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GPT Realtime 2.1 vs GPT Realtime 2.1 Mini

vs

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

The mini tier is cheaper on every line — $0.6 and $2.4 per million text tokens against $4 and $24, and $10 and $20 per million audio in and out against $32 and $64 — so roughly 3x less on audio and about 7x on text input. Both carry a 128K session context with speech-to-speech, tool use and prompt caching; the full model additionally documents configurable reasoning effort and interruption handling. Pick the mini for volume, the full model when you want to dial reasoning effort per session.

Pricing

GPT Realtime 2.1 GPT Realtime 2.1 Mini Δ
Audio input / 1M tokens $32 $10 3.2×
Audio output / 1M tokens $64 $20 3.2×
Audio cache read / 1M tokens $0.4 $0.3 1.3×
Text input / 1M tokens $4 $0.6 6.7×
Text output / 1M tokens $24 $2.4 10×
Cache write no separate charge 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

GPT Realtime 2.1 GPT Realtime 2.1 Mini
Prompt caching implicit (automatic) implicit (automatic)
Cache lifetime 5–10m, up to 1h 5–10m, up to 1h
Minimum cached prefix 1024 tokens 1024 tokens

Specs

GPT Realtime 2.1 GPT Realtime 2.1 Mini
Input modalities text audio text audio
Output modalities text audio text audio
Released 2026-07-06 2026-07-06
Knowledge cutoff 2024-09 2024-09
Session capabilities
  • Speech-to-speech over WebSocket
  • configurable reasoning effort
  • tool use
  • interruption handling
  • 128K context
  • Speech-to-speech with reasoning & tool use
  • prompt caching
  • 128K context
Context window 128K 128K

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

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=gpt-realtime-2.1"
# URL = "wss://synthorai.io/v1/realtime?model=gpt-realtime-2.1-mini"  # 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, GPT Realtime 2.1 or GPT Realtime 2.1 Mini?

GPT Realtime 2.1 Mini is cheaper on audio input / 1m tokens ($10 vs $32, 3.2× 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 GPT Realtime 2.1 against GPT Realtime 2.1 Mini 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 Realtime 2.1 and GPT Realtime 2.1 Mini support prompt caching?

Yes — both bill cached reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.

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