GPT-4o Transcribe Diarize vs Seed ASR
Which one, when
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 rate 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 | 2025-12-05 |
| 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 change to the model id, 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.