Gemini 3.1 Flash Lite Google 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.000750
Output: $0.000750
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Multimodal Input Details
Resolution: Medium
Tokens: 516,000
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 2,000 output tokens:
- Input Cost: $0.038500 (rounded ~ $0.04)
- Output Cost: $0.000750
- Total Cost: $0.039250
- Cost per 1K tokens: $0.000064
- Tokens per dollar: 15,745,223 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: 10 minutes, 42.90 seconds
- Latency: 80 milliseconds to first token
- Base Throughput: 1,000 tokens/second
- Effective Throughput: 962 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 |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.015550 (rounded ~ $0.02) Best Value | ↓ 60.4% cheaper |
| 🥈 |
Gemini 2.5 Flash
Google
|
$0.047450 (rounded ~ $0.05) | ↑ 20.9% more |
| 🥉 |
Mistral Large 3
Mistral AI
|
$0.078000 (rounded ~ $0.08) | ↑ 98.7% more |
| #4 |
GPT-5.4 mini
OpenAI
|
$0.117750 (rounded ~ $0.12) | ↑ 200% more |
| #5 |
o4-mini Deep Research
OpenAI
|
$0.156000 (rounded ~ $0.16) | ↑ 297.5% more |
| #6 |
Claude Haiku 4.5
Anthropic
|
$0.156500 (rounded ~ $0.16) | ↑ 298.7% more |
| #7 |
o4-mini
OpenAI
|
$0.171600 (rounded ~ $0.17) | ↑ 337.2% more |
| #8 |
Grok 4.3
xAI
|
$0.193750 (rounded ~ $0.19) | ↑ 393.6% more |
| #9 |
Gemini 3.5 Flash
Google
|
$0.235500 (rounded ~ $0.24) | ↑ 500% more |
| #10 |
Llama 4 Maverick (400B)
Meta AI
|
$0.257700 (rounded ~ $0.26) | ↑ 556.6% more |
| #11 |
GPT-5.3 Codex Spark
OpenAI
|
$0.276500 (rounded ~ $0.28) | ↑ 604.5% more |
| #12 |
GPT-5.3 Instant
OpenAI
|
$0.276500 (rounded ~ $0.28) | ↑ 604.5% more |
| #13 |
Gemini 3.1 Flash
Google
|
$0.314000 (rounded ~ $0.31) | ↑ 700% more |
| #14 |
Claude Sonnet 4.6
Anthropic
|
$0.469500 | ↑ 1096.2% more |
| #15 |
Claude Opus 4.7
Anthropic
|
$0.782500 (rounded ~ $0.78) | ↑ 1893.6% more |
| #16 |
Claude Opus 4.8
Anthropic
|
$0.782500 (rounded ~ $0.78) | ↑ 1893.6% more |
| #17 |
Claude Opus 4.6
Anthropic
|
$0.782500 (rounded ~ $0.78) | ↑ 1893.6% more |
| #18 |
Gemini 2.5 Pro
Google
|
$0.785000 (rounded ~ $0.79) | ↑ 1900% more |
| #19 |
GPT-5.5 Instant
OpenAI
|
$0.785000 (rounded ~ $0.79) | ↑ 1900% more |
| #20 |
Gemini 3.1 Pro
Google
|
$1.250000 | ↑ 3084.7% more |
| #21 |
o3 Deep Research
OpenAI
|
$1.560000 | ↑ 3874.5% more |
| #22 |
GPT-5.4
OpenAI
|
$1.562500 (rounded ~ $1.56) | ↑ 3880.9% more |
| #23 |
GPT-5.4 Thinking
OpenAI
|
$1.562500 (rounded ~ $1.56) | ↑ 3880.9% more |
| #24 |
o3 Pro
OpenAI
|
$3.120000 | ↑ 7849% more |
| #25 |
GPT-5.5
OpenAI
|
$3.125000 (rounded ~ $3.13) | ↑ 7861.8% more |
| #26 |
GPT-5.2 Pro
OpenAI
|
$3.318000 (rounded ~ $3.32) | ↑ 8353.5% more |
| #27 |
GPT-5.5 Pro
OpenAI
|
$4.710000 | ↑ 11900% more |
| #28 |
GPT-5.5 Pro
OpenAI
|
$4.710000 | ↑ 11900% more |
Mistral Small 3 Mistral AI
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
GPT-5.4 mini OpenAI
o4-mini Deep Research OpenAI
Claude Haiku 4.5 Anthropic
o4-mini OpenAI
Grok 4.3 xAI
Gemini 3.5 Flash Google
Llama 4 Maverick (400B) Meta AI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Gemini 3.1 Flash Google
Claude Sonnet 4.6 Anthropic
Claude Opus 4.7 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
Gemini 2.5 Pro Google
GPT-5.5 Instant OpenAI
Gemini 3.1 Pro Google
o3 Deep Research OpenAI
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
o3 Pro OpenAI
GPT-5.5 OpenAI
GPT-5.2 Pro OpenAI
GPT-5.5 Pro OpenAI
GPT-5.5 Pro OpenAI
Scaling Visual Data Extraction in Research
Academic researchers often face a significant bottleneck when attempting to catalog and describe thousands of charts, graphs, and figures across a vast corpus of literature. Utilizing Gemini 3.1 Flash Lite for processing 1,000 images offers a specialized solution for high-volume extraction where speed and resource efficiency are the primary drivers. This model is specifically engineered for rapid-fire vision tasks, making it highly effective for generating descriptive captions and structured metadata from standardized research graphics. Its primary strength lies in its ability to handle massive batches of visual input without the computational overhead associated with frontier-class reasoning models.
While Gemini 3.1 Flash Lite is optimized for maximum throughput, researchers should note that it prioritizes broad recognition and classification over the deep, iterative reasoning found in ‘Pro’ tier models. For 1,000-image batches, it provides a reliable baseline for indexing and creating searchable databases of visual evidence. This makes it an ideal choice for building comprehensive visual repositories or preparing extensive supplementary materials for systematic literature reviews. Researchers looking to optimize their budget should leverage this model for initial categorization and descriptive passes, reserving more intensive models only for the most complex, dense figures that require multi-step logical inference. The integration within the broader ecosystem allows for seamless data flow into downstream narration or summarization pipelines.