Context Window

Compare

GLM 5 vs Open Mistral Nemo 2407

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 5

Tool calling

Context window

203K

202,752 tokens · ~152K words

Model page
Mistral

Model

Open Mistral Nemo 2407

Context window

128K

128,000 tokens · ~96K 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 5203K
Open Mistral Nemo 2407128K

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

Token calculator →Memory savings →

GLM 5 has 58% more context capacity (202K vs 128K tokens). Open Mistral Nemo 2407 is 70% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GLM 5. Its 202K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Open Mistral Nemo 2407. Input tokens are 70% 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 5Open Mistral Nemo 2407
Context window202,752 tokens (202K)128,000 tokens (128K)
Max output tokens128,000 tokens (128K)128,000 tokens (128K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateFeb 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 5 inGLM 5 outOpen Mistral Nemo 2407 inOpen Mistral Nemo 2407 out
Baseten$0.950/M$3.15/M——
Mistral——$0.300/M$0.300/M
Openrouter$0.800/M$2.56/M——
Z Ai$1.00/M$3.20/M——

Frequently asked questions

GLM 5 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.

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Use a smaller model.
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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