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Codellama 34b Instruct vs Nemotron Nano V2 12b Vl
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.
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.
Codellama 34b Instruct and Nemotron Nano V2 12b Vl have identical context windows (4K tokens). Nemotron Nano V2 12b Vl is 90% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Nemotron Nano V2 12b Vl. Input tokens are 90% cheaper — critical when sending large retrieved contexts.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Codellama 34b Instruct | Nemotron Nano V2 12b Vl |
|---|---|---|
| Context window | 4,096 tokens (4K) | 4,096 tokens (4K) |
| Max output tokens | 4,096 tokens (4K) | 4,096 tokens (4K) |
| Speed tier | Balanced | Fast |
| Vision | No | No |
| Function calling | No | No |
| Extended thinking | No | No |
| Prompt caching | No | No |
| 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 | Codellama 34b Instruct in | Codellama 34b Instruct out | Nemotron Nano V2 12b Vl in | Nemotron Nano V2 12b Vl out |
|---|---|---|---|---|
| Anyscale | $1.00/M | $1.00/M | — | — |
| Fireworks | — | — | $0.100/M | $0.100/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