$ diff gemini-3-5-flash-lite gemini-3-6-flash
Gemini 3.5 Flash-Lite vs Gemini 3.6 Flash
Google DeepMind and Google DeepMind, side by side. Every value below is the one recorded on the model’s own wiki page, with its citation — nothing is estimated.
Google DeepMindGemini 3.5 Flash-Lite
- context
- 1.0M
- weights
- closed
- $/M in
- $0.3
- $/M out
- $2.5
- context
- 1.0M
- weights
- closed
- $/M in
- $1.5
- $/M out
- $7.5
What actually differs
- Context window
- Identical — both accept 1.0M tokens, so context length is not a reason to pick either.
- Input price
- Gemini 3.5 Flash-Lite at $0.3/M against $1.5/M — 5.0× cheaper to feed.
- Output price
- Gemini 3.5 Flash-Lite at $2.5/M against $7.5/M — 3.0× cheaper to generate. Output dominates the bill on most agentic workloads, where the model writes far more than it reads.
Full spec
| Attribute | Gemini 3.5 Flash-Lite | Gemini 3.6 Flash |
|---|---|---|
| Developer | Google DeepMind | Google DeepMind |
| Released | 2026-07-21 | 2026-07-21 |
| Context window | 1,048,576 tokens (1M) | 1,048,576 tokens (1M) |
| Pricing | $0.30/M input · $2.50/M output | $1.50/M input · $7.50/M output |
| License | proprietary | proprietary |
| Availability | Gemini API, Google AI Studio, Vertex AI | Gemini API, Google AI Studio, Android Studio, Vertex AI, consumer Gemini app, Google Search, GitHub Copilot |
Values come from Gemini 3.5 Flash-Lite and Gemini 3.6 Flash, where each is cited to its source. This page states no benchmark result and ranks neither model — it puts two published specifications next to each other. Where a lab has not published a figure, the row says so rather than guessing. How these pages are produced.