Context Window

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Deepseek R1 0528 Tput vs Gemini 2.5 Flash Lite

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 Flash Lite

Image inputTool calling

Context window

1.0M

1,048,576 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 R1 0528 Tput128K
Gemini 2.5 Flash Lite1.0M

Gemini 2.5 Flash Lite has about 8.2× the context window of the other in this pair.

Token calculator →Memory savings →

Gemini 2.5 Flash Lite has 719% more context capacity (1048K vs 128K tokens). Gemini 2.5 Flash Lite is 81% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemini 2.5 Flash Lite. Its 1048K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Gemini 2.5 Flash Lite. Input tokens are 81% 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 Flash Lite
Context window128,000 tokens (128K)1,048,576 tokens (1048K)
Max output tokensN/A65,535 tokens (65K)
Speed tierDeepFast
VisionNoYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AJul 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 Flash Lite inGemini 2.5 Flash Lite out
Google——$0.100/M$0.400/M
Google Vertex——$0.100/M$0.400/M
Together Ai$0.550/M$2.19/M——

Frequently asked questions

Gemini 2.5 Flash Lite has a larger context window: 1048K 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.

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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