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GLM 5.1 vs Labs Leanstral 1 5

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 5.1

Tool calling

Context window

203K

202,752 tokens · ~152K words

Model page
Mistral

Model

Labs Leanstral 1 5

Tool calling

Context window

262K

262,144 tokens · ~197K 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 5.1203K
Labs Leanstral 1 5262K

Labs Leanstral 1 5 has about 1.3× the context window of the other in this pair.

Labs Leanstral 1 5 has 29% more context capacity (262K vs 202K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Labs Leanstral 1 5. Its 262K context fits entire documents without chunking (vs 202K).

Full specs

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

SpecGLM 5.1Labs Leanstral 1 5
Context window202,752 tokens (202K)262,144 tokens (262K)
Max output tokens131,072 tokens (131K)131,072 tokens (131K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateApr 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 5.1 inGLM 5.1 outLabs Leanstral 1 5 inLabs Leanstral 1 5 out
Mistral
Openrouter$1.05/M$3.50/M
Z Ai$1.40/M$4.40/M

Frequently asked questions

Labs Leanstral 1 5 has a larger context window: 262K 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.

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