TL; DR: OpenAI's Whisper API is a capable async transcription service built on one of the most influential open-source models in speech recognition, but it caps file uploads at 25MB, has no native real-time streaming on the standard whisper-1 endpoint, and does not include diarization, NER, or custom vocabulary in the base price. Gladia's Solaria-1 model delivers partial transcripts in under 103ms via WebSocket, covers 100 languages including 42 not available on any other API, and bundles every audio intelligence feature at $0.61/hr (Starter) or as low as $0.20/hr (Growth) for async transcription, and $0.75/hr (Starter) or $0.25/hr (Growth) for real-time streaming . If your product serves multilingual users or requires accurate transcription Gladia's API provides code-switching detection that Whisper API does not offer.
Vanilla Whisper models hallucinate on audio segments with silence or low-signal content. In a contact center processing thousands of calls per day, that translates directly to fabricated transcripts and downstream errors in sentiment analysis, entity extraction, and compliance logs. Evaluating STT APIs requires looking past English accuracy benchmarks to the engineering overhead and cost predictability your infrastructure decision creates at scale.
OpenAI's Whisper changed what developers expected from speech recognition when it launched as open-source in 2022, and the managed API it powers remains a credible choice for batch English transcription. Turning a foundational model into production infrastructure for a global voice product requires more than transcription, though. This comparison breaks down the technical differences between the Whisper API and Gladia across latency, multilingual accuracy, custom vocabulary, and unit economics so you can make an evidence-backed infrastructure decision.
The architectural differences between Whisper and Gladia
OpenAI Whisper's transformer model and training scale
Whisper is a transformer-based encoder-decoder model trained by OpenAI on 680,000 hours of training data, extended in later versions to include weakly labeled and pseudo-labeled audio that reduced errors by 10-20% compared to prior releases.
The training scale is substantial, but the architecture has specific production limitations:
- Hallucination on low-signal audio: Whisper generates plausible-sounding text even when there is nothing to transcribe, a known artifact of its training data, which includes YouTube transcripts that frequently contain phrases like "Thank you for watching." This is documented behavior, not an edge case.
- English data dominance: 65% of Whisper's training data targets English speech recognition, while only 17% covers multilingual recognition. This imbalance directly affects WER in non-English production environments.
- Batch-only design: The standard OpenAI Whisper API processes pre-recorded files up to 25MB and returns a complete transcript when processing finishes. Streaming is not supported on the whisper-1 endpoint.
- Context window limits: When using prompt-based custom vocabulary, the model considers only the final 224 tokens of any prompt and discards earlier context, which limits domain-specific terminology injection for large vocabularies.
Note that the OpenAI API exposes the model as whisper-1 and does not offer version-specific model selection. Which underlying release powers whisper-1 internally has not been officially confirmed by OpenAI.
How we optimized production performance with Solaria-1
We built Solaria-1 on Whisper's foundation using a hybrid ML ensemble architecture that applies additional models at each transcription stage rather than replacing the base model entirely. The architecture removes up to 99% of hallucinations compared to vanilla Whisper while achieving materially lower WER than Whisper large-v2 and v3 on the same audio. Each processing step runs through additional AI models that validate or suppress output before it reaches the final transcript.
Solaria-1 extends this architecture to support both async and real-time workloads, with the strongest performance in batch transcription where full context improves accuracy and structure. According to Gladia’s latest open benchmark, Solaria was evaluated against 8 leading STT providers across 7 datasets and more than 74 hours of audio, using identical production API settings and an open, reproducible methodology. On conversational speech, Gladia reports up to 29% lower word error rate than competing APIs, while speaker diarization achieves up to 3x lower diarization error rate than alternative vendors. In real-time use cases, Gladia also reports partial transcript latency under 100 ms and final latency below 300 ms.
Here is how to initiate an async transcription request with custom vocabulary using the Gladia REST API (source: Gladia API reference):
import requests
url = "<https://api.gladia.io/v2/pre-recorded/>"
headers = {
"x-gladia-key": "YOUR_GLADIA_API_KEY",
"Content-Type": "application/json"
}
payload = {
"audio_url": "<https://your-storage.com/call-recording.wav>",
"diarization": True,
"custom_vocabulary": ["Solaria", "FLEURS", "pyannoteAI"],
"sentiment_analysis": True,
"named_entity_recognition": True
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
Every feature in that payload, including diarization, sentiment analysis, and NER, is included at the base rate with no additional per-feature charges.
Build vs. buy: The hidden costs of self-hosting Whisper
Self-hosting Whisper looks free until you model the GPU provisioning, engineering maintenance, and unpredictable latency costs that appear at production scale.
Whisper large-v3 is approximately 3GB in model size, but 6GB of VRAM per worker is required because PyTorch allocates additional memory for CUDA context and computation buffers. A single NVIDIA A100 GPU costs $10,000-$12,000, and handling concurrent requests at production scale typically requires multiple units.
Beyond hardware, the operational costs accumulate:
- Engineering maintenance: Model updates, infrastructure scaling, and GPU provisioning require dedicated effort. A documented infrastructure analysis from OpenMetal estimates staffing at $240,000-$480,000 per year for 3-6 engineers.
- Unpredictable latency: Multi-tenant cloud GPU environments time-slice usage among customers, introducing variance that breaks real-time latency budgets.
- No built-in audio intelligence: Self-hosted Whisper delivers transcription only. Diarization, sentiment analysis, entity extraction, and custom vocabulary all require separate implementations.
A community-built infrastructure cost analysis found that self-hosting Whisper cost $163,680 per year in infrastructure alone, excluding developer and admin overhead, while a comparable managed API cost $38,880 for the same workload. That gap is engineering sprint capacity that builds product features rather than keeping a speech pipeline running.
Aircall cut transcription time by 95% after moving off a self-hosted solution, freeing engineering capacity for product work rather than infrastructure maintenance.
Head-to-head comparison: Whisper API vs. Gladia
| Feature |
Whisper API |
Gladia (Solaria-1) |
Business impact |
| Accuracy benchmark |
WER on Common Voice 15, FLEURS |
WER on Common Voice, FLEURS (multi-version) |
Comparable transparency |
| Real-time streaming |
Not on whisper-1 (async only) |
WebSocket, 103ms partial, 270ms final |
Required for voice agents |
| File size limit |
25MB |
1,000MB, 135 min |
Chunking required for Whisper |
| Custom vocabulary |
Prompt-based, 224 token limit |
Native injection, no cap |
Large vocabularies supported |
| Languages supported |
99 |
100 (42 exclusive) |
Tagalog, Bengali, Punjabi+ |
| Code-switching |
Not native |
Native, all 100 languages |
Multilingual call support |
| Diarization |
Not included |
Included (pyannoteAI) |
No third-party integration |
| Pricing |
$0.36/hr transcription only |
$0.61/hr async, $0.75/hr real-time. Growth: from $0.20/hr async, $0.25/hr real-time. All features included |
Lower TCO at scale |
| Data retraining default |
Governed by separate agreement |
Paid tiers: never. Free tier: audio used for model training |
Explicit default posture |
| Compliance |
Via OpenAI enterprise agreements |
SOC 2, ISO 27001, GDPR certified |
Both meet requirements |
Real-time streaming capabilities and latency budgets
The standard OpenAI Whisper API processes pre-recorded files asynchronously. OpenAI does offer a separate Realtime API with WebSocket and WebRTC connections, but this is a distinct product from whisper-1 with separate pricing and integration requirements. For teams evaluating the Whisper API for batch transcription, streaming is not an included capability.
We support real-time streaming for latency-sensitive use cases like voice agents and live captioning, while most production workflows, including meeting assistants and call center analytics, rely on async transcription for higher accuracy and stability. Solaria-1 partial transcript latency is under 103ms, with final transcripts arriving in approximately 270ms, as documented on our benchmarks page. For LLM pipelines where STT output feeds directly into inference, even 200ms of additional latency creates a perceptible lag in conversational AI applications.
The following code initiates a real-time WebSocket session with Gladia (source: Gladia real-time transcription docs):
import asyncio
import websockets
import json
async def stream_transcription():
uri = "wss://api.gladia.io/audio/text/audio-transcription"
async with websockets.connect(uri) as websocket:
config = {
"x_gladia_key": "YOUR_GLADIA_API_KEY",
"language_behaviour": "automatic single language",
"diarization": True
}
await websocket.send(json.dumps(config))
async for message in websocket:
result = json.loads(message)
if result.get("type") == "transcript":
print(result["data"]["utterance"]["text"])
asyncio.run(stream_transcription())
Gladia natively supports Twilio's 8-bit 8kHz audio format without conversion, removing a preprocessing step that otherwise adds latency and pipeline complexity. The Gladia real-time streaming pipeline walkthrough demonstrates the streaming pipeline in a live production context, the no-code playground walkthrough shows the same capabilities without code, and the real-time React integration demo covers JavaScript stacks using our TypeScript SDK.
"Gladia provides a highly accurate real-time speech-to-text solution for high volumes of support and service calls. Latency is low and accuracy high, even for numericals. We've appreciated the quality of support across pre-processing, post-processing, and model optimization." - Verified user review of Gladia
Custom vocabulary and handling industry-specific terminology
Domain-specific terminology is where generic models fail in a way users notice immediately. Medical abbreviations, legal citations, B2B SaaS product names, and financial instrument codes all produce higher WER when the model has no prior exposure and no mechanism to weight those terms during inference.
The Whisper API handles custom vocabulary through prompting: you pass a text string at request time and the model uses it as context. The ceiling is a 224-token context window, which limits how many terms you can inject and discards earlier context in longer sessions. This works for casual customization but breaks down for domains with large specialized vocabularies, where you might need to inject hundreds of product names, drug names, or regulatory codes.
We implemented custom vocabulary as a native feature at the model level, not as prompt injection. You pass a list of terms at request time, the model applies weighted recognition throughout the full transcript, and there is no token cap on the vocabulary list. Teams processing calls with hundreds of terms can pass the complete list without truncation risk.
Solaria-1 also includes named entity recognition as part of the base feature set, so company names, product terms, and acronyms are tagged automatically in transcript output without a separate API call. For contact center QA workflows, this structured output eliminates a post-processing step that otherwise requires integration and maintenance.
"Gladia deliver real time highly accurate transcription with minimal latency, even across multiple languages and accents, the API is straightforward and well documented, Making integration into our internal tools quick and easy." - Verified user review of Gladia
Multilingual accuracy and code-switching in production
This is where the English-centric training distribution of vanilla Whisper creates real product risk. With 65% of training data targeting English, non-English languages, particularly low-resource ones, receive substantially less model capacity and show higher WER in production environments.
Solaria-1 covers 100+ languages, including 42 that no other API supports: Tagalog, Bengali, Punjabi, Tamil, Urdu, Persian, Marathi, Haitian Creole, Maori, and Javanese. These languages have direct commercial value in BPO and outsourcing hubs across Southeast Asia, South Asia, and the Caribbean. Accuracy claims on these languages are benchmarked and tested across multiple dataset versions under diverse audio conditions, including noisy environments and accented speech.
Code-switching, where speakers alternate between languages mid-conversation, breaks most APIs silently. Solaria-1 detects language changes automatically across all supported languages and maintains transcript continuity without requiring a language parameter reset, in both real-time and async modes. The automatic language detection documentation covers configuration options for specific language pairs.
The downstream impact matters beyond the transcript itself: sentiment analysis and NER running on a fragmented or incorrectly transcribed multilingual call return degraded signals and require manual review. Accurate code-switching handling at the transcription layer prevents those errors from propagating into your analytics pipeline.
"Excellent multilingual real-time transcription with smooth language switching. Superior accuracy on accented speech compared to competitors. Clean API, easy to integrate and deploy to production." - Verified user review of Gladia
Claap reached 1-3% WER in production, transcribing one hour of video in under 60 seconds, with users praising transcription quality and prospects converting during trials.
Pricing models and unit economics at scale
Both APIs price by audio duration, but the all-in cost profiles diverge sharply once you factor in the features a production workload actually requires.
OpenAI Whisper API charges $0.006 per minute ($0.36/hr) for transcription. Diarization, sentiment analysis, entity extraction, translation, and custom vocabulary beyond the 224-token prompt limit are not included, so adding these features requires integrating dedicated providers such as pyannoteAI for diarization or AssemblyAI for sentiment and NER, each with their own per-unit billing and maintenance overhead.
We charge $0.61/hr (Starter) for async transcription and $0.75/hr (Starter) for real-time streaming. At the Growth tier, async drops to as low as $0.20/hr and real-time to $0.25/hr with an upfront volume commitment. Diarization (pyannoteAI Precision-2), translation, sentiment analysis, NER, summarization, custom vocabulary, and code-switching are all included at every tier.
Here is what the cost model looks like at 1,000 hours of async processing:
| Service |
Whisper API |
Gladia Starter |
Gladia Growth |
| Transcription |
$360 |
$610 |
as low as $200 |
| Speaker diarization |
Third-party add-on |
Included |
Included |
| Named entity recognition |
Third-party add-on |
Included |
Included |
| Sentiment analysis |
Third-party add-on |
Included |
Included |
| Translation |
Third-party add-on |
Included |
Included |
| Custom vocabulary |
Prompt-based, token-limited |
Included, no token cap |
Included, no token cap |
| Total (transcription only for Whisper) |
$360+ without add-ons |
$610 all-in |
$200 all-in |
The headline Whisper rate looks lower, but the moment your pipeline requires diarization and sentiment analysis, the effective cost gap closes and the operational complexity of managing multiple APIs adds engineering overhead that does not appear in either invoice. Per-second billing also removes the rounding tax that accumulates across thousands of calls with variable durations, a difference that compounds at contact center volumes where call lengths vary widely.
"The speed and accuracy of the transcriptions is really solid, especially with challenging audio. I also like how easy the API is to setup, it works nicely without too much fiddling." - Verified user review of Gladia
Data privacy, compliance, and model retraining policies
For any product handling regulated data, your STT vendor's default data posture matters as much as the technical specification.
At Gladia, we do not use customer audio to retrain our models on any paid plan. On the free tier, audio may be used for model training. We are SOC 2 Type 2 certified and GDPR compliant, with EU-west and US-west cloud regions and on-premises or air-gapped deployment options for organizations with strict data residency requirements. See our privacy documentation for the full data handling policy.
OpenAI's privacy policy states that API customers are governed by separate customer agreements and that data submitted through the API is not used for model training unless the user explicitly opts in. The practical difference is where the verification burden sits: with OpenAI, you review the API customer agreement to confirm data handling, while our default is explicit and documented at every tier without requiring contract review.
For teams building on Pipecat, LiveKit, or Vapi, where audio flows from end users through your platform to the STT provider, the default data posture becomes a compliance question your legal team will raise during enterprise procurement. The Gladia x pyannoteAI diarization webinar covers how speaker attribution works within our privacy model for teams that need to understand audio data flow before integrating.
Evaluating the right STT API for your product roadmap
Gladia is built for production workloads that include:
- Async transcription workflows like meeting assistants and contact center analytics. Real-time streaming is also available for voice agents and live captioning with sub-300ms latency requirements
- Non-English language support, particularly for the 42 languages no other API covers
- Code-switching detection in multilingual calls or meetings
- Speaker attribution through diarization (async) in the same pipeline at the same hourly rate
- Predictable unit economics at scale with per-second billing and no add-on fees
The Whisper API works well for English-language batch processing, single-speaker clean audio, and low-volume pipelines where diarization and NER are not required. Its $0.36/hr rate and familiar OpenAI API surface make it fast to prototype, and teams already within the OpenAI ecosystem face less initial integration friction.
Multiple customers report reaching production in under one day of engineering work. The free tier includes 10 hours of processing with all features enabled, which is enough to run your own multilingual audio through the API and evaluate WER, code-switching behavior, and diarization output before committing any engineering sprints.
"Gladia delivers precise speech-to-text transcriptions with reliable timestamps, making it perfect for downstream tasks. It saves time and ensures smooth integration into our workflows." - Verified user review of Gladia
Get started with a pay-as-you-go or subscription package with all features included by default, no setup fees, and no add-ons. Test Gladia for a personalized walkthrough of multilingual accuracy and custom vocabulary configuration for your use case.
FAQs
What is the maximum file size for async transcription on each API?
The OpenAI Whisper API caps uploads at 25MB per file. Gladia accepts files up to 1,000MB and 135 minutes in duration on standard plans, with enterprise plans extending to 255 minutes (4 hours 15 minutes).
Does the Whisper API support real-time streaming?
The standard whisper-1 API is async-only and does not support WebSocket streaming. OpenAI offers a separate Realtime API with WebSocket and WebRTC support, but this is a distinct product with separate pricing and is not part of the standard Whisper API. Gladia's WebSocket endpoint returns partial transcripts in under 103ms and is available on all paid plans.
How does Gladia's WER compare to Whisper on non-English audio?
Solaria-1 achieves a 94% Word Accuracy Rate (6% WER) across English, Spanish, French, German, Italian, Portuguese, Dutch, Polish, and other high-resource languages, benchmarked on Mozilla Common Voice and FLEURS across multiple dataset versions. Whisper's accuracy degrades on low-resource languages due to English data dominance in its training set (65% of training data).
How does Gladia handle code-switching within a single transcription request?
Solaria-1 detects mid-conversation language changes automatically across all 100 supported languages without requiring a language parameter reset between speakers or segments, working in both real-time WebSocket and async REST modes. Whisper does not support automatic code-switching detection.
Key terms
Word Error Rate (WER): A measure of transcription accuracy calculated as the percentage of incorrectly transcribed words compared to a reference transcript, where lower WER means fewer errors and 0% represents a perfect transcript.
Code-switching: The practice of alternating between two or more languages mid-conversation, often occurring mid-sentence in multilingual meetings and contact center calls where speakers are fluent in multiple languages.
Diarization: The process of segmenting an audio stream by speaker identity to determine who spoke when across a multi-speaker recording. Essential for meeting transcripts and contact center QA workflows where multiple speakers overlap or interrupt each other. Gladia’s diarization covers the industry standard pyannoteAI Precision-2 and is included in the base rate across all plans.
Hallucination: In speech recognition, the generation of plausible-sounding text that was never spoken in the original audio, typically triggered by silence, low-signal audio, or training data artifacts. Our Whisper-Zero architecture reduces hallucinations by up to 99% compared to vanilla Whisper by applying a validation ensemble at each processing step.
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