Context Window

Compare

Deepseek V3p1 Terminus 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.

Deepseek

Model

Deepseek V3p1 Terminus

Context window

128K

128,000 tokens · ~96K 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 V3p1 Terminus128K
Glm 5p1203K

Glm 5p1 has about 1.6× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 5p1 has 58% more context capacity (202K vs 128K tokens). Deepseek V3p1 Terminus is 60% 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 128K).

  • RAG / high-volume retrieval

    Use Deepseek V3p1 Terminus. Input tokens are 60% 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.

SpecDeepseek V3p1 TerminusGlm 5p1
Context window128,000 tokens (128K)202,800 tokens (202K)
Max output tokens8,192 tokens (8K)202,800 tokens (202K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoNo
Extended thinkingYesYes
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 V3p1 Terminus inDeepseek V3p1 Terminus outGlm 5p1 inGlm 5p1 out
Fireworks$0.560/M$1.68/M$1.40/M$4.40/M

Frequently asked questions

Glm 5p1 has a larger context window: 202K 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.

Powered by Mem0

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.

Calculate your savingsLearn about Mem0

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