Context Window

Compare

Deepseek V3 1 Maas vs GPT-4.1 Mini (batch)

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Deepseek

Model

Deepseek V3 1 Maas

Tool calling

Context window

164K

163,840 tokens · ~123K words

Model page
Openai

Model

GPT-4.1 Mini (batch)

Image inputTool calling

Context window

1.0M

1,047,576 tokens · ~786K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Deepseek V3 1 Maas164K
GPT-4.1 Mini (batch)1.0M

GPT-4.1 Mini (batch) has about 6.4× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-4.1 Mini (batch) has 539% more context capacity (1047K vs 163K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-4.1 Mini (batch). Its 1047K context fits entire documents without chunking (vs 163K).

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecDeepseek V3 1 MaasGPT-4.1 Mini (batch)
Context window163,840 tokens (163K)1,047,576 tokens (1047K)
Max output tokens32,768 tokens (32K)32,768 tokens (32K)
Speed tierBalancedFast
VisionNoYes
Function callingYesYes
Extended thinkingYesNo
Prompt cachingNoYes
Batch APINoNo
Release dateN/AApr 2025

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderDeepseek V3 1 Maas inDeepseek V3 1 Maas outGPT-4.1 Mini (batch) inGPT-4.1 Mini (batch) out
Google Vertex$1.35/M$5.40/M——

Frequently asked questions

GPT-4.1 Mini (batch) has a larger context window: 1047K tokens vs 163K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

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Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model