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Model comparison

Gemma 3 27B vs DeepSeek R1 0528

Not a benchmark table. This puts pricing, context, interface fit, and key visibility into one decision card.

Provider
Google / DeepSeek
global / china
Context
131.1K / 163.8K
text+image->text / text->text
Input price
$0.20 / $0.45
per 1M tokens
Output price
$0.60 / $2.15
per 1M tokens
Left model
Gemma 3 27B
Google
FamilyGemma
Modalitytext+image->text

开源路线里高质量的 Google 家族代表。

Right model
DeepSeek R1 0528
DeepSeek
FamilyR1
Modalitytext->text

推理能力强,是中国站高热度推理模型入口之一。

Comparison summary

How to choose first

This is a cross-provider comparison. Start with the job boundary, then verify what your key can actually see.

On the listed price snapshot, Gemma 3 27B is cheaper on combined input and output, but real routing, discounts, and limits still matter.

DeepSeek R1 0528 has the larger context window, which helps with long documents, knowledge bases, logs, and multi-turn workflows.

Decision boundary

Do not start with which model is absolutely stronger. Start with the boundary: cost, context, speed, quality, ecosystem, or supply stability.

  • Gemma 3 27B is worth checking first when the Gemma family, 131.1K context, and text+image->text capability match the job.
  • DeepSeek R1 0528 is worth checking first when the R1 family, 163.8K context, and text->text capability match the job.

Key checking route

If you already hold a key, the valuable check is provider identity, callable models, and whether balance, limits, or subscription status are visible.

  • Google: Gemma 3 27B, Gemma, text+image->text
  • DeepSeek: DeepSeek R1 0528, R1, text->text

Commercial fit

Commercially, do not look at model names alone. Combine price, limits, region, upstream stability, and ongoing monitoring.

  • Gemma 3 27B: 开源路线里高质量的 Google 家族代表。
  • DeepSeek R1 0528: 推理能力强,是中国站高热度推理模型入口之一。