# Meta Llama 3 8B Instruct Reference vs Meta Llama 3.1 8B

On provider list prices, Meta Llama 3 8B Instruct Reference costs $0.20 per million input tokens against $0.20 for Meta Llama 3.1 8B: effectively level. Output is $0.20 against $0.20. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3 8B Instruct Reference | Meta Llama 3.1 8B |
| --- | --- | --- |
| Lab | Meta | Meta |
| Access | Open weights | Open weights |
| Context window | 8K tokens | 16K tokens |
| List price, input | $0.20 / M tokens | $0.20 / M tokens |
| List price, output | $0.20 / M tokens | $0.20 / M tokens |
| Cached input | n/a | n/a |
| License | Llama community | 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 $310 a month on Meta Llama 3 8B Instruct Reference and $310 on Meta Llama 3.1 8B at list: a gap of $0.

Meta Llama 3.1 8B reads 16K tokens per request against 8K for Meta Llama 3 8B Instruct Reference, 2.0x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3 8B Instruct Reference for

- Training toward a model you own

## Choose Meta Llama 3.1 8B for

- The longer context window (16K vs 8K tokens)

## Common questions

### Which is cheaper, Meta Llama 3 8B Instruct Reference or Meta Llama 3.1 8B?

Meta Llama 3 8B Instruct Reference, on this workload shape. At list prices it is $0.20/$0.20 per million tokens in and out against $0.20/$0.20 for Meta Llama 3.1 8B. Billed on Allocate: $0.21/$0.21 against $0.21/$0.21, list plus 7%.

### Which has the bigger context window?

Meta Llama 3.1 8B: 16,384 tokens (16K) against 8,192 (8K) for Meta Llama 3 8B Instruct Reference.

### Can I fine-tune Meta Llama 3 8B Instruct Reference or Meta Llama 3.1 8B?

Both publish open weights (Meta Llama 3 8B Instruct Reference: Llama community; Meta Llama 3.1 8B: 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/meta-llama-3-8b-chat-hf-vs-meta-meta-llama-3-1-8b) · [Meta Llama 3 8B Instruct Reference](https://allocate.network/models/meta-llama-3-8b-chat-hf.md) · [Meta Llama 3.1 8B](https://allocate.network/models/meta-meta-llama-3-1-8b.md) · [Machine-readable catalog](https://allocate.network/catalog.json)