Context Window

Compare

Glm 5p1 vs Qwen3 235b A22b Fp8 Tput

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 5p1

Context window

203K

202,800 tokens · ~152K words

Model page
Alibaba

Model

Qwen3 235b A22b Fp8 Tput

Context window

40K

40,000 tokens · ~30K 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 5p1203K
Qwen3 235b A22b Fp8 Tput40K

Glm 5p1 has about 5.1× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 5p1 has 407% more context capacity (202K vs 40K tokens). Qwen3 235b A22b Fp8 Tput is 85% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 5p1. Its 202K context fits entire documents without chunking (vs 40K).

  • RAG / high-volume retrieval

    Use Qwen3 235b A22b Fp8 Tput. Input tokens are 85% 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 5p1Qwen3 235b A22b Fp8 Tput
Context window202,800 tokens (202K)40,000 tokens (40K)
Max output tokens202,800 tokens (202K)N/A
Speed tierBalancedBalanced
VisionNoNo
Function callingNoNo
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 5p1 inGlm 5p1 outQwen3 235b A22b Fp8 Tput inQwen3 235b A22b Fp8 Tput out
Fireworks$1.40/M$4.40/M——
Together Ai——$0.200/M$0.600/M

Frequently asked questions

Glm 5p1 has a larger context window: 202K tokens vs 40K. 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