Compare
Apac Amazon Nova 2 Pro Preview 20251202 vs Nova 2 Lite
This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.
Model
Apac Amazon Nova 2 Pro Preview 20251202
Context window
1M
1,000,000 tokens · ~750K words
Model
Nova 2 Lite
Context window
1M
1,000,000 tokens · ~750K words
Context window · side by side
Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.
Same context window size for both models.
Apac Amazon Nova 2 Pro Preview 20251202 and Nova 2 Lite have identical context windows (1000K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long output (reports, code files)
Use Nova 2 Lite. Its 65K max output lets you generate complete artifacts in one request.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Apac Amazon Nova 2 Pro Preview 20251202 | Nova 2 Lite |
|---|---|---|
| Context window | 1,000,000 tokens (1000K) | 1,000,000 tokens (1000K) |
| Max output tokens | 64,000 tokens (64K) | 65,535 tokens (65K) |
| Speed tier | Balanced | Balanced |
| Vision | Yes | Yes |
| Function calling | Yes | Yes |
| Extended thinking | Yes | Yes |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | N/A | Dec 2025 |
Pricing matrix
Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.
| Provider | Apac Amazon Nova 2 Pro Preview 20251202 in | Apac Amazon Nova 2 Pro Preview 20251202 out | Nova 2 Lite in | Nova 2 Lite out |
|---|---|---|---|---|
| Aws Bedrock | $2.19/M | $17.50/M | — | — |
Frequently asked questions
Powered by Mem0
Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model