# Meta Llama 3.2 3B Instruct vs Qwen 2 Instruct (1.5B)

On provider list prices, Qwen 2 Instruct (1.5B) costs $0.02 per million input tokens against $0.06 for Meta Llama 3.2 3B Instruct: 3.0x apart. Output is $0.02 against $0.06 (3.0x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.2 3B Instruct | Qwen 2 Instruct (1.5B) |
| --- | --- | --- |
| Lab | Meta | Qwen |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 32K tokens |
| List price, input | $0.06 / M tokens | $0.02 / M tokens |
| List price, output | $0.06 / M tokens | $0.02 / M tokens |
| Cached input | n/a | n/a |
| License | Llama community | Qwen license |
| 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 $31 a month on Qwen 2 Instruct (1.5B) and $93 on Meta Llama 3.2 3B Instruct at list: a gap of $62, or 3.0x.

Meta Llama 3.2 3B Instruct reads 128K tokens per request against 32K for Qwen 2 Instruct (1.5B), 4.0x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3.2 3B Instruct for

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

## Choose Qwen 2 Instruct (1.5B) for

- The lower list price ($0.02 in / $0.02 out per M tokens)

## Common questions

### Which is cheaper, Meta Llama 3.2 3B Instruct or Qwen 2 Instruct (1.5B)?

Qwen 2 Instruct (1.5B), on this workload shape. At list prices it is $0.02/$0.02 per million tokens in and out against $0.06/$0.06 for Meta Llama 3.2 3B Instruct. Billed on Allocate: $0.021/$0.021 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 Qwen 2 Instruct (1.5B).

### Can I fine-tune Meta Llama 3.2 3B Instruct or Qwen 2 Instruct (1.5B)?

Both publish open weights (Meta Llama 3.2 3B Instruct: Llama community; Qwen 2 Instruct (1.5B): Qwen license), 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-3-2-3b-instruct-vs-qwen-qwen2-1-5b-instruct) · [Meta Llama 3.2 3B Instruct](https://allocate.network/models/meta-llama-3-2-3b-instruct.md) · [Qwen 2 Instruct (1.5B)](https://allocate.network/models/qwen-qwen2-1-5b-instruct.md) · [Machine-readable catalog](https://allocate.network/catalog.json)