Gemini 3.5 Flash Lite
Gemini 3.5 Flash Lite upgrades the agentic capability of the Flash-Lite tier, outperforming Gemini 3.1 Flash-Lite across thinking levels on coding, long context, and real-world task execution, with built-in computer use and a minimal default thinking level for high-throughput work. Your use is subject to Google's Terms & Privacy Policies.
import { streamText } from 'ai'
const result = streamText({ model: 'google/gemini-3.5-flash-lite', prompt: 'Why is the sky blue?'})Playground
Try out Gemini 3.5 Flash Lite by Google. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
Gemini 3.5 Flash Lite
Gemini 3.5 Flash Lite
Providers
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
More models by Google
| Model |
|---|
| 1M | 2.0s | 181tps | $1.50/M | $7.50/M | Read:$0.15/M Write:— | $14/K+1 more + input costs | +3 |  | 07/21/2026 | ||||
| 1M | 1.9s | 150tps | $1.50/M | $9/M | Read:$0.15/M Write:— | $14/K+1 more + input costs | +3 | | 05/19/2026 | ||||
| 1M | 0.4s | 309tps | $0.25/M | $1.50/M | Read:$0.03/M Write:— | $14/K+1 more + input costs | +3 | | 05/07/2026 | ||||
| 1M | 2.2s | 103tps | $2/M+1 more | $12/M+1 more | Read: $0.2/M+1 more Write: — | $14/K+1 more + input costs | +3 | | 02/19/2026 | ||||
| 1M | 0.7s | 181tps | $0.50/M+1 more | $3/M+1 more | Read: $0.05/M+1 more Write: — | $14/K+1 more + input costs | +3 | | 12/17/2025 | ||||
| 1M | 0.2s | 405tps | $0.10/M | $0.40/M | Read:$0.01/M Write:— | $35/K+1 more + input costs | +3 | | 06/17/2025 |
About Gemini 3.5 Flash Lite
Gemini 3.5 Flash Lite is the efficiency tier of the Gemini 3.5 family, released on July 21, 2026. Google built it for scaling agentic systems rather than for peak single-turn quality. Across thinking levels, Gemini 3.5 Flash Lite outperforms Gemini 3.1 Flash-Lite, and the practical effect is that a Flash-Lite model can now sit inside an agent graph instead of serving only one-shot classification.
The coding and agentic gains are wide. Gemini 3.5 Flash Lite scores 54% on Terminal-Bench 2.1 against 31% for Gemini 3.1 Flash-Lite, 72.2% on GDM-MRCR v2 for long context against 60.1%, and 1140 on GDPval-AA v2 for real-world task execution against 642. On several agentic and coding evaluations it also passes Gemini 3 Flash, including 54.2% on SWE-Bench Pro against 49.6% and 74.0% on OSWorld-Verified against 65.1%. Teams running workloads on the 2.5 or 3 Flash tiers have a cheaper option that scores higher on those tasks.
Subagent execution is the pattern Google highlights. A master agent on a heavier model, such as google/gemini-3.6-flash, decomposes a task and hands scoped pieces to Gemini 3.5 Flash Lite: read this document set, extract these fields, translate this batch, parse this JSON into a schema. Each step stays cheap and fast, so you can fan out widely without aggregate token cost dictating the architecture. Computer use ships as a built-in tool, so a scoped step can include driving a browser rather than only reading text.
Gemini 3.5 Flash Lite accepts multimodal input within a context window of 1M tokens and returns text, up to 65K tokens. Check the Specs table on this page for the current modality list. Running Gemini 3.5 Flash Lite through AI Gateway adds usage and cost tracking, automatic retries, and provider failover on one API surface, which is what keeps a wide fan-out of subagent calls observable.
What To Consider When Choosing a Provider
- Configuration: Gemini 3.5 Flash Lite defaults to the
minimalthinking level. Keep it there for high-volume extraction, classification, and translation, where latency and per-token cost decide whether the workload is viable at all. Move tolowor a higher level when a subagent has to plan across several steps and stopping early would cost you a retry. Thinking tokens count toward output tokens, so the level you set changes what each request costs. Measure total spend under the thinking levels you actually plan to run. - Zero Data Retention: AI Gateway supports Zero Data Retention for this model via direct gateway requests (BYOK is not included). To configure this, check the documentation.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
When to Use Gemini 3.5 Flash Lite
Best for
- Scoped Subagent Execution: A heavier master agent hands off bounded extraction, translation, or parsing steps
- High-Volume Document Processing: Per-token cost decides how many documents a pipeline can afford to process
- Agentic Search Pipelines: Query planning and result triage run across many parallel calls
- Structured JSON Parsing: Schema-conformant output arrives from the cheapest tier in the Gemini 3.5 family
- Long-Context Extraction: GDM-MRCR v2 results improved substantially over Gemini 3.1 Flash-Lite
Consider alternatives when
- Master Agent Reasoning:
google/gemini-3.6-flashhandles the planning and hard coding steps in a multi-agent setup - Sustained Multi-Step Analysis:
google/gemini-3.1-pro-previewcarries more reasoning depth for difficult problems - Native Image Output:
google/gemini-3.1-flash-lite-imagegenerates and edits images at a comparable tier - Semantic Retrieval Workloads: A dedicated embedding model like
google/gemini-embedding-2fits search and clustering better
Conclusion
Gemini 3.5 Flash Lite makes the Flash-Lite tier viable inside agent architectures. It beats Gemini 3.1 Flash-Lite across thinking levels and passes Gemini 3 Flash on several agentic and coding evaluations, so scoped subagents, document pipelines, and high-throughput extraction can run on the cheapest tier in the Gemini 3.5 family.