Meta Tech ProviderOpenAI Realtime alternative —
63% cheaper, Hindi-native, OpenAI as a model option
gpt-4o-realtime is exceptional for English voice agents. For Indian languages, INR-billed pricing, DLT compliance and the option to use OpenAI's models from our curated stack (without locking into their realtime audio API), ThinnestAI is the production answer.
The honest cost comparison
| Cost line | OpenAI Realtime | ThinnestAI cascaded (OpenAI as LLM) |
|---|---|---|
| Audio input | $0.06/min | Included in flat rate |
| Audio output | $0.24/min | Included in flat rate |
| LLM tokens | Included in audio price | Included in flat rate |
| Telephony | Billed separately | Included in flat rate |
| All-in cost | $0.30/min (~₹25/min) | From ₹2/min ($0.024), all-inclusive |
For Hindi/Marathi/Tamil where audio output dominates the cost, cascaded (STT → text LLM → TTS) comes in far below realtime audio-in-audio-out on published rates. OpenAI's column is their list price; ours is the flat ₹2/min standard-voice rate, pre-GST, billed in 30-second pulses.
When OpenAI Realtime wins
Don't pretend otherwise. Realtime wins when:
- Lowest possible time-to-first-audio is non-negotiable (Realtime reports 300-500ms; a cascaded pipeline has an extra hop and won't beat that).
- Native audio reasoning matters — the model needs to hear tone, sighs, hesitations (text-cascaded loses this).
- Your callers are English-first.
- You don't need DLT/RBI/DPDPA defaults.
If those describe your use case, Realtime is the right pick. We're not the alternative.
Where OpenAI Realtime falls down for India
1. Hindi pronunciation
Realtime's Hindi sounds Western-accented because audio tokens are English-tuned. An Indic-tuned cascaded pipeline holds honorifics and code-switches correctly.
2. Hinglish code-switching
When a customer says "haan, theek hai, but yeh pricing thoda high lag rahi hai" — Realtime's audio tokens drift accent across languages. Cascaded routing handles each fragment in the right STT/TTS.
3. Cost at scale
$0.30/min × 50,000 calls × 4 min = $60K/month. At Indian collection-call economics that is several times too expensive. Our flat ₹2/min standard-voice rate puts the same 200,000 minutes at ₹4L, telephony included.
Realtime vs cascaded — the architecture choice
OpenAI Realtime (audio → audio): customer audio → gpt-4o-realtime → agent audio Pros: native audio understanding, lowest latency Cons: expensive, English-tuned, no provider mixing ThinnestAI, cascaded (audio → text → audio): customer audio → Sarvam STT → the LLM → Cartesia TTS → agent audio Pros: cheap, multilingual, curated stack included in the flat rate Cons: an extra hop versus audio-to-audio, no tone/affect understanding from raw audio
For Indian-language production workloads, cascaded wins on cost and multilingual quality. Realtime wins when tone-and-affect understanding is a core differentiator. ThinnestAI is cascaded only — we do not offer a speech-to-speech or realtime model, and we are not going to pretend the trade-off doesn't exist.
FAQ
Is ThinnestAI an OpenAI alternative?
No. Many of our customers choose GPT-4o-mini as the LLM brain from our curated model list. We're not anti-OpenAI; we just don't lock you into the realtime audio API. Use OpenAI for what they're best at (reasoning), use cheaper specialised providers for audio.
Can I run OpenAI Realtime and ThinnestAI side by side?
They are separate stacks, and plenty of teams run both. ThinnestAI is a cascaded pipeline — Sarvam STT, your choice of LLM, Cartesia TTS — tuned end to end for Indian languages and billed at one flat per-minute rate with telephony included. If you want audio-native tone understanding for a specific English use case, run OpenAI Realtime directly on your own OpenAI account for those calls.
Latency comparison: realtime vs cascaded for Hindi?
OpenAI Realtime reports 300-500ms TTFA on Hindi. We don't publish a figure of our own — carrier-call performance on our stack isn't measured yet, and we'd rather say so than quote a number we can't defend. Architecturally, a cascaded pipeline adds a hop that an audio-to-audio model doesn't have, so if last-millisecond latency is your deciding factor, test both on your own traffic.
What if I want native audio understanding (sighs, tone)?
Use OpenAI Realtime directly. Its audio-in-audio-out is the only model that genuinely hears tone and affect today, and we will not pretend otherwise — ThinnestAI converts speech to text mid-pipeline and loses that dimension. Pick based on whether tone-understanding is a core requirement or a nice-to-have.
Can I migrate from gpt-4o-realtime to ThinnestAI?
Yes. System prompt and tool definitions port directly — you paste the prompt into the agent's settings, re-point the tools, and run a small share of traffic in parallel before flipping the rest.
Does ThinnestAI support Gemini Live or other speech-to-speech models?
No. ThinnestAI runs a cascaded pipeline — speech to text, then the model, then text to speech. That is a deliberate choice: it is what lets us tune each stage for Indian languages and hold one flat per-minute rate with telephony included. If audio-tokens-direct is a hard requirement for your use case, use the realtime model directly on the provider's own account.
Pricing on volume?
Enterprise pricing is negotiated — there's no fixed published per-minute rate at any volume (vs $0.30/min ≈ ₹25/min for OpenAI Realtime audio I/O combined). Contact sales for a quote.
India data residency on ThinnestAI?
Your workspace data — conversations, transcripts and call recordings — is resident in GCP Mumbai (asia-south1). Call recordings are retained 30 days on Pay As You Go and 75 on Scale (Trial does not record calls), and payment data is localised via Razorpay under RBI rules. The model and speech vendors behind the flat rate are curated and platform-managed rather than picked by you, so if a fully in-country processing chain is a hard procurement requirement, raise it with us before you build and we'll tell you exactly where each hop runs.