Compare
DeepSeek V4 Flash (free) vs Nemotron 3 Super
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
DeepSeek V4 Flash (free)
Context window
256K
256,000 tokens · ~192K 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.
Nemotron 3 Super has about 1× the context window of the other in this pair.
Nemotron 3 Super has 2% more context capacity (262K vs 256K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Nemotron 3 Super. Its 262K context fits entire documents without chunking (vs 256K).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | DeepSeek V4 Flash (free) | Nemotron 3 Super |
|---|---|---|
| Context window | 256,000 tokens (256K) | 262,144 tokens (262K) |
| Max output tokens | 256,000 tokens (256K) | N/A |
| Speed tier | Fast | Balanced |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | Yes | Yes |
| Prompt caching | No | Yes |
| Batch API | No | No |
| Release date | Apr 2026 | Mar 2026 |
Frequently asked questions
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Use a smaller model.
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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
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