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gemini-3.1-flash-live vs GPT Realtime 2.1

vs

Which one, when — curated verdict, not a benchmark table

Both are realtime audio models billed per million tokens, but gemini-3.1-flash-live is the cheaper lane: $3 audio input versus $32 (about 10.7x less) and $12 audio output versus $64, with text at $0.75/$4.5 against $4/$24 (roughly 5.3x less). It also takes image and video input alongside audio and text, carries a 131072-token context with up to 65536 output tokens, and its Live API sessions cap at 15 minutes audio-only (2 minutes with video) unless extended. Pick gpt-realtime-2.1 if you want OpenAI's audio/text stack with $0.4 cache reads, its 128000-token context and 32000 max output.

Pricing

gemini-3.1-flash-live GPT Realtime 2.1 Δ
Audio input / 1M tokens $3 $32 0.094×
Audio output / 1M tokens $12 $64 0.19×
Audio cache read / 1M tokens $0.4
Text input / 1M tokens $0.75 $4 0.19×
Text output / 1M tokens $4.5 $24 0.19×
Cache write no separate charge

Rates from the live catalog at build time; each model page carries the current card.

Where they sit — price per 1M audio tokens across all 6 realtime speech-to-speech models on this billing unit (log scale)

Capabilities

gemini-3.1-flash-live GPT Realtime 2.1
Thinking control always on
Prompt caching implicit + explicit implicit (automatic)
Cache lifetime not published 5–10m, up to 1h
Minimum cached prefix 4096 tokens 1024 tokens

Specs

gemini-3.1-flash-live GPT Realtime 2.1
Input modalities text image audio video text audio
Output modalities text audio text audio
Released 2026-03-26 2026-07-06
Knowledge cutoff 2024-09
Voices

Any voice from the Gemini text-to-speech voice set

native-audio output models switch languages naturally during a conversation.

Session capabilities
  • Function calling is sequential only — the model will not start responding until the tool response is sent
  • search grounding and thinking supported
  • VAD interruption cancels the in-flight generation
  • no caching, structured outputs, code execution or Batch API
  • Speech-to-speech over WebSocket
  • configurable reasoning effort
  • tool use
  • interruption handling
  • 128K context
Context window 131K 128K

Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: gemini-3.1-flash-live · GPT Realtime 2.1

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.

import asyncio, base64, json, websockets

URL = "wss://synthorai.io/v1/realtime?model=gemini-3.1-flash-live"
# URL = "wss://synthorai.io/v1/realtime?model=gpt-realtime-2.1"  # uncomment this line, comment the one above
# Send ONLY the Authorization header — the beta protocol is retired.
HEADERS = {"Authorization": "Bearer sk-syn-..."}

async def main():
    async with websockets.connect(URL, additional_headers=HEADERS) as ws:
        # 1) configure the speech-to-speech session
        await ws.send(json.dumps({
            "type": "session.update",
            "session": {
                "type": "realtime",
                "output_modalities": ["audio"],
                "audio": {"output": {"voice": "alloy"}},
            },
        }))
        # 2) send input audio (base64 PCM16), then request a spoken reply
        await ws.send(json.dumps({"type": "input_audio_buffer.append", "audio": pcm16_b64}))
        await ws.send(json.dumps({"type": "input_audio_buffer.commit"}))
        await ws.send(json.dumps({"type": "response.create"}))
        # 3) stream the model's audio (and text) back
        async for raw in ws:
            ev = json.loads(raw)
            if ev["type"] == "response.audio.delta":
                play(base64.b64decode(ev["delta"]))   # audio out
            elif ev["type"] == "response.done":
                break

asyncio.run(main())

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FAQ

Which is cheaper, gemini-3.1-flash-live or GPT Realtime 2.1?

gemini-3.1-flash-live is cheaper on audio input / 1m tokens ($3 vs $32, 11× apart). Other rows may point the other way — the table above carries the full card, and real cost depends on your mix.

Can I A/B test gemini-3.1-flash-live against GPT Realtime 2.1 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 gemini-3.1-flash-live and GPT Realtime 2.1 support prompt caching?

Cache-read pricing is listed for only one of the two on our feed; where a rate is missing, the provider does not price cached reads separately.

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