Context Window

Compare

Glm 5p2 vs Jamba Mini 1 7

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 5p2

Tool calling

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Ai21

Model

Jamba Mini 1 7

Context window

256K

256,000 tokens · ~192K 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 5p21.0M
Jamba Mini 1 7256K

Glm 5p2 has about 4.1× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 5p2 has 309% more context capacity (1048K vs 256K tokens). Jamba Mini 1 7 is 85% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 5p2. Its 1048K context fits entire documents without chunking (vs 256K).

  • RAG / high-volume retrieval

    Use Jamba Mini 1 7. Input tokens are 85% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Jamba Mini 1 7. Its 256K 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 5p2Jamba Mini 1 7
Context window1,048,576 tokens (1048K)256,000 tokens (256K)
Max output tokens131,072 tokens (131K)256,000 tokens (256K)
Speed tierBalancedFast
VisionNoNo
Function callingYesNo
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 5p2 inGlm 5p2 outJamba Mini 1 7 inJamba Mini 1 7 out
Ai21——$0.200/M$0.400/M
Fireworks$1.40/M$4.40/M——

Frequently asked questions

Glm 5p2 has a larger context window: 1048K tokens vs 256K. 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