Context Window

Compare

Grok4 6 vs Qwen2.5 Coder 32B Instruct

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.

Xai

Model

Grok4 6

Context window

2M

2,000,000 tokens · ~1.5M words

Model page
Alibaba

Model

Qwen2.5 Coder 32B Instruct

Context window

34K

33,792 tokens · ~25K 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.

Grok4 62M
Qwen2.5 Coder 32B Instruct34K

Grok4 6 has about 59.2× the context window of the other in this pair.

Token calculator →Memory savings →

Grok4 6 has 5818% more context capacity (2000K vs 33K tokens). Qwen2.5 Coder 32B Instruct is 67% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Grok4 6. Its 2000K context fits entire documents without chunking (vs 33K).

  • RAG / high-volume retrieval

    Use Qwen2.5 Coder 32B Instruct. Input tokens are 67% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGrok4 6Qwen2.5 Coder 32B Instruct
Context window2,000,000 tokens (2000K)33,792 tokens (33K)
Max output tokensN/A33,792 tokens (33K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingYesNo
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.

ProviderGrok4 6 inGrok4 6 outQwen2.5 Coder 32B Instruct inQwen2.5 Coder 32B Instruct out
Gmi$2.00/M$6.00/M——
Openrouter——$0.660/M$1.00/M

Frequently asked questions

Grok4 6 has a larger context window: 2000K tokens vs 33K. 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