Context Window

Compare

Qwen2p5 32b 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.

Alibaba

Model

Qwen2p5 32b

Context window

131K

131,072 tokens · ~98K 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.

Qwen2p5 32b131K
Sonar Reasoning Pro128K

Qwen2p5 32b has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen2p5 32b has 2% more context capacity (131K vs 128K tokens). Qwen2p5 32b is 55% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen2p5 32b. Its 131K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Qwen2p5 32b. 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.

SpecQwen2p5 32bSonar Reasoning Pro
Context window131,072 tokens (131K)128,000 tokens (128K)
Max output tokens131,072 tokens (131K)N/A
Speed tierBalancedDeep
VisionNoYes
Function callingNoNo
Extended thinkingNoYes
Prompt cachingNoNo
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.

ProviderQwen2p5 32b inQwen2p5 32b outSonar Reasoning Pro inSonar Reasoning Pro out
Fireworks$0.900/M$0.900/M——
Perplexity——$2.00/M$8.00/M

Frequently asked questions

Qwen2p5 32b has a larger context window: 131K 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