Gemini 3.5 Flash-Lite
Google's fastest, most cost-efficient 3.5-class model — optimized for high-throughput agentic tasks, document parsing, and low-latency reasoning.
Overview
What is Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite is Google DeepMind's fastest and most cost-effective multimodal reasoning model, released in July 2026. It supports text, images, audio, and video inputs, with a 1M-token context window and 65K-token output, optimized for high-throughput agentic workflows and low-latency tasks.
Running it privately on Venice
On Venice, Gemini 3.5 Flash-Lite runs with anonymized privacy — your prompts are never stored or used for training. You get full access to its vision, function calling, and web search capabilities without surveillance, ideal for scalable, private agentic applications. The model’s low latency and cost efficiency are preserved in Venice’s zero-retention environment.
Assessment
Strengths and limitations
- Extremely fast: delivers up to 376 output tokens per second, ideal for latency-sensitive workflows.
- Cost-efficient for high-volume tasks like classification, translation, and JSON extraction.
- Supports multimodal inputs (text, image, audio, video) and structured outputs via JSON schema.
- Enables tool use, web search, and function calling for agentic automation.
- 1M-token context window allows deep document analysis and long-form reasoning.
- Custom inference parameters (temperature, top-k, top-p) are not supported — limits fine control over output randomness.
- No computer use or code execution in sandbox: restricts autonomous tool operation.
- Less suitable for complex multi-step agentic reasoning compared to higher-tier models like Gemini 3.5 Flash or Opus-class models.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- Google DeepMind
- Released
- July 2026
- Architecture
- Based on Gemini 3.1 Flash-Lite
- Parameters
- Not publicly disclosed
- Open weights
- No — proprietary
- Context window
- 1,000K tokens
- Max output
- 65.536K tokens
- Capabilities
- Vision, Function calling, Reasoning, Web search
- Privacy on Venice
- Anonymized — prompts not stored
- Available on Venice since
- Jul 2026
- License
- Proprietary
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gemini-3-5-flash-lite",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.38 per 1M input tokens and $3.13 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Best for | Max output | Speed (tok/s) | Open weights | Price (Venice) |
|---|---|---|---|---|---|
| Gemini 3.5 Flash-Lite | Google's fastest low-cost model for agentic throughput. | 65.5K | 376 | No | $0.38 in · $3.13 out / 1M |
| DeepSeek V4 Flash 0731 | Slightly faster and far cheaper, but less integrated with Google tools. | 64K | 412 | No | $0.17 in · $0.35 out / 1M |
| Google Gemma 4 31B Instruct | Open weights and lower cost, but smaller context and weaker agentic performance. | 32K | 289 | Yes | $0.12 in · $0.36 out / 1M |
| Claude Sonnet 4.6 | More reasoning depth, but much slower and pricier for high-volume tasks. | 16K | 183 | No | $3.60 in · $18 out / 1M |
Google's fastest low-cost model for agentic throughput.
Use cases
What it is good for
- 01High-throughput document parsing and data extraction at scale.
- 02Low-latency translation and content classification pipelines.
- 03Lightweight agentic subtasks with vision and function calling.
- 04Receipt or invoice analysis with multimodal input and structured output.
- 05Rapid prototyping of UI or game concepts via API automation.
Prompting
Getting better results
Use minimal thinking level for fast, low-cost classification or extraction tasks.
Set thinking level to medium or high when using as a subagent for code or API workflows.
Include images or PDFs directly in prompts — the model handles multimodal input natively.
Leverage web search grounding for real-time information retrieval within prompts.
Version history
Predecessor model
Base model
Current — faster, more efficient
FAQ
Frequently asked questions
Gemini 3.5 Flash-Lite is Google DeepMind's fastest and most cost-efficient multimodal reasoning model, released in July 2026. It supports text, image, audio, and video inputs, with a 1M-token context window, optimized for high-throughput agentic workflows and low-latency tasks.
On Venice, Gemini 3.5 Flash-Lite is billed at $0.38 per 1M input tokens and $3.13 per 1M output tokens. Cached input is even cheaper at $0.04 per 1M tokens, making repeated queries highly efficient.
No. Gemini 3.5 Flash-Lite is a proprietary Google model with no open weights. It is not free to use — pricing is based on token consumption, though Venice offers no upfront subscription.
Yes. Gemini 3.5 Flash-Lite supports image, video, and PDF inputs natively, enabling multimodal understanding for tasks like receipt analysis, document parsing, and visual content interpretation.
Yes. The model supports function calling, web search, and structured output, making it suitable for lightweight agentic workflows. However, it does not support full computer use or sandboxed code execution.
Gemini 3.5 Flash-Lite has a 1M-token context window, allowing it to process extremely long documents or complex multimodal inputs in a single pass.
Gemini 3.5 Flash-Lite offers stronger integration with Google's ecosystem and better multimodal support, while DeepSeek V4 Flash is faster and significantly cheaper. Choose Gemini for Google-grounded workflows, DeepSeek for pure cost-performance.
Run Gemini 3.5 Flash-Lite privately
No prompt logging. No data used for training.