Speech-to-Text APIs
Gladia vs Deepgram for SIP/8kHz audio — which one is more accurate on phone calls?
7 min read
Quick Answer: For real-world SIP/8 kHz phone calls, Gladia is typically more accurate than Deepgram, especially on noisy, conversational audio with multiple speakers and accents, thanks to lower error rates on open benchmarks and explicit optimization for telephony pipelines.
Most voice products don’t fail because the LLM is bad. They fail earlier—at the transcription layer—when your STT can’t reliably parse a low-bitrate, 8 kHz phone call. Wrong names, broken numbers, and misattributed speakers silently corrupt every downstream workflow: notes, summaries, CRM syncs, QA scoring, even payments.
If your stack runs on SIP or any telephony platform, choosing between Gladia and Deepgram comes down to one core question: which engine holds up better on real call audio, not clean podcast demos?
Frequently Asked Questions
Is Gladia more accurate than Deepgram on SIP/8kHz phone call audio?
Short Answer: Yes. On open benchmarks that mirror real conversational speech, Gladia achieves significantly lower word error rates than Deepgram, which translates into more reliable transcripts for 8 kHz telephony audio.
Expanded Explanation:
While Deepgram is a well-known STT provider, Gladia’s Solaria models are explicitly tuned and evaluated on conversational, noisy, multi-speaker scenarios—the same conditions you see on SIP/8 kHz calls. In Gladia’s open benchmark across seven datasets and 500+ hours of real-world audio, Gladia delivers up to 45% lower word error rate (WER) than competing APIs on conversational speech. Deepgram v3 appears in that comparison with materially higher WER than Gladia.
For phone calls, this accuracy gap is not theoretical. On telephony audio, every misheard email, name, address, or amount propagates into broken workflows: wrong CRM records, failed verifications, invalid orders. A lower WER on conversational speech is a strong proxy for better performance on typical call-center and voice-agent traffic, especially when combined with robust diarization and entity handling.
Key Takeaways:
- Gladia’s benchmarked WER on conversational speech is significantly lower than Deepgram v3.
- Better baseline accuracy directly reduces downstream failures in call-driven workflows (notes, summaries, CRM syncs).
How do I evaluate Gladia vs Deepgram for my own SIP/8kHz call traffic?
Short Answer: Run a side-by-side evaluation on your real call recordings, using the same 8 kHz source audio, and compare WER, diarization error rate (DER), entity accuracy, and latency.
Expanded Explanation:
Benchmarks are helpful, but phone systems are messy and unique: SIP carriers, codecs, gain levels, background noise, and accents all vary. The only credible way to choose between Gladia and Deepgram for 8 kHz telephony is to test them on your own call traffic with a reproducible methodology.
You’ll want to fix the inputs (same audio), control the configuration (same sample rate, no upsampling tricks), and score both providers against a human reference transcript. Go beyond raw WER: check speaker attribution, numbers, emails, and domain-specific vocabulary. That’s where STT errors become expensive.
Steps:
-
Collect representative calls
Sample real SIP/8 kHz recordings: inbound support calls, outbound sales calls, IVR transfers, mixed languages, noisy environments, and crosstalk. -
Set up both APIs identically
Integrate Gladia and Deepgram via REST or WebSocket, feed the exact same audio (8 kHz source), and enable diarization where relevant. -
Score and compare
Create human reference transcripts and compute WER/DER, then manually spot-check entities (names, amounts, emails, addresses) and compare latency and stability across at least a few hundred calls.
How does Gladia’s accuracy on phone calls compare to Deepgram in benchmarks?
Short Answer: In published benchmarks on real-world conversational audio, Gladia shows substantially lower WER and up to 3× lower diarization error rates than competing providers like Deepgram v3.
Expanded Explanation:
Gladia publishes an open benchmark across seven datasets and 500+ hours of audio—covering customer calls, meetings, broadcast, web video, field recordings, court, clinical, and restaurant noise. This mix is intentionally closer to what you see in production telephony and contact center environments than in “clean demo” datasets.
On conversational speech, Gladia achieves up to 45% lower WER than competing APIs. In the diarization benchmark (who-spoke-when), Gladia reports diarization error rates up to 3× lower than other providers, including Deepgram v3. For phone calls with multiple speakers (agent + customer, warm transfers, supervisor joins), diarization quality directly impacts analytics and compliance—misattributed speakers mean wrong QA scores, incorrect coaching, and confusing notes.
Comparison Snapshot:
- Option A: Gladia
- Up to 45% lower WER on conversational speech vs other APIs
- Up to 3× lower diarization error vs competitors in multi-domain benchmarks
- Designed for noisy, accented, real-world speech (including telephony-like audio)
- Option B: Deepgram
- Solid STT performance, but higher WER/DER vs Gladia in the referenced open benchmarks
- Less transparency around multi-dataset evaluation on noisy call/meeting data
- Best for:
- If phone-call accuracy and diarization stability are critical to your product, Gladia is generally the safer choice based on current benchmark data.
How do I implement Gladia for SIP/8kHz phone calls in my stack?
Short Answer: You connect your telephony platform (e.g., Twilio, Vonage, Telnyx, Vapi, LiveKit, custom SIP stack) to Gladia over REST or WebSocket, stream 8 kHz audio, and consume real-time or batch transcripts with diarization and word-level timestamps from a single API.
Expanded Explanation:
Gladia is built as a speech backbone, not a standalone app. For SIP/8 kHz traffic, you typically bridge your media server or telephony provider to Gladia’s real-time WebSocket or asynchronous REST endpoint. The same API surface covers transcription, diarization, word-level timestamps, translation, and add-ons like NER, sentiment, and summarization.
Gladia’s engine is optimized for telephony protocols (8 kHz, SIP) and supports sub-300 ms latency for streaming use cases, with partial transcripts often in under 100 ms. That’s fast enough for live agent assist, real-time voice agents, and in-call analytics, without introducing awkward conversational lag.
What You Need:
- A telephony/voice layer that can forward raw audio
SIP/RTP streams via media server, Twilio/Vonage/Telnyx, or a programmable voice platform like Vapi, Pipecat, or LiveKit. - A Gladia integration over REST or WebSocket
Use the lightweight SDK or direct API calls to send 8 kHz audio and receive transcripts, diarization, and timestamps, then feed that into your notes, summaries, CRM enrichment, or QA systems.
Strategically, when does Gladia make more sense than Deepgram for phone-centric products?
Short Answer: If your product depends on reliable information extraction from phone calls—notes, summaries, CRM sync, QA, or voice agents—Gladia is usually the more defensible choice because its accuracy and diarization stability reduce downstream failures and operational noise.
Expanded Explanation:
In phone-centric products (contact centers, sales engagement platforms, AI note-takers, voice agents), STT is infrastructure. When the transcript is wrong, everything on top breaks: wrong follow-up tasks, incorrect CRM objects, failed automations, and frustrated users who lose trust in the product.
Gladia’s value for SIP/8 kHz audio isn’t just lower WER in a benchmark slide. It’s what that accuracy buys you in production:
- Fewer hallucinated or missing entities mean cleaner CRM data and more reliable analytics.
- Better diarization means coaching, QA, and compliance reports actually reflect who said what.
- Stable latency and variability mean you can design predictable in-call experiences without buffering workarounds.
Deepgram can work for many scenarios, but if your risk profile is tied to call accuracy and you operate at scale, the combination of Gladia’s open, reproducible benchmarks and telephony-focused optimization gives you more confidence in long-term stability.
Why It Matters:
- Impact on automation: More accurate call transcripts drive better intent detection, fewer NLU fallbacks, and safer “hands-off” automation (e.g., post-call summaries, ticket routing, CRM updates).
- Impact on trust: When your product consistently gets names, numbers, and speakers right—even with noise, accents, and crosstalk—users stop double-checking the transcript and start relying on your system.
Quick Recap
For SIP/8 kHz and broader telephony audio, the critical question is not “Who has the nicer demo?” but “Whose engine breaks less often on real calls?” On open, multi-dataset benchmarks, Gladia achieves significantly lower WER and up to 3× lower diarization error than competing providers like Deepgram v3. In practice, that means fewer broken notes, cleaner CRM syncs, and more reliable automation for call-heavy products. The best way to decide for your stack is to run a side-by-side evaluation on your own call recordings and measure WER, DER, entity accuracy, and latency.