Context Window

Compare

GPT-6.1 Sol vs Zai Glm 4 7

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-6.1 Sol

Image inputTool calling

Context window

922K

922,000 tokens · ~692K words

Model page
Z Ai

Model

Zai Glm 4 7

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.

GPT-6.1 Sol922K
Zai Glm 4 7128K

GPT-6.1 Sol has about 7.2× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-6.1 Sol has 620% more context capacity (922K vs 128K tokens). Zai Glm 4 7 is 70% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-6.1 Sol. Its 922K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Zai Glm 4 7. 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.

SpecGPT-6.1 SolZai Glm 4 7
Context window922,000 tokens (922K)128,000 tokens (128K)
Max output tokens128,000 tokens (128K)128,000 tokens (128K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesNo
Batch APINoNo
Release dateSep 2026N/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.

ProviderGPT-6.1 Sol inGPT-6.1 Sol outZai Glm 4 7 inZai Glm 4 7 out
Aws Bedrock——$0.600/M$2.20/M
Azure$2.00/M$10.00/M——
Cerebras——$2.25/M$2.75/M
Openai$2.00/M$10.00/M——
Openrouter$2.00/M$10.00/M——

Frequently asked questions

GPT-6.1 Sol has a larger context window: 922K 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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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