Context Window

Compare

GLM 4.6V 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 4.6V

Image inputTool calling

Context window

131K

131,072 tokens · ~98K 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 4.6V131K
Llama 3 3 70b Versatile128K

GLM 4.6V has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

GLM 4.6V has 2% more context capacity (131K vs 128K tokens). GLM 4.6V is 49% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GLM 4.6V. Its 131K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use GLM 4.6V. Input tokens are 49% 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 4.6VLlama 3 3 70b Versatile
Context window131,072 tokens (131K)128,000 tokens (128K)
Max output tokens32,768 tokens (32K)32,768 tokens (32K)
Speed tierBalancedDeep
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateDec 2025N/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 4.6V inGLM 4.6V outLlama 3 3 70b Versatile inLlama 3 3 70b Versatile out
Groq——$0.590/M$0.790/M
Novita$0.300/M$0.900/M——

Frequently asked questions

GLM 4.6V 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.

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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