Context Window

Compare

Glm 5 Code vs Mistral Large 2411

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 Code

Tool calling

Context window

200K

200,000 tokens · ~150K words

Model page
Mistral

Model

Mistral Large 2411

Tool calling

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 5 Code200K
Mistral Large 2411128K

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

Token calculator →Memory savings →

Glm 5 Code has 56% more context capacity (200K vs 128K tokens). Glm 5 Code is 40% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 5 Code. Its 200K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Glm 5 Code. 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.

SpecGlm 5 CodeMistral Large 2411
Context window200,000 tokens (200K)128,000 tokens (128K)
Max output tokens128,000 tokens (128K)128,000 tokens (128K)
Speed tierBalancedDeep
VisionNoNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesYes
Batch APINoNo
Release dateN/ANov 2024

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 Code inGlm 5 Code outMistral Large 2411 inMistral Large 2411 out
Google Vertex——$2.00/M$6.00/M
Mistral——$2.00/M$6.00/M
Z Ai$1.20/M$5.00/M——

Frequently asked questions

Glm 5 Code has a larger context window: 200K 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