Compare
Apac Amazon Nova 2 Lite vs Deepseek V3 0324
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 Lite
Context window
1M
1,000,000 tokens · ~750K words
Model
Deepseek V3 0324
Context window
128K
128,000 tokens · ~96K 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.
Apac Amazon Nova 2 Lite has about 7.8× the context window of the other in this pair.
Apac Amazon Nova 2 Lite has 681% more context capacity (1000K vs 128K tokens). Apac Amazon Nova 2 Lite is 17% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Apac Amazon Nova 2 Lite. Its 1000K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use Apac Amazon Nova 2 Lite. Input tokens are 17% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Apac Amazon Nova 2 Lite. Its 64K 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 Lite | Deepseek V3 0324 |
|---|---|---|
| Context window | 1,000,000 tokens (1000K) | 128,000 tokens (128K) |
| Max output tokens | 64,000 tokens (64K) | 8,192 tokens (8K) |
| Speed tier | Balanced | Balanced |
| Vision | Yes | No |
| Function calling | Yes | Yes |
| Extended thinking | Yes | Yes |
| Prompt caching | Yes | Yes |
| Batch API | No | No |
| Release date | N/A | N/A |
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 Lite in | Apac Amazon Nova 2 Lite out | Deepseek V3 0324 in | Deepseek V3 0324 out |
|---|---|---|---|---|
| Aws Bedrock | $0.330/M | $2.75/M | — | — |
| Azure | — | — | $1.14/M | $4.56/M |
| Baseten | — | — | $0.770/M | $0.770/M |
| Deepinfra | — | — | $0.250/M | $0.880/M |
| Fireworks | — | — | $0.900/M | $0.900/M |
| Gmi | — | — | $0.280/M | $0.880/M |
| Hyperbolic | — | — | $0.400/M | $0.400/M |
| Lambda | — | — | $0.200/M | $0.600/M |
| Nebius | — | — | $0.500/M | $1.50/M |
| Novita | — | — | $0.270/M | $1.12/M |
| Sambanova | — | — | $3.00/M | $4.50/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