Context Window

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Deepseek R1 Distill Llama 8b vs Glm 5p1

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.

Meta

Model

Deepseek R1 Distill Llama 8b

Context window

131K

131,072 tokens · ~98K words

Model page
Z Ai

Model

Glm 5p1

Context window

203K

202,800 tokens · ~152K 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.

Deepseek R1 Distill Llama 8b131K
Glm 5p1203K

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). Deepseek R1 Distill Llama 8b is 98% 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 Deepseek R1 Distill Llama 8b. Input tokens are 98% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecDeepseek R1 Distill Llama 8bGlm 5p1
Context window131,072 tokens (131K)202,800 tokens (202K)
Max output tokensN/A202,800 tokens (202K)
Speed tierFastBalanced
VisionNoNo
Function callingNoNo
Extended thinkingNoYes
Prompt cachingNoYes
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.

ProviderDeepseek R1 Distill Llama 8b inDeepseek R1 Distill Llama 8b outGlm 5p1 inGlm 5p1 out
Fireworks$0.200/M$0.200/M$1.40/M$4.40/M
Nscale$0.025/M$0.025/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
Key memories
Current turn

80% less to send — works with any model