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Codellama 34b Instruct vs LFM2-24B-A2B
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.
LFM2-24B-A2B has about 8× the context window of the other in this pair.
LFM2-24B-A2B has 700% more context capacity (32K vs 4K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use LFM2-24B-A2B. Its 32K context fits entire documents without chunking (vs 4K).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Codellama 34b Instruct | LFM2-24B-A2B |
|---|---|---|
| Context window | 4,096 tokens (4K) | 32,768 tokens (32K) |
| Max output tokens | 4,096 tokens (4K) | N/A |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | No | No |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | N/A | Feb 2026 |
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 | LFM2-24B-A2B in | LFM2-24B-A2B out |
|---|---|---|---|---|
| Anyscale | $1.00/M | $1.00/M | — | — |
Frequently asked questions
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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
80% less to send — works with any model