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Model comparison

DeepSeek V3.2 vs ERNIE 4.5 Turbo

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

Provider
DeepSeek / Baidu Wenxin
china / china
Context
163.8K / 128K
text->text / text->text
Input price
$0.26 / $0.12
per 1M tokens
Output price
$0.38 / $0.40
per 1M tokens
Left model
DeepSeek V3.2
DeepSeek
FamilyDeepSeek
Modalitytext->text

中国站主力通用模型,适合成本敏感的企业与渠道场景。

Right model
ERNIE 4.5 Turbo
Baidu Wenxin
FamilyERNIE
Modalitytext->text

适合高频问答和中国企业助手场景。

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, ERNIE 4.5 Turbo is cheaper on combined input and output, but real routing, discounts, and limits still matter.

DeepSeek V3.2 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.

  • DeepSeek V3.2 is worth checking first when the DeepSeek family, 163.8K context, and text->text capability match the job.
  • ERNIE 4.5 Turbo is worth checking first when the ERNIE family, 128K 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.

  • DeepSeek: DeepSeek V3.2, DeepSeek, text->text
  • Baidu Wenxin: ERNIE 4.5 Turbo, ERNIE, text->text

Commercial fit

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

  • DeepSeek V3.2: 中国站主力通用模型,适合成本敏感的企业与渠道场景。
  • ERNIE 4.5 Turbo: 适合高频问答和中国企业助手场景。