GPT-4o Transcribe Diarize vs Seed ASR
Which one, when — curated verdict, not a benchmark table
These bill in different units — gpt-4o-transcribe-diarize at $6.25 per million audio input tokens plus $2.5 per million text tokens, seed-asr-bigmodel at a flat $0.002 per audio minute — so no single conversion between them is honest. Both diarize: seed-asr-bigmodel takes files up to 5 hours and 512MB with word-level timestamps, against a 25MB cap and a required chunking_strategy above 30 seconds on the OpenAI side. Pick gpt-4o-transcribe-diarize for diarized_json inside an OpenAI-shaped pipeline, seed-asr-bigmodel for long recordings billed by the minute.
Pricing
| GPT-4o Transcribe Diarize | Seed ASR | Δ | |
|---|---|---|---|
| Per audio minute | — | $0.002 | — |
| Audio input / 1M tokens | $6.25 | — | — |
| Text output / 1M tokens | $2.5 | — | — |
These two models bill in different units, so no Δ is shown — converting between them would require an assumption we have not measured. Each card is listed in its own unit above.
Capabilities
| GPT-4o Transcribe Diarize | Seed ASR | |
|---|---|---|
| Speaker diarization | yes | yes |
| Streaming | yes | yes |
| Timestamps | yes | yes |
Specs
| GPT-4o Transcribe Diarize | Seed ASR | |
|---|---|---|
| Input modalities | text audio | audio |
| Output modalities | text | text |
| Released | 2025-10 | — |
| Knowledge cutoff | 2024-06 | — |
| Limits | mp3/mp4/mpeg/mpga/m4a/wav/webm, up to 25MB built-in speaker diarization with diarized_json output (speaker labels + segment timestamps) chunking_strategy required for audio >30s no prompt support | Async audio-file mode: <512MB, <5 hours, OPUS/WAV/MP3/SPX/OGG/AMR/AAC/M4A (raw PCM also accepted), results returned within 3 hours and retained 7 days speaker diarization via enable_speaker_info (audio-file API only, best with <=10 speakers, no diarization on the streaming API) sentence + word segmentation with start_time/end_time via show_utterances language identification via enable_lid hotwords/context up to 800 tokens and 20 rounds per-call billing |
| Languages | 57 languages listed for the transcriptions endpoint (one shared list for all transcription models) ISO 639-1 / 639-3 codes accepted for GPT-4o-based models | With `language` empty the model covers Mandarin, English, Cantonese, Shanghainese, Minnan, Sichuan and Shaanxi dialects 39 language keys can be pinned explicitly (en-US, zh-CN, yue-CN, ja-JP, ko-KR, id-ID, es-MX, pt-BR, de-DE, fr-FR, fil-PH, ms-MY, th-TH, ar-SA, it-IT, bn-BD, el-GR, nl-NL, ru-RU, tr-TR, vi-VN, pl-PL, ro-RO, uk-UA, az-AZ, bg-BG, cs-CZ, da-DK, fi-FI, hi-IN, hu-HU, kk-KZ, km-KH, my-MM, no-NO, pa-PK, sv-SE, sw-KE, ur-PK), plus optional auto-detection (enable_auto_lang) |
| Max output | 2K | — |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: GPT-4o Transcribe Diarize · Seed ASR
Switch between them with one line
Both ids are in every tab below — the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.audio.transcriptions.create(
model="gpt-4o-transcribe-diarize",
# model="seed-asr-bigmodel", # uncomment this line, comment the one above
file=open("meeting.mp3", "rb"),
language="en",
)
print(resp.text)import OpenAI from "openai";
import fs from "node:fs";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.audio.transcriptions.create({
model: "gpt-4o-transcribe-diarize",
// model: "seed-asr-bigmodel", // uncomment this line, comment the one above
file: fs.createReadStream("meeting.mp3"),
});
console.log(resp.text);curl https://synthorai.io/v1/audio/transcriptions \
-H "Authorization: Bearer sk-syn-..." \
-F model="gpt-4o-transcribe-diarize" \
# -F model="seed-asr-bigmodel" \ # uncomment this line, comment the one above
-F file=@meeting.mp3package main
import (
"context"
"fmt"
"os"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
f, _ := os.Open("meeting.mp3")
resp, _ := client.Audio.Transcriptions.New(context.TODO(), openai.AudioTranscriptionNewParams{
Model: "gpt-4o-transcribe-diarize",
// Model: "seed-asr-bigmodel", // uncomment this line, comment the one above
File: f,
})
fmt.Println(resp.Text)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.audio.transcriptions.*;
import java.nio.file.Paths;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
Transcription resp = client.audio().transcriptions().create(
TranscriptionCreateParams.builder()
.model("gpt-4o-transcribe-diarize")
// .model("seed-asr-bigmodel") // uncomment this line, comment the one above
.file(Paths.get("meeting.mp3"))
.build()).asTranscription();
System.out.println(resp.text());FAQ
Which is cheaper, GPT-4o Transcribe Diarize or Seed ASR?
They bill in different units, so there is no single honest number: GPT-4o Transcribe Diarize and Seed ASR each appear in their own unit in the table above. Compare them on your own workload — the practical trade-off is described in the verdict at the top of this page.
Can I A/B test GPT-4o Transcribe Diarize against Seed ASR without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key — switching is a one-line model-string change, so you can route a fraction of traffic to each and compare bills directly.
Do GPT-4o Transcribe Diarize and Seed ASR support speaker diarization?
The capability table above answers this per model, straight from each vendor’s documentation — diarization, streaming and timestamps are listed separately because models differ on all three.