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Choose a speech to text API by the audio workload: live conversation, uploaded recordings, multilingual content or transcription with downstream analysis. Deepgram, AssemblyAI, Google Cloud and OpenAI are useful candidates to investigate, but no provider's headline accuracy or price settles the decision for your audio.
Official documentation and pricing were checked on 12 November 2026. Theaimart did not run comparative accuracy or latency tests. This guide is for developers; the separate transcription apps article covers software a person can use without an integration.
Developer shortlist
| Provider | Why investigate it | Integration question | Cost dimension |
|---|---|---|---|
| Deepgram | Streaming and recorded speech workflows | Which model and endpoint fit the language and conversational timing? | Model, audio mode and optional features |
| AssemblyAI | Recorded/real-time transcription and speech understanding | Are required annotations available in the selected endpoint? | Model, transcription mode and added analysis |
| Google Cloud Speech-to-Text | A speech service within Google Cloud operations | Version, region, channels and recognition mode | Audio duration, processing mode and volume tier |
| OpenAI transcription | File transcription alongside an existing OpenAI integration | Model-specific input, output and timing support | Selected model and its documented billing basis |
Evaluate account eligibility, retention and supported regions separately from this shortlist. A language appearing in documentation does not establish accuracy for code-switching, regional names or a noisy microphone.
Normalize pricing before comparing quotes
Google's reviewed V2 standard-recognition listing starts at $0.016 per minute for the first stated volume tier. The exact mode matters: dynamic batch and other configurations have different terms. Google's page also explains that an empty successful response can still be billable.
OpenAI's pricing page lists estimated costs of $0.003/minute for gpt-4o-mini-transcribe and $0.006/minute for gpt-4o-transcribe. These named examples are not a complete current model ranking. Check model availability and output requirements before selecting by rate.
At those listed rates, 10,000 minutes would be $30, $60 or $160 for those three particular configurations, before extras or different billing conditions. This is a calculation, not a matched quality comparison. It does not account for retries, multiple channels, storage, networking or review.
Measure accuracy on representative audio
Build a permission-cleared evaluation set containing the languages, microphone types, noise and terminology your application encounters. Prepare reference transcripts independently. Include silent segments and speech without a clear answer so hallucinated text is observable.
Calculate word error rate as substitutions plus deletions plus insertions divided by reference words. Keep entity errors, speaker attribution and timestamp alignment as separate measures. A model can score well on common words while failing the account numbers or names your workflow needs.
Apply the same text-normalization rules to every provider. Document whether you ignore punctuation, number formatting or case. Comparing vendor-reported percentages from different datasets is not a valid experiment.
Streaming latency has several definitions
Measure time to the first interim text, time from speech to a stable final segment and full end-of-session completion separately. An early partial result may change substantially, while a slower final segment may be easier for downstream software to use.
Record the client region, audio chunk size, connection setup and whether the clock includes network transit. Compare percentiles and failures, not only averages. A low average can hide occasional long delays that break a conversational experience.
Do not feed every interim transcript into a consequential tool action. Define when a segment is final enough for your use case, and handle later corrections. Conversation interruption and endpointing need their own acceptance tests.
A production integration pattern
Upload files to controlled storage or stream from an authenticated client through the intended trust boundary. Keep provider keys on the server. Use a job identifier for asynchronous work and persist processing state before accepting callbacks.
Authenticate callbacks according to the provider's current method. Treat repeated notifications as normal possibilities and make processing idempotent. Validate file type, size and duration before starting billable work; do not accept arbitrary user-supplied URLs as unrestricted server fetch targets.
Separate retryable transport failures from unsupported input and account restrictions. Track attempted audio, successful transcripts, accepted transcripts and charged usage. If an uncertain request might be charged twice, reconcile before resubmitting blindly.
Budget for usable output and data handling
Use cost per accepted audio hour, including corrections and failed attempts. A cheaper raw minute can be less economical if specialized vocabulary needs heavy review. Test quality before making a financial projection.
Check retention, model-training terms, deletion, subprocessors and regional processing for the exact service and account. Redaction is a separate feature to evaluate; it is not safe to assume the unredacted source never existed in the provider's processing path.
For routing and compatibility concepts, see OpenAI-compatible API boundaries. A shared request shape does not establish identical speech behavior. Theaimart's developer resources are relevant discovery resources, not a claim that its APIs currently provide every speech endpoint discussed here.
Frequently asked questions
Which API has the lowest latency?
Measure the specific endpoint, region and definition of finality your application needs. This article reports no comparative timing results.
Can I use one model for every language?
Only after evaluation supports the required language and audio conditions. Keep fallback behavior explicit.
Should I use batch processing?
Investigate it when turnaround allows. Confirm the actual pricing and completion conditions instead of assuming a batch discount applies to real-time work.