Context Window

Compare

Fw Glm 5 vs Mistral Medium 3.5

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

Fw Glm 5

Tool calling

Context window

200K

200,000 tokens · ~150K words

Model page
Mistral

Model

Mistral Medium 3.5

Image inputTool 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.

Fw Glm 5200K
Mistral Medium 3.5262K

Mistral Medium 3.5 has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Mistral Medium 3.5 has 31% more context capacity (262K vs 200K tokens). Fw Glm 5 is 26% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Mistral Medium 3.5. Its 262K context fits entire documents without chunking (vs 200K).

  • RAG / high-volume retrieval

    Use Fw Glm 5. Input tokens are 26% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecFw Glm 5Mistral Medium 3.5
Context window200,000 tokens (200K)262,144 tokens (262K)
Max output tokens128,000 tokens (128K)N/A
Speed tierBalancedBalanced
VisionNoYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesNo
Batch APINoNo
Release dateN/AApr 2026

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.

ProviderFw Glm 5 inFw Glm 5 outMistral Medium 3.5 inMistral Medium 3.5 out
Azure$1.10/M$3.52/M——
Mistral——$1.50/M$7.50/M

Frequently asked questions

Mistral Medium 3.5 has a larger context window: 262K tokens vs 200K. 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