# Gemma-2 Instruct (27B) vs Qwen 2.5 14B Instruct

On provider list prices, Gemma-2 Instruct (27B) costs $0.80 per million input tokens against $0.80 for Qwen 2.5 14B Instruct: effectively level. Output is $0.80 against $0.80. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Gemma-2 Instruct (27B) | Qwen 2.5 14B Instruct |
| --- | --- | --- |
| Lab | Google | Qwen |
| Access | Open weights | Open weights |
| Context window | 8K tokens | 32K tokens |
| List price, input | $0.80 / M tokens | $0.80 / M tokens |
| List price, output | $0.80 / M tokens | $0.80 / M tokens |
| Cached input | n/a | n/a |
| License | Gemma terms | 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 $1,240 a month on Gemma-2 Instruct (27B) and $1,240 on Qwen 2.5 14B Instruct at list: a gap of $0.

Qwen 2.5 14B Instruct reads 32K tokens per request against 8K for Gemma-2 Instruct (27B), 4.0x the window. That decides which one can take whole documents without splitting them.

## Choose Gemma-2 Instruct (27B) for

- Training toward a model you own

## Choose Qwen 2.5 14B Instruct for

- The longer context window (32K vs 8K tokens)
- Fine-tuning under a permissive license (Apache 2.0)

## Common questions

### Which is cheaper, Gemma-2 Instruct (27B) or Qwen 2.5 14B Instruct?

Gemma-2 Instruct (27B), on this workload shape. At list prices it is $0.80/$0.80 per million tokens in and out against $0.80/$0.80 for Qwen 2.5 14B Instruct. Billed on Allocate: $0.86/$0.86 against $0.86/$0.86, list plus 7%.

### Which has the bigger context window?

Qwen 2.5 14B Instruct: 32,768 tokens (32K) against 8,192 (8K) for Gemma-2 Instruct (27B).

### Can I fine-tune Gemma-2 Instruct (27B) or Qwen 2.5 14B Instruct?

Both publish open weights (Gemma-2 Instruct (27B): Gemma terms; Qwen 2.5 14B 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/google-gemma-2-27b-it-vs-qwen-qwen2-5-14b-instruct) · [Gemma-2 Instruct (27B)](https://allocate.network/models/google-gemma-2-27b-it.md) · [Qwen 2.5 14B Instruct](https://allocate.network/models/qwen-qwen2-5-14b-instruct.md) · [Machine-readable catalog](https://allocate.network/catalog.json)