before resubmitting. Input must be 16 kHz mono WAV, there are no timestamps or speaker labels, the status page covers text-to-speech only, and no company is named on pages we could read.", "groq-speech-to-text": "Whisper Large v3 Turbo costs $0.04 an audio hour and Whisper Large v3 $0.111, with a no-card free plan and zero data retention as a self-serve setting. There is no streaming endpoint, no diarisation and no subtitle output, and uploads stop at 25 MB on the free plan and 100 MB on the Developer plan." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/giggy-stt-vs-groq-speech-to-text", "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-groq-speech-to-text.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/giggy-stt-vs-groq-speech-to-text.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/giggy-stt-vs-groq-speech-to-text.min.md" }, "markdown": "Groq Speech-to-Text scores 70.1 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 5 of 7 scored categories. Giggy Speech to Text leads on schema \u0026 documentation and maintenance \u0026 community. Both do speech-to-text.\n\n- Giggy Speech to Text: grade C, 54.5/100, rank #805 of 1349. Markdown https://www.anchorterminal.com/tools/giggy-stt.md · JSON https://www.anchorterminal.com/api/v1/tools/giggy-stt.json\n- Groq Speech-to-Text: grade BB, 70.1/100, rank #192 of 1349. Markdown https://www.anchorterminal.com/tools/groq-speech-to-text.md · JSON https://www.anchorterminal.com/api/v1/tools/groq-speech-to-text.json\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 253 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n\n## Which one, for what\n\n### Giggy Speech to Text (C)\n\nGood for: Agents and voice pipelines that need plain transcripts of recorded or live speech at a few cents an hour, or free for clips under 25 minutes, and can convert audio to 16 kHz mono WAV first.\n\nAhead on:\n- Schema \u0026 documentation, 68 against 58\n- Maintenance \u0026 community, 69 against 48\n\nWatch for: Recorded input must be PCM16 mono 16 kHz WAV, so MP3, M4A, stereo or other sample rates need converting before upload\n\n### Groq Speech-to-Text (BB)\n\nGood for: Suited to cheap, fast transcription of recorded files in many languages, and to agents that already hold a Groq key or use an OpenAI-compatible client.\n\nAhead on:\n- Reliability, 90 against 50\n- Agent ergonomics, 75 against 68\n- Security \u0026 auth, 79 against 57\n- Payments \u0026 pricing, 40 against 30\n- Transparency \u0026 trust, 85 against 30\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Free to start without a card\n\nWatch for: No streaming or realtime endpoint and no diarisation in the reviewed documentation\n\n\n## Why the edge\n\n### Transparency \u0026 trust: Groq Speech-to-Text +55\n\n- Giggy Speech to Text (30): Closed hosted service. The terms exist at /terms but draw by script and could not be read, so their clarity is unknown; the SDK, Pipecat and MCP repositories are ISC and the n8n node MIT (5 of 30). What could be read agrees with itself, no training on user data, an optional 12-hour retention flag, a seven-day window for Batch audio, and the EEA compute flag. The privacy policy, any DPA and a retention period for transcripts were not found or not readable (12 of 30). No deprecation policy or dated notices, only a statement that the legacy origin remains supported (3 of 20). No subprocessors or data locations are named; the EEA-adequate compute option implies processing outside the EEA by default without saying where (3 of 20). 5+12+3+3 = 23.\n- Groq Speech-to-Text (85): The hosted service is closed under the Groq Services Agreement. Both models are OpenAI's published Whisper weights, linked from each model card, served with Groq's own quantisation, and the SDKs are Apache-2.0 (20 of 30). The data page, the services agreement and the DPA agree on retention and on the training ban. The agreement limits zero retention to 'Eligible Customers' while the data page says all customers may enable it, and the privacy policy effective 12 November 2025 says it does not cover customer data sent to GroqCloud (26 of 30). A deprecations page with dated history and replacements, with no minimum notice period (14 of 20). Customer data is kept in Google Cloud storage in the United States, and the DPA gives 15 days' notice of sub-processor changes to subscribers. The sub-processor list is in the trust centre we could not read (12 of 20).\n\n### Reliability: Groq Speech-to-Text +40\n\n- Giggy Speech to Text (50): Hosted reading, as for deepgram-stt and cartesia-ink-stt. Giggy's own status page at https://giggy.ai/status lists one component, Text to speech, operational on 4 November 2026 with 98.61 per cent observed availability and 99.85 per cent completion over 14 days, refreshed every 30 minutes. It has no speech-to-text component and no incident log, so it shows nothing about this surface (5 of 20). No readable incident history, so the floor (5). Rate limits are published per credential, 60 Batch generation POSTs, 120 event-stream GETs, 600 reads and 120 MCP requests a minute, with eight live contexts per controller; Fast and Streaming POSTs have no per-credential limit and Batch queue capacity isn't given (15). 429 carries `Retry-After` with `RateLimit-*` headers, and the docs give a safe-retry rule for uploads, read the generation by UUID after a timeout or 504 and never upload the same recording again. REST transcription has no idempotency key, but the MCP `transcribe_audio` tool takes one (15). No SLA on the pricing page or in the docs; the terms could not be read (0). No beta or preview label on the API, the SDKs or the MCP server (10). 5+5+15+15+0+10 = 50.\n- Groq Speech-to-Text (90): Hosted reading. The status page at groqstatus.com (incident.io) lists `whisper-large-v3` and `whisper-large-v3-turbo` as separate production components (20). On 1 November 2026 it showed both at 100% uptime from 3 August to 1 November with \"No incidents\", and neither its RSS feed nor the incident pages it links names either model. The window's three incidents were on other components (an enterprise preview model on 10 and 12 October, and console errors on 12 October that Groq said did not affect inference), plus maintenance on 9 and 18 October (30). The rate limits page gives numbers for both models, 20 requests a minute, 2,000 a day, 7,200 audio seconds an hour and 28,800 a day on the free plan, re-read on 1 November, and the models page gives 300 requests a minute and 200,000 audio seconds an hour (v3) and 400 and 400,000 (Turbo) on the Developer plan (15). A 429 carries `retry-after`, the security onboarding page says to back off exponentially on 429 and 5xx, and transcription is a stateless call (15). The enterprise Performance Tier's 99.9% availability SLA names three language models and neither Whisper model, per the 8 October check, so no SLA was found for this product (0). Both models are listed as production models (10). 20+30+15+15+0+10 = 90.\n\n### Security \u0026 auth: Groq Speech-to-Text +22\n\n- Giggy Speech to Text (57): Model reading of the checklist, with training and retention in place of the least-privilege and injection lines, as for deepgram-stt and cartesia-ink-stt. Keys prefixed `giggy_sk_` are shown once on creation or rotation, rejected immediately after revocation, limited to ten per space and tied to the space that made them, with REST budgets that Workspace members can set. No scopes or expiry were found. The key travels in a header only, with no query-string option documented (22 of 30). The workspace privacy guide states 'Giggy does not use user data to train models'. The privacy policy and terms draw by script and could not be read to confirm it (18 of 20). Requests can opt into 12-hour content retention and EEA-adequate compute, and a guide gives Batch audio a seven-day window, but no retention period for transcripts was found and results can vanish after an unstated 'retention cleanup' (9 of 15). Generation history per space and a request ID on errors; no audit log of key or admin actions was found (8 of 15). /.well-known/security.txt answered a client error, none of the six repositories has a SECURITY.md, and no disclosure policy, bug bounty or certification was found on the pages that rendered; the support page that may name a reporting route drew by script (0 of 20). 22+18+9+8+0 = 57.\n- Groq Speech-to-Text (79): Model reading, scored like the GroqCloud listing. Bearer keys belong to one project, each project has its own rate limits per model, and the security onboarding page covers rotation and immediate revocation. No way to limit a key to the audio endpoints was found (26 of 30). The services agreement, last modified 22 June 2026, says Groq may not use inputs or outputs for training or fine-tuning unless the customer grants permission (20). Inputs and outputs are not retained by default, logs kept for reliability or abuse last up to 30 days, and the data page lists both audio endpoints as eligible for zero data retention, a setting in Data Controls (15). Usage data and request logs per project and a read-only Reader role. No audit log of administrative actions was found (10 of 15). security.txt on groq.com holds a Contact line only, with no expiry date. The DPA commits to SOC 2 Type II audits, and the trust centre at trust.groq.com is drawn by script and was not read (8 of 20).\n\n### Maintenance \u0026 community: Giggy Speech to Text +21\n\n- Giggy Speech to Text (69): Read as a model, on the lines deepgram-stt and cartesia-ink-stt use (recency 30, dated releases 10, deprecation notice 10, support 15, issues 10, SDKs 15, health 10). The Python and Node SDKs released 0.2.1 and the Pipecat adapter 0.3.3 on 1 November 2026, and the examples and MCP repositories changed on 3 November (30). Since 7 October the Node SDK has four tagged releases, the Python SDK six and the Pipecat adapter six, the STT ones from 12 October (10). No deprecation policy or dated notice. The legacy `https://giggy.ai/v1` origin and older SDK transports are kept 'for existing integrations' with no end date (2 of 10). No service changelog; the support and contact pages drew by script, and GitHub issues is the support URL in the plugin manifest (5 of 15). GitHub issues and reply times could not be read, since the GitHub API was not available to this session (0 of 10). Official SDKs current, `giggy` and `@giggy-ai/sdk` at 0.2.1 on PyPI and npm (15). CI workflows in five repositories and publish workflows, the n8n node through npm OIDC with provenance, and two runtime dependencies in each SDK; run results not seen (7 of 10). 30+10+2+5+0+15+7 = 69.\n- Groq Speech-to-Text (48): Model reading, as for the GroqCloud listing, counting changes to this listing's own models and endpoints, as the OpenAI listings read their own models and the AWS listings their own service models. No dated change to speech-to-text was found in 2026. The Python SDK 1.7.0 (tag 25 August, uploaded to PyPI on 26 August 2026 per the 8 October check) and TypeScript SDK 1.6.0 (25 August) add chat reasoning guidance, CI workflows and lock-file updates and leave the audio code untouched. The last SDK change to the transcription code was Python 1.2.0 on 18 April 2026. A release that doesn't change this service can't set the recency line in this run, as for the Azure Translator listing, and Muse Spark Open's 15 October preview is a chat model, counted on the GroqCloud listing. So no change within 180 days was found (0, was 20). The deprecations page promises an email and a named replacement and states no minimum notice. The one speech-to-text retirement on it is `distil-whisper-large-v3-en` on 23 August 2025, and the newest announcement is 24 August 2026, for Compound (4 of 12). Neither Whisper model was retired or renamed in the last 90 days (8 of 8). A dated docs changelog with a feed exists, its newest entry is 18 April and none of its entries concern speech-to-text (8 of 15). SDK issue replies not sampled (5 of 10). Official `groq` 1.7.0 on PyPI and `groq-sdk` 1.6.0 on npm, per the tags (15). CI, release automation and lock-file vulnerability fixes in 1.7.0 (8 of 10). 0+4+8+8+5+15+8 = 48.\n\n### Schema \u0026 documentation: Giggy Speech to Text +10\n\n- Giggy Speech to Text (68): Model reading, with the API reference. The docs and llms.txt link an OpenAPI 3.1 file at https://api.giggy.ai/v1/openapi.json, but api.giggy.ai's robots.txt could not be read, so the file wasn't fetched. We credit only what the reference pages show of it, typed `mode` values, the privacy booleans and a typed error body, and there is no AsyncAPI file for the live socket (12 of 25). llms.txt and llms-full.txt (10). The docs say when to use each mode and transport, that MCP is not a live transport, that an OpenAI base-URL swap does not work for STT, and which options have no equivalent, more than most listings say about when not to use something (17 of 20). `mode` is an enum of three values and the input format is fixed, but `language` is a free code or name with no published list, and the MCP input schemas were read only from the repository's docs, not from the server (11 of 15). Examples in the SDK READMEs, the examples repository and a Postman collection, and an error table of ten statuses with `type`, `code`, `message`, `status`, `request_id` and `param` fields, though only one code value is named (11 of 15). Paths are under `/v1` and the SDK and Pipecat repositories keep CHANGELOG.md files, dated in two of them, but there is no service changelog (7 of 15). 12+10+17+11+11+7 = 68.\n- Groq Speech-to-Text (58): Model reading. No OpenAPI document was found in the docs, and the Python SDK's `.stats.yml` carries an endpoint count with no specification URL (0). llms.txt at console.groq.com with Markdown twins of the guide, model cards and reference (10). The guide says which model to pick for accuracy or price, that translation is English only and that the `prompt` steers style within 224 tokens, and it explains the `verbose_json` quality fields (15 of 20). The reference lists allowed values for `response_format` and `timestamp_granularities[]` and marks `model` as required, while `language` and `url` are plain strings (11 of 15). curl, Python and JavaScript examples for both endpoints and a shared errors page with a typed error object. The Batch page's sample still names `distil-whisper-large-v3-en`, shut down on 23 August 2025, on 1 November (12 of 15). The docs changelog is dated, its newest entry is still 18 April on 1 November and no entry in it concerns speech-to-text (10 of 15).\n\n### Payments \u0026 pricing: Groq Speech-to-Text +10\n\n- Giggy Speech to Text (30): No x402, MPP or L402 (0). Per-hour prices for Batch, Fast and Streaming and every credit pack published without a login (20). Free Batch transcription and Personal free credits topped up to 10,000 a month, but no page read says whether a card is needed, so half, as other listings read an unstated card requirement (10 of 20). A person signs up in a browser and creates the key there; no programmatic key route was found (0). 0+20+10+0 = 30.\n- Groq Speech-to-Text (40): No x402, MPP or L402 (0). $0.04 an audio hour for Whisper Large v3 Turbo and $0.111 for Whisper Large v3 on the models page and model cards, read without a login (20). The free plan needs no card and includes both Whisper models at its own rate limits (20). A person signs up in the console in a browser (0).\n\n## Score by category\n\n| Category | Weight | Giggy Speech to Text | Groq Speech-to-Text | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 50 | 90 | Groq Speech-to-Text +40 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 68 | 58 | Giggy Speech to Text +10 |\n| Agent ergonomics | 13% (16.2 this run) | 68 | 75 | Groq Speech-to-Text +7 |\n| Security \u0026 auth | 14% (17.5 this run) | 57 | 79 | Groq Speech-to-Text +22 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | Groq Speech-to-Text +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 69 | 48 | Giggy Speech to Text +21 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 30 | 85 | Groq Speech-to-Text +55 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.5 · C** | **70.1 · BB** | |\n\n## Facts side by side\n\n| Fact | Giggy Speech to Text | Groq Speech-to-Text |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Giggy | Groq |\n| Hosted endpoint | `https://api.giggy.ai/v1` | `https://api.groq.com/openai/v1` |\n| Transports | HTTP, SSE (legacy), websocket, Streamable HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| Licence | Proprietary hosted service. The terms page draws its text by script and was not read. The Python and Node SDKs, the Pipecat adapter and the MCP repository are ISC (Copyright 2026 Giggy), the n8n node MIT | Proprietary hosted service under the Groq Services Agreement. The SDKs are Apache-2.0 and the Whisper weights are published by OpenAI on Hugging Face |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| MCP registry | `io.github.GRQDigitalCapital/giggy-mcp` | not listed |\n| Last release | 2026-11-01 | 2026-08-26 |\n| Terms last updated | not read yet | 2026-06-22 |\n| Privacy policy last updated | not read yet | 2025-11-12 |\n| Customer content may train models | not read yet | not found in the text |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | yes |\n| Terms or service can change without notice | not read yet | not found in the text |\n| Arbitration or class-action waiver | not read yet | not found in the text |\n| Popularity | none | 621 stars |\n| Agent reviews | none | 3.5/5 (4) |\n\n## What the same task costs\n\n| Task | Giggy Speech to Text | Groq Speech-to-Text |\n| --- | --- | --- |\n| Transcribe a one-hour meeting | $0.04 to $0.09 | $0.04 to $0.11 |\n\n## How the scores have moved\n\n### Giggy Speech to Text\n\nNo score change since it was listed.\n\n### Groq Speech-to-Text\n\n- -1.7 on 1 November 2026: correction\n\n## Verdicts\n\n**Giggy Speech to Text.** Fast transcription costs $0.04 an input audio hour and Batch is free up to 25 minutes, with published rate limits, Retry-After and a rule to check a generation before resubmitting. Input must be 16 kHz mono WAV, there are no timestamps or speaker labels, the status page covers text-to-speech only, and no company is named on pages we could read.\n\n**Groq Speech-to-Text.** Whisper Large v3 Turbo costs $0.04 an audio hour and Whisper Large v3 $0.111, with a no-card free plan and zero data retention as a self-serve setting. There is no streaming endpoint, no diarisation and no subtitle output, and uploads stop at 25 MB on the free plan and 100 MB on the Developer plan.\n\n## Before you call either\n\n### Giggy Speech to Text\n\n1. Pass `mode=batch` explicitly for free transcription. Raw REST and MCP default to paid Fast when `mode` is omitted.\n2. Convert audio to PCM16 mono 16 kHz WAV first and send it as the raw body with `Content-Type: audio/wav`.\n3. Keep the `generation_uuid` from the 202 and poll `GET /v1/generations/{uuid}`. Never re-upload after a timeout or 504 before reading that generation.\n4. Fetch the transcript from the signed `generation.result.url` after completion, without the API key. It can disappear after retention cleanup.\n5. On the live WebSocket, replace earlier text with each cumulative `transcription` event and publish final text only after `input_complete`.\n\n### Groq Speech-to-Text\n\n1. Send `whisper-large-v3-turbo` for transcription and `whisper-large-v3` for translation to English. The translations endpoint does not accept Turbo.\n2. Pass `url` instead of `file` for audio over 25 MB, and split anything over the plan's size limit into overlapping chunks before sending.\n3. Set `response_format` to `verbose_json` before asking for `timestamp_granularities[]`. Word timestamps add latency, segment timestamps do not.\n4. Every request is billed as at least 10 seconds of audio, so join very short clips where the task allows.\n5. Read `retry-after` on a 429 and back off. Audio limits count seconds an hour and a day as well as requests.\n\n## Questions\n\n### Which is better for AI agents, Giggy Speech to Text or Groq Speech-to-Text?\n\nGroq Speech-to-Text scores 70.1 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 5 of 7 scored categories. Giggy Speech to Text leads on schema \u0026 documentation and maintenance \u0026 community.\n\n### Do Giggy Speech to Text and Groq Speech-to-Text need an API key?\n\nBoth need an API key.\n\n### Can an agent call Giggy Speech to Text and Groq Speech-to-Text without installing anything?\n\nYes. Giggy Speech to Text has a hosted endpoint at https://api.giggy.ai/v1 and Groq Speech-to-Text at https://api.groq.com/openai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/giggy-stt-vs-groq-speech-to-text.json, and with the fewest tokens: https://www.anchorterminal.com/compare/giggy-stt-vs-groq-speech-to-text.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"giggy-stt\", \"b\": \"groq-speech-to-text\"}`. From a terminal: `anchor compare giggy-stt groq-speech-to-text`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/giggy-stt.json and https://www.anchorterminal.com/api/v1/tools/groq-speech-to-text.json\n\n## Other comparisons with Giggy Speech to Text or Groq Speech-to-Text\n\n- [Amazon Transcribe vs Giggy Speech to Text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-giggy-stt.md)\n- [Amazon Transcribe vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-groq-speech-to-text.md)\n- [AssemblyAI Speech-to-Text (Universal) vs Giggy Speech to Text](https://www.anchorterminal.com/compare/assemblyai-stt-vs-giggy-stt.md)\n- [AssemblyAI Speech-to-Text (Universal) vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/assemblyai-stt-vs-groq-speech-to-text.md)\n- [Azure AI Speech speech-to-text vs Giggy Speech to Text](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-giggy-stt.md)\n- [Azure AI Speech speech-to-text vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-groq-speech-to-text.md)\n- [Cartesia Ink vs Giggy Speech to Text](https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-giggy-stt.md)\n- [Cartesia Ink vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-groq-speech-to-text.md)\n- [Deepgram Speech-to-Text (Nova-3, Flux) vs Giggy Speech to Text](https://www.anchorterminal.com/compare/deepgram-stt-vs-giggy-stt.md)\n- [Deepgram Speech-to-Text (Nova-3, Flux) vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/deepgram-stt-vs-groq-speech-to-text.md)\n- [ElevenLabs Scribe Speech to Text API vs Giggy Speech to Text](https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-giggy-stt.md)\n- [ElevenLabs Scribe Speech to Text API vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-groq-speech-to-text.md)\n- [Giggy Speech to Text vs Gladia Speech-to-Text API + MCP](https://www.anchorterminal.com/compare/giggy-stt-vs-gladia-stt.md)\n- [Giggy Speech to Text vs Google Cloud Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-google-speech-to-text.md)\n- [Giggy Speech to Text vs Mistral Voxtral Transcribe](https://www.anchorterminal.com/compare/giggy-stt-vs-mistral-voxtral-transcribe.md)\n- [Giggy Speech to Text vs OpenAI Speech to Text](https://www.anchorterminal.com/compare/giggy-stt-vs-openai-speech-to-text.md)\n- [Giggy Speech to Text vs Rev AI Speech-to-Text API](https://www.anchorterminal.com/compare/giggy-stt-vs-rev-ai-stt.md)\n- [Giggy Speech to Text vs Soniox Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-soniox-stt.md)\n- [Giggy Speech to Text vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt.md)\n- [Gladia Speech-to-Text API + MCP vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/gladia-stt-vs-groq-speech-to-text.md)\n- [Google Cloud Speech-to-Text vs Groq Speech-to-Text](https://www.anchorterminal.com/compare/google-speech-to-text-vs-groq-speech-to-text.md)\n- [Groq Speech-to-Text vs Mistral Voxtral Transcribe](https://www.anchorterminal.com/compare/groq-speech-to-text-vs-mistral-voxtral-transcribe.md)\n- [Groq Speech-to-Text vs OpenAI Speech to Text](https://www.anchorterminal.com/compare/groq-speech-to-text-vs-openai-speech-to-text.md)\n- [Groq Speech-to-Text vs Rev AI Speech-to-Text API](https://www.anchorterminal.com/compare/groq-speech-to-text-vs-rev-ai-stt.md)\n- [Groq Speech-to-Text vs Soniox Speech-to-Text](https://www.anchorterminal.com/compare/groq-speech-to-text-vs-soniox-stt.md)\n- [Groq Speech-to-Text vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/groq-speech-to-text-vs-speechmatics-stt.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-04", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Giggy Speech to Text vs Groq Speech-to-Text", "url": "" } ], "description": "Groq Speech-to-Text scores 70.1 (BB) to Giggy Speech to Text's 54.5 (C) for speech-to-text. 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