Gemini 3.1 Flash Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.001500
Output: $0.001500
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 1,000 input tokens and 500 output tokens:
- Input Cost: $57.600500
- Output Cost: $0.001500
- Total Cost: $47.233910 (rounded ~ $47.23)
- Cost per 1K tokens: $0.000410
- Tokens per dollar: 2,438,958 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: 40 hours, 48 minutes, 2.09 seconds
- Latency: 100 milliseconds to first token
- Base Throughput: 800 tokens/second
- Effective Throughput: 784 tokens/second (temperature-adjusted)
Best Use Cases
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💰 Total Cost Calculation (from Plugin)
Output: $0.000038
Output: $0.000038
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 1,000 input tokens and 500 output tokens:
- Input Cost: $0.000025
- Output Cost: $0.000038
- Total Cost: $0.000058
- Cost per 1K tokens: $0.000039
- Tokens per dollar: 25,862,069 tokens
- Context Window: 32000 tokens
Speed & Performance Analysis
With a processing speed of 400 tokens per second and 150ms time to first token:
- Processing Time: 4.01 seconds
- Latency: 150 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 392 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Voxtral Small 24B. 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 Gemini 3.1 Flash| Rank | AI Model & Provider | Total Cost | vs Gemini 3.1 Flash | vs Voxtral Small 24B |
|---|---|---|---|---|
| 🏆 |
Gemini 3.1 Flash Lite
Google
|
$5.904239 (rounded ~ $5.90) Best Value | ↓ 87.5% cheaper | ↑ 10179622% more |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$7.085174 (rounded ~ $7.09) | ↓ 85% cheaper | ↑ 12215717.2% more |
| 🥉 |
Gemini 2.5 Flash
Google
|
$7.085174 (rounded ~ $7.09) | ↓ 85% cheaper | ↑ 12215717.2% more |
| #4 |
Gemini 3.8 Flash
Google
|
$17.712623 (rounded ~ $17.71) | ↓ 62.5% cheaper | ↑ 30538904.3% more |
| #5 |
Gemini 3.6 Flash
Google
|
$35.425245 (rounded ~ $35.43) | ↓ 25% cheaper | ↑ 61077908.6% more |
| #6 |
Gemini 3.5 Flash
Google
|
$35.425433 (rounded ~ $35.43) | ↓ 25% cheaper | ↑ 61078231.9% more |
| #7 |
Gemini 2.5 Pro
Google
|
$118.084775 (rounded ~ $118.08) | ↑ 150% more | ↑ 203594339.7% more |
| #8 |
Grok 4.3
xAI
|
$188.931640 (rounded ~ $188.93) | ↑ 300% more | ↑ 325744106.9% more |
| #9 |
Grok 4.3
xAI
|
$188.931640 (rounded ~ $188.93) | ↑ 300% more | ↑ 325744106.9% more |
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Gemini 2.5 Pro Google
Grok 4.3 xAI
Grok 4.3 xAI
Optimizing Transcription Pipelines in EdTech
For EdTech product managers building real-time tutoring and meeting note solutions, transcription accuracy and latency are the primary drivers of user retention. When processing high volumes like 1,000 hours of audio monthly, selecting the right model requires balancing native audio understanding with architectural efficiency.
Gemini 3.1 Flash excels in multimodal environments where native audio and video inputs are processed alongside text. Its ability to ingest audio directly reduces the complexity of pre-processing pipelines, which is a major advantage for live tutoring sessions where every millisecond counts. For teams already deep in the Google ecosystem, the integration is often seamless.
Voxtral Small 24B, conversely, represents a specialized approach to audio-input tasks. It is often favored by engineering teams looking for a performant, lightweight model that minimizes overhead. While it provides excellent value for high-throughput transcription, it may require more sophisticated orchestration if your pipeline demands complex, multi-step analysis beyond simple transcription. For compliance-heavy EdTech use cases, evaluating how each model handles pedagogical terminology and diverse student accents is critical. If your application prioritizes native multimodal support, Gemini offers a robust path forward. If you are building a modular pipeline that demands high-performance, specialized audio processing at scale, Voxtral provides a compelling alternative for your production architecture.