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

Giggy Speech to Text vs OpenAI Speech to Text

OpenAI Speech to Text scores 75 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 6 of 7 scored categories. Both do speech-to-text.

Best speech-to-text APIs for AI agents · All 253 stt comparisons

Which one, for what

Giggy Speech to Text C

Best 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.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Recorded input must be PCM16 mono 16 kHz WAV, so MP3, M4A, stereo or other sample rates need converting before upload

OpenAI Speech to Text BB

Best for Suited to plain transcription of recorded files at a low price a minute and to teams already holding an OpenAI key.

Ahead on

  • Reliability, 65 against 50
  • Schema & documentation, 89 against 68
  • Agent ergonomics, 78 against 68
  • Security & auth, 97 against 57
  • Transparency & trust, 90 against 30

Also in its favour

  • Agent-ready, a grade of BB or better

Watch for

whisper-1, gpt-4o-transcribe, gpt-4o-mini-transcribe, gpt-4o-transcribe-diarize and /v1/audio/translations shut down on 26 February 2027

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Transparency & trust OpenAI Speech to Text +60

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.

OpenAI Speech to Text 90

The hosted models are closed with clear terms, the Python SDK Apache-2.0 and the OpenAPI document MIT, per the OpenAI API listing's check of the services agreement. openai.com answered the services agreement with 403 again on 1 November (15, was 12). The data controls page, re-read on 1 November, lists both audio endpoints with no training, no abuse-monitoring retention, no application state and Zero Data Retention eligibility. The DPA, effective 1 January 2026, commits to return or deletion of customer data on termination, 30 days to object to a new subprocessor, and SCCs with the UK Addendum, and nothing in it conflicts with the guide, per the OpenAI API listing's 4 October check, not re-read on 1 November (openai.com answered 403). The same reading as the OpenAI API listing (30 of 30, was 28). A deprecations page with notice periods by model class, dated shutdowns and replacements, including /v1/audio/translations to v1/realtime/translations (20). Data residency is documented by region and endpoint, with the audio endpoints in every listed region for storage and in the United States and Europe for processing. The subprocessor list, updated 9 July 2026, names third-party subprocessors and OpenAI affiliates with processing countries for each and a form for notice of new ones, per the OpenAI API listing's 4 October check, not re-read on 1 November (openai.com answered 403). The same reading as the OpenAI API listing (20 of 20, was 16). 15 + 30 + 20 + 20 = 85.

Security & auth OpenAI Speech to Text +40

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.

OpenAI Speech to Text 97

Model reading, on the lines the OpenAI API and Decisions API listings use. Project keys with permissions per key, service-account keys through the Administration API and workload identity federation, per the Decisions API listing's read of 8 October. The access-control guide, re-read on 1 November, puts audio under one Model capabilities permission with chat completions, embeddings and images, so a restricted key can't be limited to the audio endpoints alone (27 of 30, as the Decisions API listing, was 26). The data controls page says API data isn't used for training unless the customer opts in (20). Both audio endpoints are listed with no abuse-monitoring retention, no application state and Zero Data Retention eligibility, re-read on 1 November (15). The Admin API has an audit log endpoint, and the OpenAPI document has GET /organization/usage/audio_transcriptions grouped by project, API key or model, the per-key usage view both other OpenAI listings got 15 for (15, was 13). security.txt, re-read on 1 November, is PGP-signed, names a Bugcrowd programme and a coordinated disclosure policy and has no Expires field. trust.openai.com, read on 1 November, names SOC 2 Type 2 and ISO/IEC 27001, 27017, 27018 and 27701 (20, was 12 when no trust centre was read, the award both other OpenAI listings carry). 27 + 20 + 15 + 15 + 20 = 97.

Schema & documentation OpenAI Speech to Text +21

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.

OpenAI Speech to Text 89

Model reading. The public OpenAPI 3.1 document in openai/openai-openapi (version 2.3.0, last committed 31 October) covers /audio/transcriptions and /audio/translations and names gpt-transcribe (25). llms.txt and a Markdown twin of every docs page (10). The transcription overview says which model to use for files, live audio, speaker labels, timestamps and translation, and the file guide now opens by recommending gpt-transcribe and saying the specialised examples use deprecated models (18 of 20). response_format, timestamp_granularities and model are enums in the document. language is a plain string, and which formats each model accepts is stated in prose only (12 of 15). Examples in seven languages and curl, and an error codes page with a cause and fix for each. The Markdown twin of the endpoint reference now lists the body parameters, including languages and keywords for gpt-transcribe, but its first example still names the deprecated gpt-4o-transcribe, and the guide lists seven input formats where the document lists nine (12 of 15, was 11). A dated changelog and deprecations page. gpt-transcribe has one alias and no dated snapshot to pin (12 of 15). 25 + 10 + 18 + 12 + 12 + 12 = 89.

Reliability OpenAI Speech to Text +15

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.

OpenAI Speech to Text 65

Hosted reading. status.openai.com (incident.io) lists Audio and Realtime as components of the API group (20). The feed, read on 1 November, names the Audio component in three incidents in the 90 days from 3 August, and their incident pages give the Audio impact. 17 September, "elevated error rates across API models", Audio degraded from 20:20 to 21:20 UTC with ten other API components, an hour. 29 September (last updated 6 October in the feed, which the 9 October check dated 6 October), errors across ChatGPT, Codex and the API, Audio degraded from 17:52 to 23:14 UTC, 5 hours 21 minutes. 21 October, degraded performance across 33 components, Audio from 02:26 to 02:48 UTC, 22 minutes and minor. The first two are API-wide and an hour or more, so two majors, and two majors give 5 of 30, as the OpenAI API, OpenAI Moderation and OpenAI Image API listings read the same two incidents (5, was 20 when the durations were unread). The July incidents are outside the window. The gpt-transcribe model page, re-read on 1 November, gives 5,000 requests a minute on Build, 10,000 on Launch and 30,000 on Grow (15). The rate limits guide, re-read on 1 November, documents Retry-After on 429 and 503, the slow_down and server_is_overloaded codes and backoff with jitter, and transcription is a stateless call (15). No SLA covering the audio endpoints was found, read as for the OpenAI Decisions API listing; the Scale Tier pages are on openai.com and weren't requested (0). gpt-transcribe carries no preview label (10). 20 + 5 + 15 + 15 + 0 + 10 = 65.

Agent ergonomics OpenAI Speech to Text +10

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.

OpenAI Speech to Text 78

API reading, as for the other speech-to-text listings. The default json response is the text, detected languages and usage, text returns a bare string, and segments, words and speaker labels come only when asked for on the models that have them (20 of 25). There is no transcript store to page through. Files stop at 25 MB, the caller splits longer audio, and the gpt-transcribe model page marks the Batch API as not supported (10 of 20). Errors carry message, type, param and code, and the error codes page gives a fix for each status (18 of 20). The call is stateless and safe to retry, and the Python SDK retries 408, 409, 429 and 5xx twice by default. No idempotency key applies to this endpoint (15 of 20). file and model are the only required fields, with guide examples for JavaScript, Python, Go, Java, C# and Ruby SDKs and a CLI (15).

Score by category

CategoryWeight this runGiggy Speech to TextOpenAI Speech to TextEdge
Reliability16%205065OpenAI Speech to Text +15
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.26889OpenAI Speech to Text +21
Agent ergonomics13%16.26878OpenAI Speech to Text +10
Security & auth14%17.55797OpenAI Speech to Text +40
Payments & pricing10%12.53030even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.86971OpenAI Speech to Text +2
Transparency & trust7%8.83090OpenAI Speech to Text +60
Negative events≤1500
TotalC 54.5/100BB 75/100

Facts side by side

FactGiggy Speech to TextOpenAI Speech to Text
KindModel APIModel API
VendorGiggyOpenAI
Hosted endpointhttps://api.giggy.ai/v1https://api.openai.com/v1
TransportsHTTP, SSE (legacy), websocket, Streamable HTTPHTTP, websocket
AuthAPI keyAPI key
PricingFreemiumPay per use
x402nono
LicenceProprietary 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 MITProprietary hosted service. The service terms were not read (see open questions). The Python SDK is Apache-2.0 and the OpenAPI document is MIT
Read-only variant documentednono
llms.txtyesyes
MCP registryio.github.GRQDigitalCapital/giggy-mcpnot listed
Last release2026-11-012026-08-26
Terms last updatednot read yetcouldn't be read
Privacy policy last updatednot read yetcouldn't be read
Customer content may train modelsnot read yetcouldn't be read
Terms restrict automated accessnot read yetcouldn't be read
Terms restrict benchmarkingnot read yetcouldn't be read
Terms or service can change without noticenot read yetcouldn't be read
Arbitration or class-action waivernot read yetcouldn't be read
Popularitynone32k stars
Agent reviewsnone3.3/5 (4)

What the same task costs

TaskGiggy Speech to TextOpenAI Speech to Text
Transcribe a one-hour meeting60 minutes of recorded audio$0.04 to $0.09$0.27 to $1.02

Estimated from each listing's published prices, the same tasks as on the price index. Plans, minimums and free allowances aren't counted.

How the scores have moved

Verdicts

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.

OpenAI Speech to Text

gpt-transcribe costs $0.0045 an audio minute, and the audio endpoints keep no abuse-monitoring logs or application state. Speaker labels, word timestamps and subtitles exist only on whisper-1 and gpt-4o-transcribe-diarize, which shut down on 26 February 2027 with no replacement named for those functions.

Before you call either

Giggy Speech to Text

  1. Pass mode=batch explicitly for free transcription. Raw REST and MCP default to paid Fast when mode is omitted.
  2. Convert audio to PCM16 mono 16 kHz WAV first and send it as the raw body with Content-Type: audio/wav.
  3. 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.
  4. Fetch the transcript from the signed generation.result.url after completion, without the API key. It can disappear after retention cleanup.
  5. On the live WebSocket, replace earlier text with each cumulative transcription event and publish final text only after input_complete.

OpenAI Speech to Text

  1. Send gpt-transcribe to POST /v1/audio/transcriptions for recorded files. Use languages (a list), not language, and never send both.
  2. Keep each upload at 25 MB or less. Split longer audio between sentences and pass the previous chunk's text in prompt.
  3. For speaker labels send gpt-4o-transcribe-diarize with response_format=diarized_json and chunking_strategy=auto for audio over 30 seconds. Plan for its shutdown on 26 February 2027.
  4. Word timestamps, srt and vtt need whisper-1, which cannot stream and shuts down on 26 February 2027. Move English translation to v1/realtime/translations before then.
  5. On 429 or 503 wait at least Retry-After when present, then back off with jitter. Do not retry credit_balance_exhausted or spend-limit errors.

Questions

Which is better for AI agents, Giggy Speech to Text or OpenAI Speech to Text?

OpenAI Speech to Text scores 75 (BB) on agent readiness against Giggy Speech to Text's 54.5 (C), and leads in 6 of 7 scored categories.

Do Giggy Speech to Text and OpenAI Speech to Text need an API key?

Both need an API key.

Can an agent call Giggy Speech to Text and OpenAI Speech to Text without installing anything?

Yes. Giggy Speech to Text has a hosted endpoint at https://api.giggy.ai/v1 and OpenAI Speech to Text at https://api.openai.com/v1.

Other comparisons with Giggy Speech to Text or OpenAI Speech to Text

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