Gemini 2.5 Flash Lite vs Gemini 3.1 Flash
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Gemini 2.5 Flash Lite and Gemini 3.1 Flash.
Gemini 2.5 Flash Lite
Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance...
Gemini 3.1 Flash
Gemini 3.1 Flash is Google's high-speed, cost-efficient multimodal model in the 3.1 generation, purpose-built for high-volume content synthesis, classification, and intelligent routing at scale. Featuring a 1-million-token context window, it can process large batches of documents, customer data, or multimedia content in a single inference pass, dramatically reducing pipeline complexity. At just $0.25/MTok for input, it is one of the most affordable routes to Google-caliber multimodal AI, making it an ideal backbone for production pipelines, data enrichment workflows, and high-frequency API integrations.
Technical Specifications
| Specification | Gemini 2.5 Flash Lite | Gemini 3.1 Flash |
|---|---|---|
| Provider | ||
| Context Window | 1,048,576 tokens | 1,000,000 tokens |
| Agent Suitability | N/A | 86/100 |
| Time to First Token (TTFT) | N/A | 150 ms |
| Deployment Model | managed api | managed api |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-07-22 | 2026-04-20 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 2.5 Flash Lite
$0.10
Gemini 3.1 Flash
$0.25
Output Price per Million Tokens
Gemini 2.5 Flash Lite
$0.40
Gemini 3.1 Flash
$1.50
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Gemini 2.5 Flash Lite Quirks & Gotchas
No developer gotchas reported.
Gemini 3.1 Flash Quirks & Gotchas
- ▸Most cost-effective Google model — ideal for high-volume pipelines
- ▸Context caching available via Vertex AI for repeated document processing