Context Window

Compare

Glm 4p7 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.

Z Ai

Model

Glm 4p7

Tool calling

Context window

203K

202,800 tokens · ~152K 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.

Glm 4p7203K
Sonar Reasoning Pro128K

Glm 4p7 has about 1.6× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 4p7 has 58% more context capacity (202K vs 128K tokens). Glm 4p7 is 70% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 4p7. Its 202K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Glm 4p7. Input tokens are 70% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGlm 4p7Sonar Reasoning Pro
Context window202,800 tokens (202K)128,000 tokens (128K)
Max output tokens202,800 tokens (202K)N/A
Speed tierBalancedDeep
VisionNoYes
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.

ProviderGlm 4p7 inGlm 4p7 outSonar Reasoning Pro inSonar Reasoning Pro out
Fireworks$0.600/M$2.20/M——
Perplexity——$2.00/M$8.00/M

Frequently asked questions

Glm 4p7 has a larger context window: 202K 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