// head_to_head
Nova 2 Lite vs Gemini 3.5 Flash Lite
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Nova 2 Lite and Gemini 3.5 Flash Lite are priced within ~10% of each other.
Nova 2 Lite does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
amazon
Nova 2 Lite
- Blended / 1M
- $0.850
- Context
- 1M
- Released
- Dec 2, 2025
- Overall score
- Not evaluated
Gemini 3.5 Flash Lite
- Blended / 1M
- $0.850
- Context
- 1.0M
- Released
- Jul 21, 2026
- Overall score
- 63.9
Specs and pricing
| Metric | Nova 2 Lite | Gemini 3.5 Flash Lite |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 63.9 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0379 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.850 | $0.850 |
| Input price / 1M | $0.300 | $0.300 |
| Output price / 1M | $2.50 | $2.50 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | $0.030 |
| Context window | 1M | 1.0M |
| Max output tokens | 66K | 66K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Nova 2 Lite on top, Gemini 3.5 Flash Lite below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Nova 2 Lite | Gemini 3.5 Flash Lite |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $272.00/mo | $252.56/mo |
| RAG assistant 8K in / 600 out × 100K requests | $390.00/mo | $282.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $440.00/mo | $288.80/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $487.50/mo | $474.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,000/mo | $865.00/mo |
Nova 2 Lite vs Gemini 3.5 Flash Lite FAQ
Which is better, Nova 2 Lite or Gemini 3.5 Flash Lite?
Nova 2 Lite and Gemini 3.5 Flash Lite are priced within ~10% of each other. Nova 2 Lite does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is Nova 2 Lite cheaper than Gemini 3.5 Flash Lite?
They cost about the same. Both land near $0.850 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does Nova 2 Lite or Gemini 3.5 Flash Lite have a bigger context window?
They are effectively the same — 1M for Nova 2 Lite and 1.0M for Gemini 3.5 Flash Lite.
Do Nova 2 Lite and Gemini 3.5 Flash Lite support prompt caching?
Gemini 3.5 Flash Lite publishes a cached-input rate of $0.030 per million tokens against a full input rate of $0.300. The catalogue lists no separate cached rate for Nova 2 Lite, which means the provider does not price it separately here — not that caching is unavailable.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.