Groq vs OpenAI Whisper: Real Benchmarks for Voice Transcription (2026)

I've been building dictate.app — a Windows dictation tool — and the biggest decision early on was which Whisper API to use. I ran both Groq and OpenAI through real-world testing. Here's what the numbers actually look like.

Why Whisper APIs, Not Local Models

Local Whisper (running on your machine) is free but slow unless you have a GPU. For a dictation tool where latency is everything, you want a hosted API. The two main options in 2026 are OpenAI's Whisper endpoint and Groq's Whisper endpoint.

Both run the same underlying model family (Whisper large-v3). The difference is infrastructure.

Latency: The Real-World Numbers

I tested with audio clips of varying lengths — 5 seconds, 15 seconds, 30 seconds, and 60 seconds — and measured round-trip time from sending the request to receiving the transcription.

Clip Length	Groq	OpenAI
5 seconds	~180ms	~750ms
15 seconds	~210ms	~820ms
30 seconds	~260ms	~1100ms
60 seconds	~380ms	~1800ms

Groq is consistently 4-5x faster. For a dictation app, this is the difference between feeling instant and feeling like you're waiting.

The latency gap comes from Groq's LPU (Language Processing Unit) hardware. These chips are purpose-built for inference and deliver dramatically lower time-to-first-token compared to GPU clusters.

How to Call Each API

Groq Whisper

const Groq = require("groq-sdk");
const fs = require("fs");

const groq = new Groq({ apiKey: process.env.GROQ_API_KEY });

async function transcribeWithGroq(audioFilePath) {
  const start = Date.now();

  const transcription = await groq.audio.transcriptions.create({
    file: fs.createReadStream(audioFilePath),
    model: "whisper-large-v3",
    language: "en",
    response_format: "json",
  });

  console.log(`Groq latency: ${Date.now() - start}ms`);
  return transcription.text;
}

OpenAI Whisper

const OpenAI = require("openai");
const fs = require("fs");

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

async function transcribeWithOpenAI(audioFilePath) {
  const start = Date.now();

  const transcription = await openai.audio.transcriptions.create({
    file: fs.createReadStream(audioFilePath),
    model: "whisper-1",
    language: "en",
    response_format: "json",
  });

  console.log(`OpenAI latency: ${Date.now() - start}ms`);
  return transcription.text;
}

The API shapes are nearly identical — switching between them is about 3 lines of code.

Cost Comparison

This is where Groq wins by a landslide.

Groq Whisper pricing: $0.02 per hour of audio
OpenAI Whisper pricing: $0.006 per minute = $0.36 per hour of audio

That's an 18x cost difference for the same model.

For a power user dictating 2 hours a day:

• Groq: $0.04/day, $1.20/month
• OpenAI: $0.72/day, $21.60/month

For a SaaS app with 1,000 users each dictating 30 minutes a day:

• Groq: ~$300/month
• OpenAI: ~$5,400/month

Unless you're already deeply locked into the OpenAI ecosystem, the cost math is hard to ignore.

Accuracy Comparison

This is where things get more nuanced. Both APIs run Whisper large-v3, so accuracy should be similar in theory. In practice, I noticed differences on:

Technical Terms and Proper Nouns

I tested dictating content with technical vocabulary — programming terms, product names, developer jargon.

• Groq: Occasionally struggles with very niche technical terms, especially compound words and camelCase concepts spoken aloud.
• OpenAI: Marginally better on highly technical vocabulary, likely due to fine-tuning or post-processing on their side.

For everyday English, both are excellent. For dictating code-heavy content, the gap is real but small.

Punctuation and Formatting

Neither API auto-inserts punctuation without prompting. You need to say "period", "comma", etc. or post-process with an LLM. This is the same for both.

Noise Handling

Both handle moderate background noise well. Neither is great with significant ambient noise — you'll want to denoise before sending if your recording environment is rough.

The streaming question

Neither Groq nor OpenAI Whisper supports true streaming transcription through these REST APIs. You send a complete audio file, wait, get text back. For a dictation tool, this means you need to chunk your audio:

// Record in chunks, transcribe each chunk
const CHUNK_DURATION_MS = 5000; // 5-second chunks

function startChunkedDictation(onTranscript) {
  let currentChunk = [];

  recorder.on("data", (data) => {
    currentChunk.push(data);
  });

  setInterval(async () => {
    if (currentChunk.length === 0) return;
    const chunk = currentChunk.splice(0);
    const audioBuffer = Buffer.concat(chunk);

    const text = await transcribeWithGroq(audioBuffer);
    onTranscript(text);
  }, CHUNK_DURATION_MS);
}

With Groq's ~200ms latency, a 5-second chunk transcribes in ~200ms after the chunk ends — giving you text about 5.2 seconds behind real-time. With OpenAI's ~800ms latency, that's 5.8 seconds. Not a huge difference at this chunk size, but if you shorten chunks to 2-3 seconds for lower latency, the difference grows.

My Recommendation

For most dictation and voice-to-text use cases in 2026: use Groq.

• 4-5x lower latency
• 18x lower cost
• Accuracy is equivalent for 95% of use cases
• API is near-identical to OpenAI's — easy to switch

The only reason to choose OpenAI Whisper:

• You're already paying for an OpenAI subscription and usage is low
• Your use case involves heavy technical jargon where that marginal accuracy edge matters
• You need OpenAI's ecosystem integrations (Assistants API, etc.)

dictate.app uses Groq as the primary transcription backend with OpenAI as a fallback. In production, we've seen Groq handle over 95% of requests with no issues.

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Benchmarks run in April 2026 from a US-East server. Latency figures are median across 50 requests per category. Your numbers may vary based on geography and API load.