# Cogito v2.1 671B vs Qwen2-VL (72B) Instruct

On provider list prices, Qwen2-VL (72B) Instruct costs $1.20 per million input tokens against $1.25 for Cogito v2.1 671B: effectively level. Output is $1.20 against $1.25. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Cogito v2.1 671B | Qwen2-VL (72B) Instruct |
| --- | --- | --- |
| Lab | Deepcogito | Qwen |
| Access | Open weights | Open weights |
| Context window | 160K tokens | 32K tokens |
| List price, input | $1.25 / M tokens | $1.20 / M tokens |
| List price, output | $1.25 / M tokens | $1.20 / M tokens |
| Cached input | n/a | n/a |
| License | Not listed | 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 $1,860 a month on Qwen2-VL (72B) Instruct and $1,938 on Cogito v2.1 671B at list: a gap of $77.50.

Cogito v2.1 671B reads 160K tokens per request against 32K for Qwen2-VL (72B) Instruct, 5.0x the window. That decides which one can take whole documents without splitting them.

## Choose Cogito v2.1 671B for

- The longer context window (160K vs 32K tokens)

## Choose Qwen2-VL (72B) Instruct for

- The lower list price ($1.20 in / $1.20 out per M tokens)

## Common questions

### Which is cheaper, Cogito v2.1 671B or Qwen2-VL (72B) Instruct?

Qwen2-VL (72B) Instruct, on this workload shape. At list prices it is $1.20/$1.20 per million tokens in and out against $1.25/$1.25 for Cogito v2.1 671B. Billed on Allocate: $1.28/$1.28 against $1.34/$1.34, list plus 7%.

### Which has the bigger context window?

Cogito v2.1 671B: 163,840 tokens (160K) against 32,768 (32K) for Qwen2-VL (72B) Instruct.

### Can I fine-tune Cogito v2.1 671B or Qwen2-VL (72B) Instruct?

Both publish open weights (Cogito v2.1 671B: Not listed; Qwen2-VL (72B) Instruct: 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/deepcogito-cogito-v2-1-671b-vs-qwen-qwen2-vl-72b-instruct) · [Cogito v2.1 671B](https://allocate.network/models/deepcogito-cogito-v2-1-671b.md) · [Qwen2-VL (72B) Instruct](https://allocate.network/models/qwen-qwen2-vl-72b-instruct.md) · [Machine-readable catalog](https://allocate.network/catalog.json)