Context Window

Compare

Anthropic Claude vs Deepseek R1 0528 Tput

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.

Anthropic

Model

Anthropic Claude

Context window

100K

100,000 tokens · ~75K words

Model page
Deepseek

Model

Deepseek R1 0528 Tput

Tool calling

Context window

128K

128,000 tokens · ~96K 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.

Anthropic Claude100K
Deepseek R1 0528 Tput128K

Deepseek R1 0528 Tput has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Deepseek R1 0528 Tput has 28% more context capacity (128K vs 100K tokens). Deepseek R1 0528 Tput is 93% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Deepseek R1 0528 Tput. Its 128K context fits entire documents without chunking (vs 100K).

  • RAG / high-volume retrieval

    Use Deepseek R1 0528 Tput. Input tokens are 93% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecAnthropic ClaudeDeepseek R1 0528 Tput
Context window100,000 tokens (100K)128,000 tokens (128K)
Max output tokens8,191 tokens (8K)N/A
Speed tierBalancedDeep
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
Release dateN/AN/A

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.

ProviderAnthropic Claude inAnthropic Claude outDeepseek R1 0528 Tput inDeepseek R1 0528 Tput out
Aws Bedrock$8.00/M$24.00/M——
Together Ai——$0.550/M$2.19/M

Frequently asked questions

Deepseek R1 0528 Tput has a larger context window: 128K tokens vs 100K. 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