Context Window

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GLM Latest vs Nvidia Nemotron 3 5 Lightning 30b A3b

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 Latest

Tool calling

Context window

1.3M

1,310,720 tokens · ~983K words

Model page
Nvidia

Model

Nvidia Nemotron 3 5 Lightning 30b A3b

Context window

66K

65,536 tokens · ~49K 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 Latest1.3M
Nvidia Nemotron 3 5 Lightning 30b A3b66K

GLM Latest has about 20× the context window of the other in this pair.

Token calculator →Memory savings →

GLM Latest has 1900% more context capacity (1310K vs 65K tokens). Nvidia Nemotron 3 5 Lightning 30b A3b is 77% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GLM Latest. Its 1310K context fits entire documents without chunking (vs 65K).

  • RAG / high-volume retrieval

    Use Nvidia Nemotron 3 5 Lightning 30b A3b. Input tokens are 77% 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 LatestNvidia Nemotron 3 5 Lightning 30b A3b
Context window1,310,720 tokens (1310K)65,536 tokens (65K)
Max output tokens235,929 tokens (235K)N/A
Speed tierBalancedFast
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesYes
Batch APINoNo
Release dateAug 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 Latest inGLM Latest outNvidia Nemotron 3 5 Lightning 30b A3b inNvidia Nemotron 3 5 Lightning 30b A3b out
Gmi——$0.150/M$1.00/M
Openrouter$0.654/M$2.05/M——

Frequently asked questions

GLM Latest has a larger context window: 1310K tokens vs 65K. 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