{ "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": 806, "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": "2026-11-04T21:39:24.734230033Z", "lastOk": true, "lastStatus": 404, "lastMs": 398, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 286, "p95ms24h": 654, "samples24h": 64, "samples30d": 64, "days": [ { "date": "2026-11-04", "probes": 64, "ok": 64 } ] }, "vendorStatus": { "page": "https://giggy.ai/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-04T21:25:54.463598549Z" }, "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": "2026-11-04T21:39:24.734230033Z" } }, "answer": "Speechmatics Speech-to-Text scores 67.8 (B) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in every scored category.", "b": { "slug": "speechmatics-stt", "name": "Speechmatics Speech-to-Text", "vendor": "Speechmatics", "vendorUrl": "https://www.speechmatics.com", "kind": "model", "category": "speech-to-text", "summary": "Speechmatics' APIs for batch and real-time transcription, including speaker-attributed turns for voice agents.", "url": "https://www.anchorterminal.com/tools/speechmatics-stt", "markdownUrl": "https://www.anchorterminal.com/tools/speechmatics-stt.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/speechmatics-stt.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/speechmatics-stt.json", "dns": "speechmatics-stt.grade.anchorterminal.com", "repo": "https://github.com/speechmatics/speechmatics-python-sdk", "license": "MIT (SDKs)", "transports": [ "http" ], "remoteUrl": "https://eu1.asr.api.speechmatics.com/v2", "packages": [ { "registry": "npm", "name": "@speechmatics/batch-client" }, { "registry": "npm", "name": "@speechmatics/real-time-client" }, { "registry": "pypi", "name": "speechmatics-batch" }, { "registry": "pypi", "name": "speechmatics-rt" } ], "auth": "api-key", "authNotes": "API key as a Bearer token. Short-lived JWTs for browser and realtime clients, passed as `?jwt=` on the WebSocket URL. A separate management token covers project and key administration.", "pricing": "usage", "pricingNotes": "$100 free credit with no card, then credits at $1 each before discounts, billed per hour of audio. Pre-recorded Melia 1 $0.12, Oak 1 $0.15, Standard $0.23, Enhanced $0.38. Realtime Melia 1 $0.20, Oak 1 $0.23, Standard $0.23, Enhanced $0.40, Linden 1 (Agent STT) $0.15 (was $0.20). Translation adds $0.49 an hour. Credit packs and subscriptions give up to 25 per cent off, and opting in to model training 33 per cent off. Sign-up and pack credits last 12 months (https://www.speechmatics.com/pricing).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402 or machine payment in the docs or pricing. Card or prepaid credits only (checked 2026-09-30).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 20, "npmWeekly": 58050, "pypiWeekly": 48085, "asOf": "2026-09-30" }, "docsUrl": "https://docs.speechmatics.com", "llmsTxt": "https://docs.speechmatics.com/llms.txt", "openapi": "https://docs.speechmatics.com/batch.yaml", "capabilities": [ "speech.stt", "speech.streaming", "speech.batch", "speech.diarisation", "speech.languages", "speech.translation" ], "tags": [ "hosted", "no-card", "closed-source", "python", "typescript", "llms-txt", "streaming", "batch", "webhooks", "async-jobs", "enterprise", "self-hosted" ], "lastRelease": "2026-10-26", "graded": true, "anchor": { "graded": true, "score": 67.8, "grade": "B", "agentReady": false, "rank": 280, "ranked": true, "rankOf": 1349, "categoryRank": 9, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 80, "maintenance": 70, "payments": 40, "reliability": 70, "schema": 90, "security": 65, "transparency": 76 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": -3, "negativeNotes": [ "7 August 2026, -3. Published behaviour changed for existing clients, announced the day it took effect. The 2026.08.07 Batch SaaS changelog entry says the default behaviour of GET /jobs/{jobid} and GET /jobs/{jobid}/transcript \"is changing\" so requests wait up to 2 seconds before returning unless the caller sends `wait=0`, and the docs were changed the same day. The rise to 5 seconds was likewise announced in the 2026.09.23 entry on the day it applied (the August docs said only that the default \"will increase\"), and folds into the same term (https://speechmatics.featurebase.app/en/changelog/20260807-batch-saas)" ], "verdict": "Training is opt-in and real-time audio is not stored. Enhanced transcription costs $0.38 an hour for files and $0.40 for live audio.", "bestFor": "Regulated or privacy-sensitive audio, multilingual batch with Melia 1, and voice agents that want speaker-attributed turns.", "strengths": [ "Training is opt-in only, and realtime audio isn't stored", "ISO/IEC 27001:2022 and SOC 2 Type II", "A Swagger 2.0 file for the Batch API, linked from the API reference", "Transcripts fetched as plain text, JSON or SRT", "$100 credit with no card" ], "weaknesses": [ "Enhanced costs $0.38 to $0.40 an hour, above most rivals", "Batch options travel as a JSON string inside a multipart form field", "429s carry a reason but no Retry-After or backoff guidance", "Realtime temporary keys travel in the WebSocket URL", "Free plan allows 2 realtime sessions" ], "agentNotes": [ "Set `\"model\": \"enhanced\"` explicitly. The default is `standard`", "Use notifications instead of polling. Polling waits 5 seconds by default since the 23 September 2026 change, and `wait=0` turns that off", "Fetch batch transcripts within 7 days. After that the API returns 404 `expired`", "Pass a `fetch_data` URL for files over 1 GB", "Use `/v2/agent` with `linden-1` for live agents instead of the plain realtime path", "Read the Batch API's Swagger file at https://docs.speechmatics.com/batch.yaml before building the multipart `config` field" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 67.8 } ], "editorialScores": { "ergonomics": 80, "maintenance": 70, "payments": 40, "reliability": 70, "schema": 90, "security": 65, "transparency": 65 }, "provenanceScore": 87, "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": { "http": "curl -X POST https://eu1.asr.api.speechmatics.com/v2/jobs/ -H \"Authorization: Bearer $SPEECHMATICS_API_KEY\" \\\n -F data_file=@call.wav \\\n -F config='{\"type\":\"transcription\",\"transcription_config\":{\"language\":\"en\",\"model\":\"enhanced\",\"diarization\":\"speaker\"}}'" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/speechmatics-stt" }, "area": "voice", "unitPrices": [ { "item": "Batch Melia 1", "unit": "audio-minute", "usd": 0.002, "note": "published as $0.12 an hour" }, { "item": "Batch Oak 1", "unit": "audio-minute", "usd": 0.0025, "note": "published as $0.15 an hour" }, { "item": "Batch Standard", "unit": "audio-minute", "usd": 0.0038, "note": "published as $0.23 an hour" }, { "item": "Batch Enhanced", "unit": "audio-minute", "usd": 0.0063, "note": "published as $0.38 an hour" }, { "item": "Realtime Melia 1", "unit": "audio-minute", "usd": 0.0033, "note": "published as $0.20 an hour" }, { "item": "Realtime Oak 1", "unit": "audio-minute", "usd": 0.0038, "note": "published as $0.23 an hour" }, { "item": "Realtime Standard", "unit": "audio-minute", "usd": 0.0038, "note": "published as $0.23 an hour" }, { "item": "Realtime Enhanced", "unit": "audio-minute", "usd": 0.0067, "note": "published as $0.40 an hour" }, { "item": "Linden 1 Agent STT", "unit": "audio-minute", "usd": 0.0025, "note": "published as $0.15 an hour, reduced from $0.20" }, { "item": "Translation add-on", "unit": "audio-minute", "usd": 0.0082, "note": "published as $0.49 an hour" } ], "provenance": { "legalEntity": "Cantab Research Ltd", "domain": "speechmatics.com", "domainRegistered": "2006-05-10", "endpointOnVendorDomain": true, "terms": "https://www.speechmatics.com/legal/terms-of-service", "privacy": "https://www.speechmatics.com/legal/privacy-policy", "statusPage": "https://status.speechmatics.com", "changelog": "https://speechmatics.featurebase.app/en/changelog", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "Trades as Speechmatics, company number 05697423 in England and Wales. US customers contract with Speechmatics (USA) Inc., a Delaware company" ], "score": 87 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/speechmatics-stt.json", "live": { "slug": "speechmatics-stt", "probe": { "target": "https://eu1.asr.api.speechmatics.com/v2", "method": "get", "lastAt": "2026-11-04T21:39:48.308973481Z", "lastOk": true, "lastStatus": 200, "lastMs": 92, "authRequired": false, "uptime24h": 100, "uptime30d": 99.99, "p50ms24h": 92, "p95ms24h": 278, "samples24h": 247, "samples30d": 8090, "days": [ { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "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": 250, "ok": 250 }, { "date": "2026-10-29", "probes": 249, "ok": 249 }, { "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": 247 }, { "date": "2026-11-04", "probes": 223, "ok": 223 } ] }, "vendorStatus": { "page": "https://status.speechmatics.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-04T21:38:55.932606175Z" }, "versions": [ { "registry": "github", "name": "speechmatics/speechmatics-python-sdk", "version": "rt/v1.2.2", "released": "2026-10-28", "seenAt": "2026-11-04T20:56:53.89391136Z" }, { "registry": "npm", "name": "@speechmatics/batch-client", "version": "5.4.2", "seenAt": "2026-11-04T20:56:47.482430189Z" }, { "registry": "npm", "name": "@speechmatics/real-time-client", "version": "8.6.2", "seenAt": "2026-11-04T20:56:47.956697532Z" }, { "registry": "pypi", "name": "speechmatics-batch", "version": "1.1.0", "released": "2026-10-02", "seenAt": "2026-11-04T20:56:49.751856565Z" }, { "registry": "pypi", "name": "speechmatics-rt", "version": "1.2.2", "released": "2026-10-28", "seenAt": "2026-11-04T20:56:50.023883586Z" } ], "githubStars": 20, "npmWeekly": 37485, "pypiWeekly": 18992, "securityTxt": { "url": "https://speechmatics.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-04T15:54:30.24797471Z" }, "llmsTxt": { "url": "https://docs.speechmatics.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-04T15:24:16.847353249Z" }, "domain": { "domain": "speechmatics.com", "registered": "2006-05-10", "source": "https://rdap.verisign.com/com/v1/domain/speechmatics.com", "checkedAt": "2026-11-01T14:25:12.815432254Z" }, "pages": [ { "url": "https://speechmatics.featurebase.app/en/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-11-04T08:33:22.156616606Z", "changedAt": "2026-11-04T08:33:22.156616606Z", "fingerprint": "37ea6ba5f9d5" }, { "url": "https://www.speechmatics.com/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-04T08:45:47.317302636Z", "changedAt": "2026-10-31T06:32:46.178807389Z", "fingerprint": "418585bd0fd0" }, { "url": "https://www.speechmatics.com/legal/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-04T08:45:43.051541759Z", "changedAt": "2026-10-22T01:37:11.822856613Z", "fingerprint": "c62253b5269a" }, { "url": "https://www.speechmatics.com/legal/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-11-04T08:45:45.268604403Z", "changedAt": "2026-10-02T15:28:24.437639084Z", "fingerprint": "33b19ec6fdcb" } ], "github": { "repo": "speechmatics/speechmatics-python-sdk", "checkedAt": "2026-11-02T09:27:04.130422133Z", "stars": 20, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-28T13:31:15Z", "openIssues": 7, "openPRs": 14, "issuesOpened90d": 3, "issuesClosed90d": 1, "prsMerged90d": 12, "ci": { "branch": "main", "runs": 50, "success": 50, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-10-26T09:58:58Z" }, "replies": { "sampled": 18, "unanswered": 6, "medianDays": 6 } }, "updatedAt": "2026-11-04T21:39:48.308973481Z" } }, "edges": [ { "by": 46, "category": "transparency", "leads": "speechmatics-stt", "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": "Closed service with clear terms and MIT SDKs (15). The pricing page, limits pages and security page agree on opt-in training, 7-day batch retention and no storage for realtime (25). The changelog announces behaviour changes in advance, such as the 23 September 2026 switch to waiting polls, but we found no deprecation policy (10). EU1, US1 and AU1 regions documented per endpoint and the service named as Azure-hosted, but no sub-processor list was found (15)." }, "scores": { "a": 30, "b": 76 } }, { "by": 22, "category": "schema", "leads": "speechmatics-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": "A Swagger 2.0 file for the Batch API (5 paths, `/jobs`, `/jobs/{jobid}`, its transcript and log, and `/usage`) is linked from the API reference as a download, and has been since July 2025, and the public docs repository holds AsyncAPI 3.0 files for Realtime and Agent STT (25). llms.txt and Markdown pages (10). Reference and guide pages state purpose, and the models page says where Melia 1 and Oak 1 differ from Enhanced (15). Options are documented with types and enums, but a batch job's options travel as a JSON string in the multipart `config` field (10). Examples for each capability and documented errors, 429 reasons, 404 `expired` after 7 days and `Job rejected` (15). Versioned `/v2` paths and a dated public changelog (15)." }, "scores": { "a": 68, "b": 90 } }, { "by": 20, "category": "reliability", "leads": "speechmatics-stt", "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": "Statuspage at status.speechmatics.com with components per region and history (20). Since 3 August, minor incidents only, each touching one region, one mode or a subset of requests. Batch AU slower for 2 hours on 10 August, a subset of Batch EU1, US1 and US2 jobs failing or slow for 56 minutes on 19 August, Realtime 4003 errors in the EU and US for 37 minutes on 4 September, and TLS errors for a subset of US Realtime sessions for about 2 hours on 10 September. The rest were scheduled database maintenance, a portal maintenance and a billing credit error, and nothing was posted after 10 September (20). Limits published with numbers, 10 new batch jobs and 50 status calls a second, 20,000 concurrent jobs, and 2 or 50 realtime sessions by plan (15). Both batch limits return 429 with a reason, but the docs only suggest notifications instead of polling and give no backoff or Retry-After (5). No SLA found for self-serve plans (0). Batch, Realtime and Agent STT on `/v2/agent` are GA (10)." }, "scores": { "a": 50, "b": 70 } }, { "by": 12, "category": "ergonomics", "leads": "speechmatics-stt", "name": "Agent ergonomics", "reasons": { "a": "API reading, as for the other speech-to-text listings. The transcript is compact, text, detected languages, audio duration and model metadata, with no word array to trim; on an MCP reading five tools would score 25 (20 of 25). Nothing to page through and no output options, no timestamps, subtitles or speaker labels, and recorded input must be PCM16 mono 16 kHz WAV, so the caller converts everything else; a 12-hour recording goes up in one request (10 of 20). Errors are structured with a type, code, message, status, request ID and parameter, and the reference says what each status means and what to do after a 504, but no list of code values was found and the SDK error classes expose only the status and the raw body (13 of 20). Upload retries are left to the caller with clear rules, read the generation first and never re-upload after a timeout; REST has no idempotency key and `client_request_id` is documented as no guarantee, while MCP `transcribe_audio` takes an `idempotency_key`. The SDKs never retry, and closing an unfinished live socket cancels it (13 of 20). Only the audio is required, with official Python and Node SDKs, but raw REST and MCP default to paid Fast while the SDKs default to free Batch (12 of 15). 20+10+13+13+12 = 68.", "b": "API reading of the checklist. Transcripts can be fetched as `txt`, `json-v2` or `srt` (20). The jobs list pages by creation date and limit (20). Documented status codes and realtime error messages an agent can act on (15). No idempotency key on job creation (10). A batch job needs a file and a language. Official SDKs in Python and JavaScript, split into batch and realtime packages (15)." }, "scores": { "a": 68, "b": 80 } }, { "by": 10, "category": "payments", "leads": "speechmatics-stt", "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). Per-hour prices for every model published without a login, with credits at $1 each before discounts (20). $100 credit with no card (20). A person signs up in a browser (0)." }, "scores": { "a": 30, "b": 40 } }, { "by": 8, "category": "security", "leads": "speechmatics-stt", "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 of the checklist, with training and retention in place of least-privilege and injection lines. API keys as bearer tokens, with a separate management token for key administration (20). Browser and realtime clients pass temporary keys as `?jwt=` on the WebSocket URL, with a lifetime the caller sets from 60 to 86,400 seconds, so we deduct half (minus 5). Training is opt-in only, and opting in buys a 33 per cent discount (20). Realtime audio isn't stored, batch data is deleted after 7 days or on request (15). A usage reporting service exists, but we found no per-request audit log (10). ISO/IEC 27001:2022 and SOC 2 Type II with a SafeBase trust centre. No security.txt (404 on 1 November), disclosure policy, bug bounty or public advisories found (5)." }, "scores": { "a": 57, "b": 65 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Giggy", "b": "Speechmatics", "name": "Vendor" }, { "a": "https://api.giggy.ai/v1", "b": "https://eu1.asr.api.speechmatics.com/v2", "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": "Pay per use", "name": "Pricing" }, { "a": "not published", "b": "$0.0025 per minute of audio", "name": "Price for speech-to-text" }, { "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": "MIT (SDKs)", "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": "2026-10-26", "name": "Last release" }, { "a": "not read yet", "b": "no date given", "name": "Terms last updated" }, { "a": "not read yet", "b": "2026-05-27", "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": "20 stars, 58k npm/wk, 48k PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "3.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Speechmatics Speech-to-Text scores 67.8 (B) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in every scored category.", "question": "Which is better for AI agents, Giggy Speech to Text or Speechmatics Speech-to-Text?" }, { "answer": "Both need an API key.", "question": "Do Giggy Speech to Text and Speechmatics Speech-to-Text need an API key?" }, { "answer": "Yes. Giggy Speech to Text has a hosted endpoint at https://api.giggy.ai/v1 and Speechmatics Speech-to-Text at https://eu1.asr.api.speechmatics.com/v2.", "question": "Can an agent call Giggy Speech to Text and Speechmatics Speech-to-Text without installing anything?" } ], "goodFor": [ { "aheadOn": null, "also": [ "No incidents deducted, where Speechmatics Speech-to-Text loses 3 points for them" ], "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, 70 against 50", "Schema \u0026 documentation, 90 against 68", "Agent ergonomics, 80 against 68", "Security \u0026 auth, 65 against 57", "Payments \u0026 pricing, 40 against 30", "Transparency \u0026 trust, 76 against 30" ], "also": [ "Free to start without a card" ], "goodFor": "Regulated or privacy-sensitive audio, multilingual batch with Melia 1, and voice agents that want speaker-attributed turns.", "slug": "speechmatics-stt", "watchFor": "Enhanced costs $0.38 to $0.40 an hour, above most rivals" } ], "history": { "giggy-stt": null, "speechmatics-stt": [ { "causes": [ "correction" ], "date": "2026-11-01", "delta": 0.6, "from": 67.2, "fromGrade": "B", "to": 67.8, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.1, "from": 67.1, "fromGrade": "B", "to": 67.2, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-08", "delta": -0.2, "from": 67.3, "fromGrade": "B", "to": 67.1, "toGrade": "B" } ] }, "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-speechmatics-stt.json", "title": "Amazon Transcribe vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/amazon-transcribe-vs-speechmatics-stt" }, { "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-speechmatics-stt.json", "title": "AssemblyAI Speech-to-Text (Universal) vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/assemblyai-stt-vs-speechmatics-stt" }, { "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-speechmatics-stt.json", "title": "Azure AI Speech speech-to-text vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/azure-speech-to-text-vs-speechmatics-stt" }, { "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-speechmatics-stt.json", "title": "Cartesia Ink vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-speechmatics-stt" }, { "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-speechmatics-stt.json", "title": "Deepgram Speech-to-Text (Nova-3, Flux) vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/deepgram-stt-vs-speechmatics-stt" }, { "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-speechmatics-stt.json", "title": "ElevenLabs Scribe Speech to Text API vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-speechmatics-stt" }, { "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-groq-speech-to-text.json", "title": "Giggy Speech to Text vs Groq Speech-to-Text", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-groq-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/gladia-stt-vs-speechmatics-stt.json", "title": "Gladia Speech-to-Text API + MCP vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/gladia-stt-vs-speechmatics-stt" }, { "json": "https://www.anchorterminal.com/compare/google-speech-to-text-vs-speechmatics-stt.json", "title": "Google Cloud Speech-to-Text vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/google-speech-to-text-vs-speechmatics-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" }, { "json": "https://www.anchorterminal.com/compare/mistral-voxtral-transcribe-vs-speechmatics-stt.json", "title": "Mistral Voxtral Transcribe vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/mistral-voxtral-transcribe-vs-speechmatics-stt" }, { "json": "https://www.anchorterminal.com/compare/openai-speech-to-text-vs-speechmatics-stt.json", "title": "OpenAI Speech to Text vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/openai-speech-to-text-vs-speechmatics-stt" }, { "json": "https://www.anchorterminal.com/compare/rev-ai-stt-vs-speechmatics-stt.json", "title": "Rev AI Speech-to-Text API vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/rev-ai-stt-vs-speechmatics-stt" }, { "json": "https://www.anchorterminal.com/compare/soniox-stt-vs-speechmatics-stt.json", "title": "Soniox Speech-to-Text vs Speechmatics Speech-to-Text", "url": "https://www.anchorterminal.com/compare/soniox-stt-vs-speechmatics-stt" } ], "panel": null, "scores": [ { "by": 20, "edge": "speechmatics-stt", "giggy-stt": 50, "key": "reliability", "name": "Reliability", "speechmatics-stt": 70, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 22, "edge": "speechmatics-stt", "giggy-stt": 68, "key": "schema", "name": "Schema \u0026 documentation", "speechmatics-stt": 90, "weight": 13 }, { "by": 12, "edge": "speechmatics-stt", "giggy-stt": 68, "key": "ergonomics", "name": "Agent ergonomics", "speechmatics-stt": 80, "weight": 13 }, { "by": 8, "edge": "speechmatics-stt", "giggy-stt": 57, "key": "security", "name": "Security \u0026 auth", "speechmatics-stt": 65, "weight": 14 }, { "by": 10, "edge": "speechmatics-stt", "giggy-stt": 30, "key": "payments", "name": "Payments \u0026 pricing", "speechmatics-stt": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 1, "edge": "speechmatics-stt", "giggy-stt": 69, "key": "maintenance", "name": "Maintenance \u0026 community", "speechmatics-stt": 70, "weight": 7 }, { "by": 46, "edge": "speechmatics-stt", "giggy-stt": 30, "key": "transparency", "name": "Transparency \u0026 trust", "speechmatics-stt": 76, "weight": 7 } ], "summary": "Speechmatics Speech-to-Text scores 67.8 (B) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in every scored category. Both do speech-to-text.", "tasks": [ { "a": "$0.04 to $0.09", "b": "$0.12 to $0.40", "cheaper": "giggy-stt", "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.", "speechmatics-stt": "Training is opt-in and real-time audio is not stored. Enhanced transcription costs $0.38 an hour for files and $0.40 for live audio." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt", "json": "https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt.min.md" }, "markdown": "Speechmatics Speech-to-Text scores 67.8 (B) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in every scored category. Both do speech-to-text.\n\n- Giggy Speech to Text: grade C, 54.5/100, rank #806 of 1349. Markdown https://www.anchorterminal.com/tools/giggy-stt.md · JSON https://www.anchorterminal.com/api/v1/tools/giggy-stt.json\n- Speechmatics Speech-to-Text: grade B, 67.8/100, rank #280 of 1349. Markdown https://www.anchorterminal.com/tools/speechmatics-stt.md · JSON https://www.anchorterminal.com/api/v1/tools/speechmatics-stt.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\nAlso in its favour:\n- No incidents deducted, where Speechmatics Speech-to-Text loses 3 points for them\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### Speechmatics Speech-to-Text (B)\n\nGood for: Regulated or privacy-sensitive audio, multilingual batch with Melia 1, and voice agents that want speaker-attributed turns.\n\nAhead on:\n- Reliability, 70 against 50\n- Schema \u0026 documentation, 90 against 68\n- Agent ergonomics, 80 against 68\n- Security \u0026 auth, 65 against 57\n- Payments \u0026 pricing, 40 against 30\n- Transparency \u0026 trust, 76 against 30\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: Enhanced costs $0.38 to $0.40 an hour, above most rivals\n\n\n## Why the edge\n\n### Transparency \u0026 trust: Speechmatics Speech-to-Text +46\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- Speechmatics Speech-to-Text (76): Closed service with clear terms and MIT SDKs (15). The pricing page, limits pages and security page agree on opt-in training, 7-day batch retention and no storage for realtime (25). The changelog announces behaviour changes in advance, such as the 23 September 2026 switch to waiting polls, but we found no deprecation policy (10). EU1, US1 and AU1 regions documented per endpoint and the service named as Azure-hosted, but no sub-processor list was found (15).\n\n### Schema \u0026 documentation: Speechmatics Speech-to-Text +22\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- Speechmatics Speech-to-Text (90): A Swagger 2.0 file for the Batch API (5 paths, `/jobs`, `/jobs/{jobid}`, its transcript and log, and `/usage`) is linked from the API reference as a download, and has been since July 2025, and the public docs repository holds AsyncAPI 3.0 files for Realtime and Agent STT (25). llms.txt and Markdown pages (10). Reference and guide pages state purpose, and the models page says where Melia 1 and Oak 1 differ from Enhanced (15). Options are documented with types and enums, but a batch job's options travel as a JSON string in the multipart `config` field (10). Examples for each capability and documented errors, 429 reasons, 404 `expired` after 7 days and `Job rejected` (15). Versioned `/v2` paths and a dated public changelog (15).\n\n### Reliability: Speechmatics Speech-to-Text +20\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- Speechmatics Speech-to-Text (70): Statuspage at status.speechmatics.com with components per region and history (20). Since 3 August, minor incidents only, each touching one region, one mode or a subset of requests. Batch AU slower for 2 hours on 10 August, a subset of Batch EU1, US1 and US2 jobs failing or slow for 56 minutes on 19 August, Realtime 4003 errors in the EU and US for 37 minutes on 4 September, and TLS errors for a subset of US Realtime sessions for about 2 hours on 10 September. The rest were scheduled database maintenance, a portal maintenance and a billing credit error, and nothing was posted after 10 September (20). Limits published with numbers, 10 new batch jobs and 50 status calls a second, 20,000 concurrent jobs, and 2 or 50 realtime sessions by plan (15). Both batch limits return 429 with a reason, but the docs only suggest notifications instead of polling and give no backoff or Retry-After (5). No SLA found for self-serve plans (0). Batch, Realtime and Agent STT on `/v2/agent` are GA (10).\n\n### Agent ergonomics: Speechmatics Speech-to-Text +12\n\n- Giggy Speech to Text (68): API reading, as for the other speech-to-text listings. The transcript is compact, text, detected languages, audio duration and model metadata, with no word array to trim; on an MCP reading five tools would score 25 (20 of 25). Nothing to page through and no output options, no timestamps, subtitles or speaker labels, and recorded input must be PCM16 mono 16 kHz WAV, so the caller converts everything else; a 12-hour recording goes up in one request (10 of 20). Errors are structured with a type, code, message, status, request ID and parameter, and the reference says what each status means and what to do after a 504, but no list of code values was found and the SDK error classes expose only the status and the raw body (13 of 20). Upload retries are left to the caller with clear rules, read the generation first and never re-upload after a timeout; REST has no idempotency key and `client_request_id` is documented as no guarantee, while MCP `transcribe_audio` takes an `idempotency_key`. The SDKs never retry, and closing an unfinished live socket cancels it (13 of 20). Only the audio is required, with official Python and Node SDKs, but raw REST and MCP default to paid Fast while the SDKs default to free Batch (12 of 15). 20+10+13+13+12 = 68.\n- Speechmatics Speech-to-Text (80): API reading of the checklist. Transcripts can be fetched as `txt`, `json-v2` or `srt` (20). The jobs list pages by creation date and limit (20). Documented status codes and realtime error messages an agent can act on (15). No idempotency key on job creation (10). A batch job needs a file and a language. Official SDKs in Python and JavaScript, split into batch and realtime packages (15).\n\n### Payments \u0026 pricing: Speechmatics 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- Speechmatics Speech-to-Text (40): No x402, MPP or L402 (0). Per-hour prices for every model published without a login, with credits at $1 each before discounts (20). $100 credit with no card (20). A person signs up in a browser (0).\n\n### Security \u0026 auth: Speechmatics Speech-to-Text +8\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- Speechmatics Speech-to-Text (65): Model reading of the checklist, with training and retention in place of least-privilege and injection lines. API keys as bearer tokens, with a separate management token for key administration (20). Browser and realtime clients pass temporary keys as `?jwt=` on the WebSocket URL, with a lifetime the caller sets from 60 to 86,400 seconds, so we deduct half (minus 5). Training is opt-in only, and opting in buys a 33 per cent discount (20). Realtime audio isn't stored, batch data is deleted after 7 days or on request (15). A usage reporting service exists, but we found no per-request audit log (10). ISO/IEC 27001:2022 and SOC 2 Type II with a SafeBase trust centre. No security.txt (404 on 1 November), disclosure policy, bug bounty or public advisories found (5).\n\n## Score by category\n\n| Category | Weight | Giggy Speech to Text | Speechmatics Speech-to-Text | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 50 | 70 | Speechmatics Speech-to-Text +20 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 68 | 90 | Speechmatics Speech-to-Text +22 |\n| Agent ergonomics | 13% (16.2 this run) | 68 | 80 | Speechmatics Speech-to-Text +12 |\n| Security \u0026 auth | 14% (17.5 this run) | 57 | 65 | Speechmatics Speech-to-Text +8 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | Speechmatics 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 | 70 | Speechmatics Speech-to-Text +1 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 30 | 76 | Speechmatics Speech-to-Text +46 |\n| Negative events | ≤15 | 0 | -3 | |\n| **Total** | | **54.5 · C** | **67.8 · B** | |\n\n## Facts side by side\n\n| Fact | Giggy Speech to Text | Speechmatics Speech-to-Text |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Giggy | Speechmatics |\n| Hosted endpoint | `https://api.giggy.ai/v1` | `https://eu1.asr.api.speechmatics.com/v2` |\n| Transports | HTTP, SSE (legacy), websocket, Streamable HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Pay per use |\n| Price for speech-to-text | not published | $0.0025 per minute of audio |\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 | MIT (SDKs) |\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 | 2026-10-26 |\n| Terms last updated | not read yet | no date given |\n| Privacy policy last updated | not read yet | 2026-05-27 |\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 | 20 stars, 58k npm/wk, 48k PyPI/wk |\n| Agent reviews | none | 3.5/5 (2) |\n\n## What the same task costs\n\n| Task | Giggy Speech to Text | Speechmatics Speech-to-Text |\n| --- | --- | --- |\n| Transcribe a one-hour meeting | $0.04 to $0.09 | $0.12 to $0.40 |\n\n## How the scores have moved\n\n### Giggy Speech to Text\n\nNo score change since it was listed.\n\n### Speechmatics Speech-to-Text\n\n- +0.6 on 1 November 2026: correction\n- +0.1 on 27 October 2026: checklist\n- -0.2 on 8 October 2026: checklist\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**Speechmatics Speech-to-Text.** Training is opt-in and real-time audio is not stored. Enhanced transcription costs $0.38 an hour for files and $0.40 for live audio.\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### Speechmatics Speech-to-Text\n\n1. Set `\"model\": \"enhanced\"` explicitly. The default is `standard`\n2. Use notifications instead of polling. Polling waits 5 seconds by default since the 23 September 2026 change, and `wait=0` turns that off\n3. Fetch batch transcripts within 7 days. After that the API returns 404 `expired`\n4. Pass a `fetch_data` URL for files over 1 GB\n5. Use `/v2/agent` with `linden-1` for live agents instead of the plain realtime path\n6. Read the Batch API's Swagger file at https://docs.speechmatics.com/batch.yaml before building the multipart `config` field\n\n## Questions\n\n### Which is better for AI agents, Giggy Speech to Text or Speechmatics Speech-to-Text?\n\nSpeechmatics Speech-to-Text scores 67.8 (B) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in every scored category.\n\n### Do Giggy Speech to Text and Speechmatics Speech-to-Text need an API key?\n\nBoth need an API key.\n\n### Can an agent call Giggy Speech to Text and Speechmatics Speech-to-Text without installing anything?\n\nYes. Giggy Speech to Text has a hosted endpoint at https://api.giggy.ai/v1 and Speechmatics Speech-to-Text at https://eu1.asr.api.speechmatics.com/v2.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt.json, and with the fewest tokens: https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"giggy-stt\", \"b\": \"speechmatics-stt\"}`. From a terminal: `anchor compare giggy-stt speechmatics-stt`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/giggy-stt.json and https://www.anchorterminal.com/api/v1/tools/speechmatics-stt.json\n\n## Other comparisons with Giggy Speech to Text or Speechmatics 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 Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/amazon-transcribe-vs-speechmatics-stt.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 Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/assemblyai-stt-vs-speechmatics-stt.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 Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/azure-speech-to-text-vs-speechmatics-stt.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 Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/cartesia-ink-stt-vs-speechmatics-stt.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 Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/deepgram-stt-vs-speechmatics-stt.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 Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/elevenlabs-scribe-vs-speechmatics-stt.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 Groq Speech-to-Text](https://www.anchorterminal.com/compare/giggy-stt-vs-groq-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- [Gladia Speech-to-Text API + MCP vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/gladia-stt-vs-speechmatics-stt.md)\n- [Google Cloud Speech-to-Text vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/google-speech-to-text-vs-speechmatics-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- [Mistral Voxtral Transcribe vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/mistral-voxtral-transcribe-vs-speechmatics-stt.md)\n- [OpenAI Speech to Text vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/openai-speech-to-text-vs-speechmatics-stt.md)\n- [Rev AI Speech-to-Text API vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/rev-ai-stt-vs-speechmatics-stt.md)\n- [Soniox Speech-to-Text vs Speechmatics Speech-to-Text](https://www.anchorterminal.com/compare/soniox-stt-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 Speechmatics Speech-to-Text", "url": "" } ], "description": "Speechmatics Speech-to-Text scores 67.8 (B) to Giggy Speech to Text's 54.5 (C) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Giggy Speech to Text C 54.5", "Speechmatics Speech-to-Text B 67.8", "scores" ], "h1": "Giggy Speech to Text vs Speechmatics Speech-to-Text", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/giggy-stt-vs-speechmatics-stt", "published": "2026-10-01", "section": "tools", "title": "Giggy Speech to Text vs Speechmatics Speech-to-Text (2026)", "toc": null, "updated": "2026-11-04", "url": "https://www.anchorterminal.com/compare/giggy-stt-vs-speechmatics-stt" }, "tokens": { "markdown": 5650, "slim": 3480 }, "version": 1 }