# Meta Llama 3.3 70B Instruct Turbo vs Qwen3-VL-32B-Instruct

On provider list prices, Qwen3-VL-32B-Instruct costs $0.50 per million input tokens against $1.04 for Meta Llama 3.3 70B Instruct Turbo: 2.1x apart. Output is $1.50 against $1.04. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.3 70B Instruct Turbo | Qwen3-VL-32B-Instruct |
| --- | --- | --- |
| Lab | Meta | Qwen |
| Access | Open weights | Open weights |
| Context window | 128K tokens | 256K tokens |
| List price, input | $1.04 / M tokens | $0.50 / M tokens |
| List price, output | $1.04 / M tokens | $1.50 / M tokens |
| Cached input | n/a | n/a |
| License | Llama community | Apache 2.0 |
| 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 $1,125 a month on Qwen3-VL-32B-Instruct and $1,612 on Meta Llama 3.3 70B Instruct Turbo at list: a gap of $487, or 1.4x.

Qwen3-VL-32B-Instruct reads 256K tokens per request against 128K for Meta Llama 3.3 70B Instruct Turbo, 2.0x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3.3 70B Instruct Turbo for

- Training toward a model you own

## Choose Qwen3-VL-32B-Instruct for

- The lower list price ($0.50 in / $1.50 out per M tokens)
- The longer context window (256K vs 128K tokens)
- Fine-tuning under a permissive license (Apache 2.0)

## Common questions

### Which is cheaper, Meta Llama 3.3 70B Instruct Turbo or Qwen3-VL-32B-Instruct?

Qwen3-VL-32B-Instruct, on this workload shape. At list prices it is $0.50/$1.50 per million tokens in and out against $1.04/$1.04 for Meta Llama 3.3 70B Instruct Turbo. Billed on Allocate: $0.54/$1.60 against $1.11/$1.11, list plus 7%.

### Which has the bigger context window?

Qwen3-VL-32B-Instruct: 262,144 tokens (256K) against 131,072 (128K) for Meta Llama 3.3 70B Instruct Turbo.

### Can I fine-tune Meta Llama 3.3 70B Instruct Turbo or Qwen3-VL-32B-Instruct?

Both publish open weights (Meta Llama 3.3 70B Instruct Turbo: Llama community; Qwen3-VL-32B-Instruct: Apache 2.0), 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-3-3-70b-instruct-turbo-vs-qwen-qwen3-vl-32b-instruct) · [Meta Llama 3.3 70B Instruct Turbo](https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md) · [Qwen3-VL-32B-Instruct](https://allocate.network/models/qwen-qwen3-vl-32b-instruct.md) · [Machine-readable catalog](https://allocate.network/catalog.json)