# Qwen2 72B Instruct vs Qwen3-VL-32B-Instruct

On provider list prices, Qwen2 72B Instruct costs $0.90 per million input tokens against $0.50 for Qwen3-VL-32B-Instruct: effectively level. Output is $0.90 against $1.50 (1.7x). On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Qwen2 72B Instruct | Qwen3-VL-32B-Instruct |
| --- | --- | --- |
| Lab | Togethercomputer | Qwen |
| Access | Open weights | Open weights |
| Context window | 32K tokens | 256K tokens |
| List price, input | $0.90 / M tokens | $0.50 / M tokens |
| List price, output | $0.90 / M tokens | $1.50 / M tokens |
| Cached input | n/a | n/a |
| License | Qwen license | 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 $1,395 on Qwen2 72B Instruct at list: a gap of $270, or 1.2x.

Qwen3-VL-32B-Instruct reads 256K tokens per request against 32K for Qwen2 72B Instruct, 8.0x the window. That decides which one can take whole documents without splitting them.

## Choose Qwen2 72B Instruct 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 32K tokens)
- Fine-tuning under a permissive license (Apache 2.0)

## Common questions

### Which is cheaper, Qwen2 72B Instruct 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 $0.90/$0.90 for Qwen2 72B Instruct. Billed on Allocate: $0.54/$1.60 against $0.96/$0.96, list plus 7%.

### Which has the bigger context window?

Qwen3-VL-32B-Instruct: 262,144 tokens (256K) against 32,768 (32K) for Qwen2 72B Instruct.

### Can I fine-tune Qwen2 72B Instruct or Qwen3-VL-32B-Instruct?

Both publish open weights (Qwen2 72B Instruct: Qwen license; 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/qwen-qwen2-72b-instruct-vs-qwen-qwen3-vl-32b-instruct) · [Qwen2 72B Instruct](https://allocate.network/models/qwen-qwen2-72b-instruct.md) · [Qwen3-VL-32B-Instruct](https://allocate.network/models/qwen-qwen3-vl-32b-instruct.md) · [Machine-readable catalog](https://allocate.network/catalog.json)