Context Window

Compare

Gpt 5 1 2025 11 13 vs Sonar Reasoning 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.

Openai

Model

Gpt 5 1 2025 11 13

Image inputTool calling

Context window

272K

272,000 tokens · ~204K words

Model page
Perplexity

Model

Sonar Reasoning Pro

Image input

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.

Gpt 5 1 2025 11 13272K
Sonar Reasoning Pro128K

Gpt 5 1 2025 11 13 has about 2.1× the context window of the other in this pair.

Token calculator →Memory savings →

Gpt 5 1 2025 11 13 has 112% more context capacity (272K vs 128K tokens). Gpt 5 1 2025 11 13 is 37% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gpt 5 1 2025 11 13. Its 272K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Gpt 5 1 2025 11 13. Input tokens are 37% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGpt 5 1 2025 11 13Sonar Reasoning Pro
Context window272,000 tokens (272K)128,000 tokens (128K)
Max output tokens128,000 tokens (128K)N/A
Speed tierBalancedDeep
VisionYesYes
Function callingYesNo
Extended thinkingYesYes
Prompt cachingYesNo
Batch APINoNo
Release dateN/AMar 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.

ProviderGpt 5 1 2025 11 13 inGpt 5 1 2025 11 13 outSonar Reasoning Pro inSonar Reasoning Pro out
Azure$1.25/M$10.00/M——
Openai$1.25/M$10.00/M——
Perplexity——$2.00/M$8.00/M

Frequently asked questions

Gpt 5 1 2025 11 13 has a larger context window: 272K 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.

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Use a smaller model.
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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