Gemini 3.1 Flash Lite Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.001500
Output: $0.001500
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 500,000 input tokens and 1,000 output tokens:
- Input Cost: $0.125000 (rounded ~ $0.13)
- Output Cost: $0.001500
- Total Cost: $0.081500 (rounded ~ $0.08)
- Cost per 1K tokens: $0.000163
- Tokens per dollar: 6,147,239 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 1,000 tokens per second and 80ms time to first token:
- Processing Time: 8 minutes, 56.25 seconds
- Latency: 80 milliseconds to first token
- Base Throughput: 1,000 tokens/second
- Effective Throughput: 935 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to Gemini 3.1 Flash Lite| Rank | AI Model & Provider | Total Cost | vs Gemini 3.1 Flash Lite |
|---|---|---|---|
| 🏆 |
Gemini 3.5 Flash-Lite
Google
|
$0.098500 (rounded ~ $0.10) Best Value | ↑ 20.9% more |
| 🥈 |
Gemini 2.5 Flash
Google
|
$0.098500 (rounded ~ $0.10) | ↑ 20.9% more |
| 🥉 |
Gemini 3.8 Flash
Google
|
$0.243750 (rounded ~ $0.24) | ↑ 199.1% more |
| #4 |
Gemini 3.1 Flash
Google
|
$0.326000 (rounded ~ $0.33) | ↑ 300% more |
| #5 |
GPT-5.6 Luna
OpenAI
|
$0.326000 (rounded ~ $0.33) | ↑ 300% more |
| #6 |
Gemini 3.6 Flash
Google
|
$0.487500 (rounded ~ $0.49) | ↑ 498.2% more |
| #7 |
Gemini 3.5 Flash
Google
|
$0.489000 (rounded ~ $0.49) | ↑ 500% more |
| #8 |
Claude Sonnet 5
Anthropic
|
$0.650000 | ↑ 697.5% more |
| #9 |
Grok 4.3
xAI
|
$0.805000 (rounded ~ $0.81) | ↑ 887.7% more |
| #10 |
Grok 4.20 Beta
xAI
|
$0.805000 (rounded ~ $0.81) | ↑ 887.7% more |
| #11 |
Gemini 2.5 Pro
Google
|
$0.815000 (rounded ~ $0.82) | ↑ 900% more |
| #12 |
GPT-5.6 Terra
OpenAI
|
$0.815000 (rounded ~ $0.82) | ↑ 900% more |
| #13 |
Claude Sonnet 4.6
Anthropic
|
$0.975000 (rounded ~ $0.98) | ↑ 1096.3% more |
| #14 |
Gemini 3.1 Pro
Google
|
$1.298000 (rounded ~ $1.30) | ↑ 1492.6% more |
| #15 |
GPT-5.4
OpenAI
|
$1.622500 (rounded ~ $1.62) | ↑ 1890.8% more |
| #16 |
GPT-5.4 Thinking
OpenAI
|
$1.622500 (rounded ~ $1.62) | ↑ 1890.8% more |
| #17 |
Claude Opus 4.7
Anthropic
|
$1.625000 (rounded ~ $1.63) | ↑ 1893.9% more |
| #18 |
Claude Opus 5
Anthropic
|
$1.625000 (rounded ~ $1.63) | ↑ 1893.9% more |
| #19 |
Claude Opus 4.8
Anthropic
|
$1.625000 (rounded ~ $1.63) | ↑ 1893.9% more |
| #20 |
Claude Opus 4.6
Anthropic
|
$1.625000 (rounded ~ $1.63) | ↑ 1893.9% more |
| #21 |
GPT-5.6 Sol
OpenAI
|
$1.630000 | ↑ 1900% more |
| #22 |
Claude Fable 5.1
Anthropic
|
$3.100000 | ↑ 3703.7% more |
| #23 |
Claude Mythos 5.1
Anthropic
|
$3.100000 | ↑ 3703.7% more |
| #24 |
GPT-5.5
OpenAI
|
$3.245000 (rounded ~ $3.25) | ↑ 3881.6% more |
| #25 |
Claude Fable 5
Anthropic
|
$3.250000 | ↑ 3887.7% more |
| #26 |
Claude Mythos 5
Anthropic
|
$3.250000 | ↑ 3887.7% more |
| #27 |
GPT-6 Astra
OpenAI
|
$6.500000 | ↑ 7875.5% more |
| #28 |
GPT-6 Astra
OpenAI
|
$6.500000 | ↑ 7875.5% more |
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Claude Sonnet 5 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
Gemini 2.5 Pro Google
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
Gemini 3.1 Pro Google
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.6 Sol OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
GPT-5.5 OpenAI
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-6 Astra OpenAI
GPT-6 Astra OpenAI
Qualitative Analysis for Small-Scale RAG
Developing a Retrieval-Augmented Generation (RAG) system for a small-scale MVP requires balancing intelligence with extreme operational efficiency. For developers prototyping these systems, Gemini 3.1 Flash Lite presents a compelling option due to its massive context window. This allows for more generous retrieval strategies where larger chunks of documentation can be passed into the prompt without hitting context limits or significantly impacting responsiveness.
Qualitatively, this model shines in processing multimodal inputs, which is particularly useful for customer support leads who anticipate users sharing screenshots or technical diagrams alongside text queries. While it is built for speed, the model maintains a high level of accuracy for structured data extraction, making it suitable for populating support tickets or summarizing long conversation histories.
One key consideration for hobbyists is the Google Cloud ecosystem integration. If your prototype already lives on Firebase or Google Cloud, the deployment friction is virtually zero. However, users should monitor performance on highly nuanced linguistic tasks compared to larger frontier models. For a 500K-token workload, this model acts as a robust ‘workhorse’ that handles the heavy lifting of information retrieval and summarization, allowing developers to focus on refining their vector database and retrieval logic rather than worrying about prompt compression or complex context management.