Context Window

Compare

Deepseek R1 0528 Tput vs Gemini 2.5 Pro

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 R1 0528 Tput

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Google

Model

Gemini 2.5 Pro

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K 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 R1 0528 Tput128K
Gemini 2.5 Pro1M

Gemini 2.5 Pro has about 7.8× the context window of the other in this pair.

Token calculator →Memory savings →

Gemini 2.5 Pro has 681% more context capacity (1000K vs 128K tokens). Deepseek R1 0528 Tput is 55% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemini 2.5 Pro. Its 1000K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Deepseek R1 0528 Tput. Input tokens are 55% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecDeepseek R1 0528 TputGemini 2.5 Pro
Context window128,000 tokens (128K)1,000,000 tokens (1000K)
Max output tokensN/A1,000,000 tokens (1000K)
Speed tierDeepFast
VisionNoYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AJun 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 R1 0528 Tput inDeepseek R1 0528 Tput outGemini 2.5 Pro inGemini 2.5 Pro out
Deepinfra——$1.25/M$10.00/M
Google——$1.25/M$10.00/M
Google Vertex——$1.25/M$10.00/M
Openrouter——$1.25/M$10.00/M
Together Ai$0.550/M$2.19/M——

Frequently asked questions

Gemini 2.5 Pro has a larger context window: 1000K tokens vs 128K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

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.

Calculate your savingsLearn about Mem0

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