---
title: "OpenAI vs Deepgram: live STT accuracy and latency, independent benchmark | Openbenchmarks"
description: "OpenAI vs Deepgram speech-to-text, compared on a live rolling 7-day Word Error Rate and Time to Final Segment (TTFS). Independent benchmark data on identical audio. Not vendor claims."
url: https://openbenchmarks.com/speech-to-text-benchmark-by-coval/openai-vs-deepgram
---

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[benchmarks](https://openbenchmarks.com/) / [stt by coval](https://openbenchmarks.com/speech-to-text-benchmark-by-coval) / openai vs deepgram 

speech-to-text head-to-head · data by Coval 

# OpenAI vs Deepgram: Speech-to-Textlatency & accuracy

Measured head-to-head, not marketing: the verdict is **split**: **Deepgram** is faster on median TTFS (90 ms vs 554 ms), while **OpenAI** is more accurate (3.8% WER vs 5.1%). Median TTFS gaps of tens of milliseconds are usually not a pick reason; read p95 TTFS with WER. Independent data, refreshed daily, on identical inputs. Price and whether emails, phones, paths, and IDs survive verbatim are not measured.

\[00\] verdict

## OpenAI or Deepgram: which is better?

Each provider's best model on each measured axis, last 7d. Lower is better on both:

| Axis | OpenAI (best model) | Deepgram (best model) | Measured winner |
| --- | --- | --- | --- |
| **Speed**: median TTFS | gpt-realtime-whisper · **554 ms** | nova-3 · **90 ms** | **Deepgram** |
| **Accuracy**: avg WER | gpt-4o-mini-transcribe · **3.8%** | nova-3 · **5.1%** | **OpenAI** |

Not measured here: **price, hallucination on silence, endpointing, timestamps, diarization, long-form drift, rate limits, and whether emails, phones, paths, and IDs survive verbatim**. Treat those as vendor claims until measured. Full leaderboard context: [the independent speech-to-text benchmark →](https://openbenchmarks.com/speech-to-text-benchmark-by-coval) 

\[01\] every model

## Every OpenAI and Deepgram model, measured

All benchmarked models from both providers on the same pinned dataset, ranked fastest-first (median TTFS), last 7d:

| Model | Provider | TTFS median | TTFS p95 | WER | Samples |
| --- | --- | --- | --- | --- | --- |
| **nova-3** | Deepgram | **90 ms** | 135 ms | 5.1% | 6,381 |
| **nova-2** | Deepgram | **91 ms** | 144 ms | 6.6% | 6,368 |
| **flux-general-en** | Deepgram | **99 ms** | 164 ms | 5.7% | 6,384 |
| **flux-general-multi** | Deepgram | **101 ms** | 164 ms | 7.4% | 6,384 |
| **gpt-realtime-whisper** | OpenAI | **554 ms** | 694 ms | 4.0% | 6,361 |
| **gpt-4o-mini-transcribe** | OpenAI | **636 ms** | 1,094 ms | 3.8% | 6,365 |
| **gpt-4o-transcribe** | OpenAI | **751 ms** | 1,041 ms | 3.8% | 6,365 |

\[02\] source

## Data by Coval

Coval sells voice-agent evaluation infrastructure. Numbers are a rolling 7d aggregate on a pinned dataset under production-realistic conditions. Openbenchmarks mirrors the results with attribution; methodology and runner are open-source.

synced from Coval 2026-10-11 11:51 UTC · [full STT benchmark →](https://openbenchmarks.com/speech-to-text-benchmark-by-coval) · [methodology →](https://github.com/coval-ai/benchmarks) 

\[03\] faq

## OpenAI vs Deepgram: common questions

### Is OpenAI faster than Deepgram for speech-to-text?

On the current 7d window, OpenAI's fastest model (gpt-realtime-whisper) has a median TTFS of 554 ms, vs 90 ms for Deepgram's fastest (nova-3), so Deepgram is faster on measured median latency. Distributions matter too: p95 is 694 ms for OpenAI vs 135 ms for Deepgram.

### Which is more accurate, OpenAI or Deepgram?

By Word Error Rate: OpenAI's best model (gpt-4o-mini-transcribe) averages 3.8%, vs 5.1% for Deepgram's best (nova-3). OpenAI leads on measured accuracy. Lower is better; WER is measured on identical inputs under production-realistic conditions.

### Which should I pick, OpenAI or Deepgram?

The measured verdict is split: Deepgram is faster on median TTFS, OpenAI is more accurate (WER). A few milliseconds of median TTFS is usually not a pick reason; read p95 TTFS and WER together. price, hallucination on silence, endpointing, timestamps, diarization, long-form drift, rate limits, and whether emails, phones, paths, and IDs survive verbatim are not measured here.

### Where does this data come from?

Coval sells voice-agent evaluation infrastructure. Every model runs the same pinned dataset under production-realistic conditions. The table is a rolling 7d aggregate, not a single job; Openbenchmarks mirrors it with attribution. Methodology and runner are open-source (Apache-2.0).