Context Window

Compare

GLM 5.2 (batch) vs Llama 4 Maverick 17b 128e Instruct Fp8

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 5.2 (batch)

Tool calling

Context window

512K

512,000 tokens · ~384K words

Model page
Meta

Model

Llama 4 Maverick 17b 128e Instruct Fp8

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K 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 5.2 (batch)512K
Llama 4 Maverick 17b 128e Instruct Fp81M

Llama 4 Maverick 17b 128e Instruct Fp8 has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 4 Maverick 17b 128e Instruct Fp8 has 95% more context capacity (1000K vs 512K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama 4 Maverick 17b 128e Instruct Fp8. Its 1000K context fits entire documents without chunking (vs 512K).

Full specs

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

SpecGLM 5.2 (batch)Llama 4 Maverick 17b 128e Instruct Fp8
Context window512,000 tokens (512K)1,000,000 tokens (1000K)
Max output tokensN/A16,384 tokens (16K)
Speed tierBalancedFast
VisionNoYes
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateJun 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.

ProviderGLM 5.2 (batch) inGLM 5.2 (batch) outLlama 4 Maverick 17b 128e Instruct Fp8 inLlama 4 Maverick 17b 128e Instruct Fp8 out
Azure——$1.41/M$0.350/M
Deepinfra——$0.150/M$0.600/M
Lambda——$0.050/M$0.100/M
Meta————
Novita——$0.270/M$0.850/M
Together Ai——$0.270/M$0.850/M

Frequently asked questions

Llama 4 Maverick 17b 128e Instruct Fp8 has a larger context window: 1000K tokens vs 512K. 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