Fun-ASR Flash vs GPT-4o Transcribe Diarize
Which one, when — curated verdict, not a benchmark table
Both are transcription-only models, but they bill in different units — fun-asr-flash charges $0.0021 per audio minute while gpt-4o-transcribe-diarize charges $6.25 per million audio input tokens, so no single conversion between them is honest. Pick fun-asr-flash for straightforward synchronous jobs under 5 minutes or 2GB, across 30+ languages, with domain-term context injection but no diarization. Pick gpt-4o-transcribe-diarize when you need built-in speaker diarization with diarized_json speaker labels and segment timestamps, accepting its 25MB file cap, 16000-token context, and required chunking_strategy for audio over 30s.
Pricing
| Fun-ASR Flash | GPT-4o Transcribe Diarize | Δ | |
|---|---|---|---|
| Per audio minute | $0.0021 | — | — |
| 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
| Fun-ASR Flash | GPT-4o Transcribe Diarize | |
|---|---|---|
| Speaker diarization | no | yes |
| Streaming | yes | yes |
| Timestamps | — | yes |
Specs
| Fun-ASR Flash | GPT-4o Transcribe Diarize | |
|---|---|---|
| Input modalities | audio | text audio |
| Output modalities | text | text |
| Released | 2026-06 | 2025-10 |
| Knowledge cutoff | — | 2024-06 |
| Limits | Synchronous fast recognition, <=5 min / <=2GB per audio context injection for domain terms 30+ languages no diarization | 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 |
| Languages | Multilingual with dialects, the same 30-language list as the Fun-ASR main versions: Chinese (Mandarin, Cantonese, Wu, Hokkien, Hakka, Gan, Xiang, Jin plus regional accents), English, Japanese, Korean, Vietnamese, Thai, Indonesian, Malay, Filipino, Hindi, Arabic, French, German, Spanish, Portuguese, Russian, Italian, Dutch, Swedish, Danish, Finnish, Norwegian, Greek, Polish, Czech, Hungarian, Romanian, Bulgarian, Croatian, Slovak | 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 |
| 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: Fun-ASR Flash · GPT-4o Transcribe Diarize
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="fun-asr-flash",
# model="gpt-4o-transcribe-diarize", # 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: "fun-asr-flash",
// model: "gpt-4o-transcribe-diarize", // 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="fun-asr-flash" \
# -F model="gpt-4o-transcribe-diarize" \ # 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: "fun-asr-flash",
// Model: "gpt-4o-transcribe-diarize", // 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("fun-asr-flash")
// .model("gpt-4o-transcribe-diarize") // uncomment this line, comment the one above
.file(Paths.get("meeting.mp3"))
.build()).asTranscription();
System.out.println(resp.text());FAQ
Which is cheaper, Fun-ASR Flash or GPT-4o Transcribe Diarize?
They bill in different units, so there is no single honest number: Fun-ASR Flash and GPT-4o Transcribe Diarize 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 Fun-ASR Flash against GPT-4o Transcribe Diarize 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 Fun-ASR Flash and GPT-4o Transcribe Diarize 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.