Context Window

Compare

Glm 5p1 vs Llama3 1 70b 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 5p1

Context window

203K

202,800 tokens · ~152K words

Model page
Meta

Model

Llama3 1 70b Instruct Fp8

Tool calling

Context window

131K

131,072 tokens · ~98K 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 5p1203K
Llama3 1 70b Instruct Fp8131K

Glm 5p1 has about 1.5× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 5p1 has 54% more context capacity (202K vs 131K tokens). Llama3 1 70b Instruct Fp8 is 91% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 5p1. Its 202K context fits entire documents without chunking (vs 131K).

  • RAG / high-volume retrieval

    Use Llama3 1 70b Instruct Fp8. Input tokens are 91% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Glm 5p1. Its 202K 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 5p1Llama3 1 70b Instruct Fp8
Context window202,800 tokens (202K)131,072 tokens (131K)
Max output tokens202,800 tokens (202K)131,072 tokens (131K)
Speed tierBalancedDeep
VisionNoNo
Function callingNoYes
Extended thinkingYesNo
Prompt cachingYesNo
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 5p1 inGlm 5p1 outLlama3 1 70b Instruct Fp8 inLlama3 1 70b Instruct Fp8 out
Fireworks$1.40/M$4.40/M——
Lambda——$0.120/M$0.300/M

Frequently asked questions

Glm 5p1 has a larger context window: 202K tokens vs 131K. 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
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Current turn

80% less to send — works with any model