Context Window

Compare

Glm 4 7 251222 vs Llama 3 3 70b Versatile

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 4 7 251222

Tool calling

Context window

205K

204,800 tokens · ~154K words

Model page
Meta

Model

Llama 3 3 70b Versatile

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.

Glm 4 7 251222205K
Llama 3 3 70b Versatile128K

Glm 4 7 251222 has about 1.6× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 4 7 251222 has 60% more context capacity (204K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 4 7 251222. Its 204K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Glm 4 7 251222. Its 131K max output lets you generate complete artifacts in one request.

Full specs

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

SpecGlm 4 7 251222Llama 3 3 70b Versatile
Context window204,800 tokens (204K)128,000 tokens (128K)
Max output tokens131,072 tokens (131K)32,768 tokens (32K)
Speed tierBalancedDeep
VisionNoNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingNoNo
Batch APINoNo
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.

ProviderGlm 4 7 251222 inGlm 4 7 251222 outLlama 3 3 70b Versatile inLlama 3 3 70b Versatile out
Groq——$0.590/M$0.790/M
Volcengine————

Frequently asked questions

Glm 4 7 251222 has a larger context window: 204K 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.
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.

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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