GPT Realtime Mini OpenAI
💰 Total Cost Calculation (from Plugin)
Output: $0.004800
Output: $0.004800
Unit: $0.000000
Fees: $0.010000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Cost: $18000.000000
Detailed Cost Analysis (from Plugin)
For 5,000 input tokens and 2,000 output tokens:
- Input Cost: $0.003000
- Output Cost: $0.004800
- Service Fees: $0.010000
- Total Cost: $0.017800 (rounded ~ $0.02)
- Cost per 1K tokens: $0.002543
- Tokens per dollar: 393,258 tokens
- Context Window: 128000 tokens
Speed & Performance Analysis
With a processing speed of 250 tokens per second and 50ms time to first token:
- Processing Time: 30.14 seconds
- Latency: 50 milliseconds to first token
- Base Throughput: 250 tokens/second
- Effective Throughput: 234 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to GPT Realtime Mini| Rank | AI Model & Provider | Total Cost | vs GPT Realtime Mini |
|---|---|---|---|
| 🏆 |
Gemini 3.1 Flash Lite
Google
|
$288.004250 (rounded ~ $288.00) Best Value | ↑ 1617901.4% more |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$345.606500 (rounded ~ $345.61) | ↑ 1941509.6% more |
| 🥉 |
Gemini 2.5 Flash
Google
|
$345.606500 (rounded ~ $345.61) | ↑ 1941509.6% more |
| #4 |
Gemini 3.8 Flash
Google
|
$864.011250 (rounded ~ $864.01) | ↑ 4853895.8% more |
| #5 |
Gemini 3.1 Flash
Google
|
$1152.017000 (rounded ~ $1,152.02) | ↑ 6471905.6% more |
| #6 |
Gemini 3.6 Flash
Google
|
$1728.022500 (rounded ~ $1,728.02) | ↑ 9707891.6% more |
| #7 |
Gemini 3.5 Flash
Google
|
$1728.025500 (rounded ~ $1,728.03) | ↑ 9707908.4% more |
| #8 |
Grok 4.3
xAI
|
$2880.022500 (rounded ~ $2,880.02) | ↑ 16179801.7% more |
| #9 |
Gemini 2.5 Pro
Google
|
$2880.042500 (rounded ~ $2,880.04) | ↑ 16179914% more |
| #10 |
Gemini 2.5 Pro
Google
|
$2880.042500 (rounded ~ $2,880.04) | ↑ 16179914% more |
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
Gemini 3.1 Flash Google
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Grok 4.3 xAI
Gemini 2.5 Pro Google
Gemini 2.5 Pro Google
Deploying real-time voice agents at scale requires more than just raw processing power; it demands a specialized architecture capable of handling speech-in, speech-out interactions with sub-second latency. When processing high-volume workloads—such as 600,000 minutes of call logs—the primary challenge for legal tech engineers is maintaining natural conversational rhythm while ensuring reliable function calling for compliance checks and data retrieval.
GPT Realtime Mini excels in these scenarios by streamlining the voice AI pipeline. Traditional voice AI often relies on a three-step relay—transcription, reasoning, and speech synthesis—which introduces cumulative latency and breaks immersion. This model simplifies the workflow by handling audio input and output natively. For legal applications, this means faster response times during sensitive client interactions, where every millisecond counts toward reducing user abandonment.
However, the decision to use a specialized voice-first model depends on your specific infrastructure needs. While this model is optimized for the low-latency requirements of interactive assistants, engineers must also consider the integration complexity with existing VoIP or WebRTC stacks. If your workload involves complex, multi-modal analysis alongside voice, you may need a more versatile, general-purpose model. For pure, high-frequency voice interactions where latency is the defining metric for success, this model remains a top-tier choice for production-grade voice agents. It effectively balances the need for rapid turn-taking with the conversational nuance required to navigate legal discovery or intake processes.