# LFM2.5-8B-A1B vs Meta Llama 3.2 3B Instruct

On provider list prices, Meta Llama 3.2 3B Instruct costs $0.06 per million input tokens against $0.03 for LFM2.5-8B-A1B: effectively level. Output is $0.06 against $0.12 (2.0x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | LFM2.5-8B-A1B | Meta Llama 3.2 3B Instruct |
| --- | --- | --- |
| Lab | LiquidAI | Meta |
| Access | Open weights | Open weights |
| Context window | 32K tokens | 128K tokens |
| List price, input | $0.03 / M tokens | $0.06 / M tokens |
| List price, output | $0.12 / M tokens | $0.06 / M tokens |
| Cached input | n/a | n/a |
| License | Custom license | Llama community |
| Fine-tunable | Yes | Yes |

Specifications and provider list prices from the Allocate catalog, checked 2026-07-08. Billed price is list plus the 7% transaction fee.

## What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $78 a month on LFM2.5-8B-A1B and $93 on Meta Llama 3.2 3B Instruct at list: a gap of $15, or 1.2x.

Meta Llama 3.2 3B Instruct reads 128K tokens per request against 32K for LFM2.5-8B-A1B, 4.0x the window. That decides which one can take whole documents without splitting them.

## Choose LFM2.5-8B-A1B for

- The lower list price ($0.03 in / $0.12 out per M tokens)

## Choose Meta Llama 3.2 3B Instruct for

- The longer context window (128K vs 32K tokens)

## Common questions

### Which is cheaper, LFM2.5-8B-A1B or Meta Llama 3.2 3B Instruct?

LFM2.5-8B-A1B, on this workload shape. At list prices it is $0.03/$0.12 per million tokens in and out against $0.06/$0.06 for Meta Llama 3.2 3B Instruct. Billed on Allocate: $0.032/$0.13 against $0.064/$0.064, list plus 7%.

### Which has the bigger context window?

Meta Llama 3.2 3B Instruct: 131,072 tokens (128K) against 32,768 (32K) for LFM2.5-8B-A1B.

### Can I fine-tune LFM2.5-8B-A1B or Meta Llama 3.2 3B Instruct?

Both publish open weights (LFM2.5-8B-A1B: Custom license; Meta Llama 3.2 3B Instruct: Llama community), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

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[HTML page](https://allocate.network/compare/liquid-lfm2-5-8b-a1b-vs-meta-llama-3-2-3b-instruct) · [LFM2.5-8B-A1B](https://allocate.network/models/liquid-lfm2-5-8b-a1b.md) · [Meta Llama 3.2 3B Instruct](https://allocate.network/models/meta-llama-3-2-3b-instruct.md) · [Machine-readable catalog](https://allocate.network/catalog.json)