Gemini 3.1 Flash Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.006000 (rounded ~ $0.01)
Output: $0.006000 (rounded ~ $0.01)
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 2,000 output tokens:
- Input Cost: $0.107600 (rounded ~ $0.11)
- Output Cost: $0.006000 (rounded ~ $0.01)
- Total Cost: $0.065180 (rounded ~ $0.07)
- Cost per 1K tokens: $0.000300
- Tokens per dollar: 3,332,311 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, 37.11 seconds
- Latency: 100 milliseconds to first token
- Base Throughput: 800 tokens/second
- Effective Throughput: 784 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.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →Voxtral Small 24B Mistral AI
💰 Total Cost Calculation (from Plugin)
Output: $0.000150
Output: $0.000150
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 2,000 output tokens:
- Input Cost: $0.002500
- Output Cost: $0.000150
- Total Cost: $0.001525
- Cost per 1K tokens: $0.000015
- Tokens per dollar: 66,885,246 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 minutes, 20.28 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.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
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
|
$0.008148 (rounded ~ $0.01) Best Value | ↓ 87.5% cheaper | ↑ 434.3% more |
| 🥈 |
Gemini 3.5 Flash-Lite
Google
|
$0.010127 | ↓ 84.5% cheaper | ↑ 564.1% more |
| 🥉 |
Gemini 2.5 Flash
Google
|
$0.010127 | ↓ 84.5% cheaper | ↑ 564.1% more |
| #4 |
Gemini 3.8 Flash
Google
|
$0.024068 (rounded ~ $0.02) | ↓ 63.1% cheaper | ↑ 1478.2% more |
| #5 |
Gemini 3.6 Flash
Google
|
$0.048135 (rounded ~ $0.05) | ↓ 26.2% cheaper | ↑ 3056.4% more |
| #6 |
Gemini 3.5 Flash
Google
|
$0.048885 (rounded ~ $0.05) | ↓ 25% cheaper | ↑ 3105.6% more |
| #7 |
Gemini 2.5 Pro
Google
|
$0.162950 (rounded ~ $0.16) | ↑ 150% more | ↑ 10585.2% more |
| #8 |
Grok 4.3
xAI
|
$0.244720 (rounded ~ $0.24) | ↑ 275.5% more | ↑ 15947.2% more |
| #9 |
Grok 4.3
xAI
|
$0.244720 (rounded ~ $0.24) | ↑ 275.5% more | ↑ 15947.2% 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
For creators and technical teams managing consistent audio workloads—such as a 60-minute podcast episode—choosing the right transcription model is essential for balancing operational overhead with output quality. The decision often hinges on whether your pipeline requires a general-purpose multimodal engine or a specialized tool designed specifically for audio intelligence.
Gemini 3.1 Flash is well-regarded for its tight integration with broader AI workflows. It excels in scenarios where transcription is just the first step in a larger process, such as automatically summarizing content, generating show notes, or identifying key themes across multi-hour archives. Because of its multimodal capabilities, Gemini 3.1 Flash can process audio files directly without the need for a separate speech-to-text pre-processing layer, which simplifies your architecture significantly.
Voxtral Small 24B takes a different approach, prioritizing specialized audio intelligence. It leverages an architecture tuned for speech processing, making it highly effective for tasks where transcription accuracy and structured output—such as multi-speaker attribution or dialect handling—are the top priorities. Developers often prefer Voxtral for its predictable performance in dedicated transcription pipelines, where the primary goal is turning audio into structured, searchable text with high fidelity.
Ultimately, if your workflow is heavily integrated with other AI agents or automated post-processing, Gemini 3.1 Flash offers a unified, frictionless environment. Conversely, if your project demands a high-performance, specialized transcription engine with granular control over audio-specific inputs, Voxtral Small 24B provides a robust, efficient alternative. Consider whether you need the broad reasoning capabilities of a general-purpose model or the streamlined precision of an audio-focused architecture.