Context Window

Compare

GLM 4.6 vs Gpt 35 Turbo Instruct 0914

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 4.6

Tool calling

Context window

203K

202,800 tokens · ~152K words

Model page
Openai

Model

Gpt 35 Turbo Instruct 0914

Context window

4K

4,097 tokens · ~3K 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 4.6203K
Gpt 35 Turbo Instruct 09144K

GLM 4.6 has about 49.5× the context window of the other in this pair.

Token calculator →Memory savings →

GLM 4.6 has 4849% more context capacity (202K vs 4K tokens). GLM 4.6 is 60% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GLM 4.6. Its 202K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use GLM 4.6. Input tokens are 60% 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 4.6Gpt 35 Turbo Instruct 0914
Context window202,800 tokens (202K)4,097 tokens (4K)
Max output tokens131,000 tokens (131K)N/A
Speed tierBalancedBalanced
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateSep 2025N/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 4.6 inGLM 4.6 outGpt 35 Turbo Instruct 0914 inGpt 35 Turbo Instruct 0914 out
Azure——$1.50/M$2.00/M
Baseten$0.600/M$2.20/M——
Novita$0.550/M$2.20/M——
Openrouter$0.400/M$1.75/M——
Together Ai$0.600/M$2.20/M——
Z Ai$0.600/M$2.20/M——

Frequently asked questions

GLM 4.6 has a larger context window: 202K tokens vs 4K. 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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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