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Comparisons

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.

L LFM2.5-8B-A1B Meta Llama 3.2 3B Instruct
LabLiquidAIMeta
AccessOpen weightsOpen weights
Context window32K tokens128K tokens
List price, input$0.03 / M tokens$0.06 / M tokens
List price, output$0.12 / M tokens$0.06 / M tokens
Cached inputn/an/a
LicenseCustom licenseLlama community
Fine-tunableYesYes

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.

LFM2.5-8B-A1B$0.03$0.12
Meta Llama 3.2 3B Instruct$0.06$0.06
InputOutput

Choose LFM2.5-8B-A1B for

  • The lower list price ($0.03 in / $0.12 out per M tokens)
LFM2.5-8B-A1B details

Choose Meta Llama 3.2 3B Instruct for

  • The longer context window (128K vs 32K tokens)
Meta Llama 3.2 3B Instruct details

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.

Related comparisons

LFM2.5-8B-A1B vs Meta Llama 3.2 1B InstructMeta Llama 3.2 3B Instruct vs Meta Llama 3.2 1B InstructLFM2.5-8B-A1B vs Gemma 3N E4B InstructMeta Llama 3.2 3B Instruct vs Qwen 2 Instruct (1.5B)

Run the numbers on your workload

LLM price comparisonAgent cost calculatorLFM2.5-8B-A1BMeta Llama 3.2 3B Instruct

Or don’t choose. On Allocate a route name is the contract: point yours at one model today, swap to the other tomorrow, and compare them on your live traffic with per-token metering.

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