Context Window

Compare

Apac Amazon Nova Pro vs Glm 5 1 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.

Amazon

Model

Apac Amazon Nova Pro

Image inputTool calling

Context window

300K

300,000 tokens · ~225K words

Model page
Z Ai

Model

Glm 5 1 Fp8

Context window

203K

202,752 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.

Apac Amazon Nova Pro300K
Glm 5 1 Fp8203K

Apac Amazon Nova Pro has about 1.5× the context window of the other in this pair.

Token calculator →Memory savings →

Apac Amazon Nova Pro has 47% more context capacity (300K vs 202K tokens). Apac Amazon Nova Pro is 40% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Apac Amazon Nova Pro. Its 300K context fits entire documents without chunking (vs 202K).

  • RAG / high-volume retrieval

    Use Apac Amazon Nova Pro. Input tokens are 40% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecApac Amazon Nova ProGlm 5 1 Fp8
Context window300,000 tokens (300K)202,752 tokens (202K)
Max output tokens10,000 tokens (10K)N/A
Speed tierBalancedBalanced
VisionYesNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingYesYes
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.

ProviderApac Amazon Nova Pro inApac Amazon Nova Pro outGlm 5 1 Fp8 inGlm 5 1 Fp8 out
Aws Bedrock$0.840/M$3.36/M——
Gmi——$1.40/M$4.40/M

Frequently asked questions

Apac Amazon Nova Pro has a larger context window: 300K tokens vs 202K. 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