GPT Realtime Mini OpenAI
💰 Total Cost Calculation (from Plugin)
Output: $0.001200
Output: $0.001200
Unit: $0.000000
Fees: $0.010000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Cost: $1.800000
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 500 output tokens:
- Input Cost: $0.060000
- Output Cost: $0.001200
- Service Fees: $0.010000
- Total Cost: $0.060400
- Cost per 1K tokens: $0.000601
- Tokens per dollar: 1,663,907 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: 7 minutes, 10.32 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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This calculator shows the math for GPT Realtime Mini. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
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💰 Total Cost Calculation (from Plugin)
Output: $0.003000
Output: $0.003000
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 500 output tokens:
- Input Cost: $0.215200 (rounded ~ $0.22)
- Output Cost: $0.003000
- Total Cost: $0.179464
- Cost per 1K tokens: $0.000832
- Tokens per dollar: 1,201,912 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 800 tokens per second and 100ms time to first token:
- Processing Time: 4 minutes, 48.68 seconds
- Latency: 100 milliseconds to first token
- Base Throughput: 800 tokens/second
- Effective Throughput: 748 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Gemini 3.1 Flash. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
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Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to GPT Realtime Mini| Rank | AI Model & Provider | Total Cost | vs GPT Realtime Mini | vs Gemini 3.1 Flash |
|---|---|---|---|---|
| 🏆 |
Gemini 3.1 Flash Lite
Google
|
$0.044866 (rounded ~ $0.04) Best Value | ↓ 25.7% cheaper | ↓ 75% cheaper |
| 🥈 |
Gemini 2.5 Flash
Google
|
$0.054189 (rounded ~ $0.05) | ↓ 10.3% cheaper | ↓ 69.8% cheaper |
| 🥉 |
Gemini 3.1 Flash
Google
|
$0.179464 | ↑ 197.1% more | Same price |
| #4 |
Grok 4.3
xAI
|
$0.221830 (rounded ~ $0.22) | ↑ 267.3% more | ↑ 23.6% more |
| #5 |
Gemini 3.5 Flash
Google
|
$0.269196 | ↑ 345.7% more | ↑ 50% more |
| #6 |
Gemini 2.5 Pro
Google
|
$0.448660 (rounded ~ $0.45) | ↑ 642.8% more | ↑ 150% more |
| #7 |
Gemini 2.5 Pro
Google
|
$0.448660 (rounded ~ $0.45) | ↑ 642.8% more | ↑ 150% more |
Gemini 3.1 Flash Lite Google
Gemini 2.5 Flash Google
Gemini 3.1 Flash Google
Grok 4.3 xAI
Gemini 3.5 Flash Google
Gemini 2.5 Pro Google
Gemini 2.5 Pro Google
Choosing the Right Architecture for Voice Agents
Deploying real-time voice agents requires balancing extreme low latency with natural, conversational capability. For customer support teams, the choice between a dedicated streaming model and a high-performance multimodal model depends entirely on your architectural priorities. The platform you select will dictate how your system handles interruptions, turn-taking, and the overall flow of the conversation.
GPT Realtime Mini is explicitly engineered for the sub-second responsiveness required in live voice interaction. It minimizes the overhead of handling audio buffers, making it the superior choice if your primary success metric is time-to-first-token in a fast-paced conversation. It shines in support scenarios where rapid turn-taking is critical to maintaining user engagement.
Conversely, Gemini 3.1 Flash offers a more versatile multimodal framework. While it may introduce slightly higher latency compared to specialized streaming models, its ability to integrate complex contextual data—like retrieving customer history or cross-referencing support documentation in real-time—is highly advanced. If your agent’s success relies more on sophisticated reasoning over retrieved context than on the raw speed of the voice cadence, Gemini’s architecture is compelling.
Consider the trade-off: choose the streaming-optimized model to ensure users never feel the lag that kills engagement. Choose the multimodal powerhouse when the agent needs to act as a research assistant during the call, pulling information from diverse sources to resolve complex tickets. For most early-stage voice deployments, testing both with a small batch of 60-minute call transcripts is the most reliable way to validate which path best matches your specific customer experience targets.